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#00000033;--space-xxl:64px;--space-xl:48px;--space-lg:32px;--space-ml:24px;--space-md:16px;--space-ms:12px;--space-sm:8px;--space-ss:6px;--space-xs:4px;--space-xxs:2px;--space-none:0px}.t-laoRNc{--colors-brand:#76b900;--colors-n000:#ffffff;--colors-n025:#fafafa;--colors-n050:#f2f2f2;--colors-n100:#e2e2e2;--colors-n200:#d8d8d8;--colors-n300:#c6c6c6;--colors-n400:#8f8f8f;--colors-n500:#767676;--colors-n600:#5e5e5e;--colors-n700:#343434;--colors-n800:#292929;--colors-n900:#202020;--colors-n950:#121212;--colors-n1000:#000000;--colors-g050:#ecfad9;--colors-g100:#d6f2ad;--colors-g200:#bfe882;--colors-g300:#a7db57;--colors-g400:#8fcb2b;--colors-g500:#76b900;--colors-g600:#57920d;--colors-g700:#407015;--colors-g800:#2f521a;--colors-g900:#23371a;--colors-g950:#192116;--colors-b050:#e9f4fb;--colors-b100:#cde6fa;--colors-b200:#badef8;--colors-b300:#9accf2;--colors-b400:#3a96d9;--colors-b500:#2378ca;--colors-b600:#2a63ba;--colors-b700:#013076;--colors-b800:#00265e;--colors-b900:#001e4b;--colors-b950:#00112c;--colors-y050:#f9f4ca;--colors-y100:#f6e383;--colors-y200:#f3d85d;--colors-y300:#e7c32b;--colors-y400:#c58420;--colors-y500:#a96700;--colors-y600:#924c00;--colors-y700:#5a2400;--colors-y800:#491c00;--colors-y900:#3a1500;--colors-y950:#220c00;--colors-r050:#ffefed;--colors-r100:#ffdad6;--colors-r200:#ffceca;--colors-r300:#feb3ad;--colors-r400:#ec6358;--colors-r500:#dc3528;--colors-r600:#b6251b;--colors-r700:#6c0d08;--colors-r800:#570704;--colors-r900:#480200;--colors-r950:#2d0100;--colors-t050:#e1f7f3;--colors-t100:#b7eee4;--colors-t200:#a0e6d9;--colors-t300:#7cd7c7;--colors-t400:#1fa18d;--colors-t500:#008471;--colors-t600:#006b5e;--colors-t700:#003b35;--colors-t800:#002f2b;--colors-t900:#002522;--colors-t950:#001514;--colors-p050:#fbeefe;--colors-p100:#f1dcf7;--colors-p200:#ead1f3;--colors-p300:#debae8;--colors-p400:#b579c6;--colors-p500:#a05ab4;--colors-p600:#8c3ca0;--colors-p700:#5a066d;--colors-p800:#490457;--colors-p900:#3b0047;--colors-p950:#25002c;--colors-tk000:rgba(0,0,0,0.00);--colors-tk050:rgba(0,0,0,0.05);--colors-tk100:rgba(0,0,0,0.10);--colors-tk120:rgba(0,0,0,0.12);--colors-tk150:rgba(0,0,0,0.15);--colors-tk200:rgba(0,0,0,0.20);--colors-tk300:rgba(0,0,0,0.30);--colors-tk400:rgba(0,0,0,0.40);--colors-tk500:rgba(0,0,0,0.50);--colors-tk600:rgba(0,0,0,0.60);--colors-tk700:rgba(0,0,0,0.70);--colors-tk800:rgba(0,0,0,0.80);--colors-tk900:rgba(0,0,0,0.90);--colors-tw000:rgba(255,255,255,0.00);--colors-tw050:rgba(255,255,255,0.05);--colors-tw100:rgba(255,255,255,0.10);--colors-tw120:rgba(255,255,255,0.12);--colors-tw250:rgba(255,255,255,0.25);--colors-tw200:rgba(255,255,255,0.20);--colors-tw300:rgba(255,255,255,0.30);--colors-tw400:rgba(255,255,255,0.40);--colors-tw500:rgba(255,255,255,0.50);--colors-tw600:rgba(255,255,255,0.60);--colors-tw700:rgba(255,255,255,0.70);--colors-tw800:rgba(255,255,255,0.80);--colors-tw900:rgba(255,255,255,0.90);--colors-displayBgHighest:#000000;--colors-displayBgHighestHover:#202020;--colors-displayBgHighestActive:#343434;--colors-displayBgHigher:#343434;--colors-displayBgHigherHover:#292929;--colors-displayBgHigherActive:#121212;--colors-displayBgHigh:#292929;--colors-displayBgHighHover:#202020;--colors-displayBgHighActive:#000000;--colors-displayBgMid:#202020;--colors-displayBgMidHover:#343434;--colors-displayBgMidActive:#5e5e5e;--colors-displayBgLow:#121212;--colors-displayBgLowHover:#292929;--colors-displayBgLowActive:#343434;--colors-displayBgCanvas:#202020;--colors-displayBgAccent1Hover:#013076;--colors-displayBgAccent1Active:#9accf2;--colors-displayBgAccent1Solid:#9accf2;--colors-displayBgAccent1SolidHover:#badef8;--colors-displayBgAccent1SolidActive:#2a63ba;--colors-displayBgAccent1Bold:#2a63ba;--colors-displayBgAccent1Disabled:#8f8f8f;--colors-displayBgAccent2Hover:#407015;--colors-displayBgAccent2Active:#a7db57;--colors-displayBgAccent2Solid:#a7db57;--colors-displayBgAccent2SolidHover:#bfe882;--colors-displayBgAccent2SolidActive:#57920d;--colors-displayBgAccent2Bold:#57920d;--colors-displayBgAccent2Disabled:#8f8f8f;--colors-displayBgAccent3Hover:#5a2400;--colors-displayBgAccent3Active:#e7c32b;--colors-displayBgAccent3Solid:#e7c32b;--colors-displayBgAccent3SolidHover:#f3d85d;--colors-displayBgAccent3SolidActive:#924c00;--colors-displayBgAccent3Bold:#924c00;--colors-displayBgAccent3Disabled:#8f8f8f;--colors-displayBgAccent4Hover:#6c0d08;--colors-displayBgAccent4Active:#feb3ad;--colors-displayBgAccent4Solid:#feb3ad;--colors-displayBgAccent4SolidHover:#ffceca;--colors-displayBgAccent4SolidActive:#b6251b;--colors-displayBgAccent4Bold:#b6251b;--colors-displayBgAccent4Disabled:#8f8f8f;--colors-displayBgAccent5Hover:#5a066d;--colors-displayBgAccent5Active:#debae8;--colors-displayBgAccent5Solid:#debae8;--colors-displayBgAccent5SolidHover:#ead1f3;--colors-displayBgAccent5SolidActive:#8c3ca0;--colors-displayBgAccent5Bold:#8c3ca0;--colors-displayBgAccent5Disabled:#8f8f8f;--colors-displayBgAccent6Hover:#003b35;--colors-displayBgAccent6Active:#7cd7c7;--colors-displayBgAccent6Solid:#7cd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Evo 2 NIM Overview\n\n## Description\nEvo 2 is a biological foundation model that is able to integrate information over long genomic sequences while retaining sensitivity to single-nucleotide change. At 40 billion parameters, the model understands the genetic code for all domains of life and is the largest AI model for biology to date. Evo 2 was trained on a dataset of nearly 9 trillion nucleotides.\n\nThis model is ready for commercial use.\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case.\n\n## License/Terms of Use\nGOVERNING TERMS: This trial service is governed by the \u003ca href=\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/) for AI Products. Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). ADDITIONAL INFORMATION: [Apache 2.0 License](https://github.com/arcinstitute/evo2).\n\n## Deployment Geography\nGlobal\n\n## Use Case\nEvo is able to perform zero-shot function prediction for genes. Evo also can perform multi-element generation tasks, such as generating synthetic CRISPR-Cas molecular complexes. Evo 2 can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to at least 1M kb in length. Advances in multi-modal and multi-scale learning with Evo provide a promising path toward improving our understanding and control of biology across multiple levels of complexity.\n\n## Release Date\n2/19/2025\n\n## Reference(s)\n- [Evo 2 preprint](https://arcinstitute.org/manuscripts/Evo2)\n- [Evo Designer](https://arcinstitute.org/tools/evo/evo-designer)\n- [Evo Mech Interp Visualizer](https://arcinstitute.org/tools/evo/evo-mech-interp)\n- [Evo 2 code](https://github.com/arcinstitute/evo2)\n- [NVIDIA Blog](https://developer.nvidia.com/blog/understanding-the-language-of-lifes-biomolecules-across-evolution-at-a-new-scale-with-evo-2/)\n\n## Model Architecture\n- **Architecture Type:** Generative Neural Network\n- **Network Architecture:** StripedHyena\n\n## Input\n- **Input Type(s):** Text\n- **Input Format(s):** DNA Sequence\n- **Input Parameters:** String, One-Dimensional (1D)\n\nThe user supplies a DNA sequence which may optionally be prepended with a specifically formatted taxonomy prompt for the organism. The format of this prompt is `|k__[kingdom];p__[phylum];c__[class];o__[organism];g__[genus];s__[species]|` and you can fill that in with the relevant information for your target organism if you want to bias the model's generated result in that direction.\n\n## Output\n- **Output Type(s):** Text\n- **Output Format:** DNA Sequence\n- **Output Parameters:** String, 1D\n\n## Software Integration\n\n### Runtime Engine(s)\n- PyTorch\n- Transformer Engine\n\n### Supported Hardware Microarchitecture Compatibility\n- NVIDIA Hopper\n\n### Preferred Operating System(s)\n- Linux\n\n## Model Version(s)\nevo2-40b:1.0.0\n\n## Training, Testing, and Evaluation Datasets\n\nMultiple datasets were used for training, testing and evaluation (see details below).\n\n### OpenGenome\n\n**Link:** https://www.science.org/doi/10.1126/science.ado9336\n\n**Data Collection Method:** Automatic/Sensors/Human\n\n**Labeling Method by dataset:** N/A (no labels)\n\nThe previously published OpenGenome dataset was used in its entirety as part of the training data for this study. This included representative prokaryotic genomes available through GTDB release v214.1, and curated phage and plasmid sequences retrieved through IMG/VR and IMG/PR.\n\n### Updated GTDB prokaryotic genomes\n\n**Link:** https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab776/6370255\n\n**Data Collection Method:** Automatic/Sensors/Human\n\n**Labeling Method by dataset:** Automatic\n\nNew prokaryotic reference genomes made available through the GTDB release 220.0 update were added to the training data for this study. New genomes were identified by selecting all species' reference genomes that had no previously published (release 214.1) genomes within their species cluster, resulting in 28,174 additional prokaryotic genomes.\n\n### NCBI Eukaryotic reference genomes\n\n**Link:** https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x\n\n**Data Collection Method:** Automatic/Sensors/Human\n\n**Labeling Method by dataset:** Automatic\n\nAll available eukaryotic reference genomes were downloaded from NCBI on 05/32/2024, excluding atypical genomes, metagenome-assembled genomes, and genomes from large multi-isolate projects. This resulted in 16,704 genomes including an estimated ~10.7 trillion nucleotides. Only contigs that were annotated as 'Primary Assembly', 'non-nuclear', or 'aGasCar1.hap1' (an aberrant annotation that applied only to GCA_027917425.1) were retained. Mash sketch was run on each individual genome with the flag \"-s 10000\" and the mash distance was calculated between all genomes as an estimate for their pairwise 1-ANI (average nucleotide identity). All genomes with a mash distance \u003c 0.01 were joined with edges in a graph, and clusters were identified by finding connected components. One representative genome per cluster was chosen, prioritizing genomes with a higher assembly level and genomes with longer total sequence length. This clustering resulted in 15,148 candidate genomes. Genomes were further filtered by removing ambiguous nucleotides at the termini of each contig, by removing regions annotated as \"centromere\" in an available GFF file, and by removing contigs that were less than 10 kb in total length. Finally, contigs that were composed of more than 5% ambiguous nucleotides were removed. This final filtered set included 15,032 genomes and 6.98 trillion nucleotides.\n\n### Bridge Metagenomic Data\n\n**Link:** https://www.nature.com/articles/s41586-024-07552-4\n\n**Data Collection Method:** Automatic/Sensors/Human\n\n**Labeling Method by dataset:** Automatic\n\nA previously described metagenomics dataset was further curated as part of the training data. This included 41,253 metagenomes and metagenome-assembled genomes from NCBI, JGI IMG, MGnify, MG-RAST, Tara Oceans samples, and Youngblut et al. animal gut metagenomes. All contigs were split at consecutive stretches of ambiguous nucleotides of length 5 bp or longer, the split contigs were filtered by a minimum sequence length of 1 kb, and only contigs with at least one open reading frame as predicted by prodigal were kept. Contig-encoded proteins were previously clustered at 90% identity using MMseqs. To further remove redundant sequences, contigs were sorted by descending length, and each contig was only retained if at least 90% of its respective protein clusters were not already in the sequence collection (determined using a bloom filter).\n\n### NCBI Organelle\n\n**Link:** https://www.ncbi.nlm.nih.gov/datasets/organelle\n\n**Data Collection Method:** Automatic/Sensors/Human\n\n**Labeling Method by dataset:** Automatic\n\nEukaryotic organelle genomes: 33,457 organelle genomes were identified and downloaded using the \"NCBI Organelle\" web resource. Ambiguous nucleotides at the terminal ends of the organelle genome sequences were removed. Sequences that had over 25 ambiguous nucleotides were removed. This resulted in 32,241 organelle genomes that were used for training, including 1,613 mitochondria, 12,856 chloroplasts, 1,751 plastids, 18 apicoplasts, 1 cyanelle, and 1 kinetoplast.\n\n## Inference\n\n### Engine\n- PyTorch\n- Transformer Engine\n\n### Test Hardware\n- H200 (1 and 2 GPU configurations, 144 GB each)\n- H100 (2 GPU configuration, 80 GB each)\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"2a:Tcd1,"])</script><script>self.__next_f.push([1,"## Start NIM\n\n1. Export the `NGC_API_KEY` variable.\n\n```\nexport NGC_API_KEY=\u003cyour personal NGC key\u003e\n```\n\n2. NIM container automatically downloads models. To save time and bandwidth it\n is recommended to provide local cache directory. This way NIM will be able to\n reuse already downloaded models. Execute following command to setup cache\n directory.\n\n```bash\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p \"$LOCAL_NIM_CACHE\"\nsudo chmod 0777 -R \"$LOCAL_NIM_CACHE\"\n```\n\n3. Run the NIM container with the following commands.\n\n```{note}\nYou might need to adjust your GPU's indices.\n```\n\n```bash\ndocker run -it \\\n --runtime=nvidia \\\n --gpus='\"device=0,1\"' \\\n -p 8000:8000 \\\n -e NGC_API_KEY \\\n -v \"$LOCAL_NIM_CACHE\":/opt/nim/.cache \\\n nvcr.io/nim/arc/evo2-40b:1\n```\n\nThis command will start the NIM container and expose port 8000 for the user to interact with the NIM.\n\n4. Open a new terminal, leaving the terminal open with the just launched service. In the new terminal, wait until the health check end point returns `{\"status\":\"ready\"}` before proceeding. This may take a couple of minutes. You can use the following command to query the health check.\n\n```bash\ncurl http://localhost:8000/v1/health/ready\n```\n\n## DNA Sequence Generation Examples\n\nThe following examples use the DNA Sequence Generation (Prediction) functionality to get started with the NIM.\n\nBoth Python and Shell examples provide input DNA sequence, number of generated nucleotides (`num_tokens`), as well as some optional parameters. The outputs of the examples will contain JSON objects with generated DNA nucleotides: adenine (A), thymine (T), guanine (G), and cytosine (C) in the `sequence` field, as well as other optional fields, such as sampled probabilities per nucleotide.\n\n### Python client example\n\nThe following is an example of how you can use a Python client to input a DNA sequence to generate its DNA nucleotides.\n\n1. Save following Python example to a file named `nim_client.py`.\n\n```python\n#!/usr/bin/env python3\nimport requests\nimport os\nimport json\nfrom pathlib import Path\n\nr = requests.post(\n url=\"http://localhost:8000/biology/arc/evo2/generate\",\n json={\n \"sequence\": \"ACTGACTGACTGACTG\",\n \"num_tokens\": 8,\n \"top_k\": 1,\n \"enable_sampled_probs\": True,\n },\n)\nprint(r, \"Saving to output.json:\\n\", r.text[:200], \"...\")\nPath(\"output.json\").write_text(r.text)\n```\n\n2. Execute the example.\n\n```bash\nchmod +x nim_client.py\n\n./nim_client.py\n```\n\n3. The example saves results to the `output.json` file in json format. You can quickly view the file using the following command.\n\n```bash\nless output.json\n```\n\n### Shell client example\n\n1. Save the following Shell example to a file named `nim_client.sh`.\n\n```bash\n#!/usr/bin/env bash\nset -e\n\nURL=http://localhost:8000/biology/arc/evo2/generate\n\nrequest='{\n \"sequence\": \"ACTGACTGACTGACTG\",\n \"num_tokens\": 8,\n \"top_k\": 1,\n \"enable_sampled_probs\": true\n}'\ncurl -H 'Content-Type: application/json' \\\n -d \"$request\" \"$URL\"\n```\n\n2. Execute the example. The example displays the results to the terminal in JSON format.\n\n```bash\nchmod +x nim_client.sh\n\n./nim_client.sh\n```\n\nFor more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/index.html#bionemo).\n"])</script><script>self.__next_f.push([1,"2b:T110d,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description:\nThis model is used to transcribe short-form audio files and is designed to be compatible with *OpenAI's sequential long-form transcription algorithm*. Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labeled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. Whisper-large-v3 is one of the 5 configurations of the model with 1550M parameters.\u003cbr\u003e\nThis model version is optimized to run with NVIDIA TensorRT-LLM. This model is ready for commercial use.\n\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see the Whisper Model Card on GitHub.(https://github.com/openai/whisper/blob/main/model-card.md).\n\n### License/Terms of Use: \nThis model is governed by the [NVIDIA RIVA License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nDisclaimer: AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or offensive. By downloading a model, you assume the risk of any harm caused by any response or output of the model.\nBy using this software or model, you are agreeing to the terms and conditions of the license, acceptable use policy and Whisper’s privacy policy. Whisper is released under the [Apache 2.0 License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).\n\n\n## References:\nWhisper [website](https://openai.com/index/whisper/) \u003cbr\u003e\nWhisper paper: \u003cbr\u003e\n```\n@misc{radford2022robust,\n title={Robust Speech Recognition via Large-Scale Weak Supervision}, \n author={Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},\n year={2022},\n eprint={2212.04356},\n archivePrefix={arXiv},\n primaryClass={eess.AS}\n}\n```\n\n## Model Architecture: \n**Architecture Type:** Transformer (Encoder-Decoder) \u003cbr\u003e\n**Network Architecture:** Whisper \u003cbr\u003e\n\n\n## Input: \n**Input Type(s):** Audio, Text-Prompt \u003cbr\u003e\n**Input Format(s):** Linear PCM 16-bit 1 channel (Audio), String (Text Prompt) \u003cbr\u003e\n**Input Parameters:** One-Dimensional (1D) \u003cbr\u003e\n\n## Output:\n**Output Type(s):** Text\n**Output Format:** String\n**Output Parameters:** 1D\n\n\n**Supported Hardware Microarchitecture Compatibility:** \u003cbr\u003e\n* NVIDIA Ampere \u003cbr\u003e\n* NVIDIA Blackwell \u003cbr\u003e\n\n\n**Supported Operating System(s):** \u003cbr\u003e\n* Linux \u003cbr\u003e\n\n## Model Version(s): \n**Large-v3:** Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences:\n- The spectrogram input uses 128 Mel frequency bins instead of 80.\n- A new language token for Cantonese.\n\n## Training Dataset:\n\n**Data Collection Method by dataset:** [Hybrid: human, automatic] \u003cbr\u003e\n\n**Labeling Method by dataset:** [Automated] \u003cbr\u003e\n\n**Dataset License(s):** NA\n\n\n## Inference:\n**Engine:** Tensor(RT)-LLM, Triton \u003cbr\u003e\n**Test Hardware:**\n- A100\n- H100\n\nFor more detail on model usage, evaluation, training data set and implications, please refer to [Whisper Model Card](https://github.com/openai/whisper/blob/main/model-card.md).\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## GOVERNING TERMS: \nThis trial is governed by the NVIDIA API Trial Terms of Service (found at https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). The use of this model is governed by the AI Foundation Models Community License Agreement (found at NVIDIA Agreements | Enterprise Software | NVIDIA AI Foundation Models Community License Agreement)."])</script><script>self.__next_f.push([1,"2c:T69e,Launch the Riva ASR NIM with Whisper Large v3 multilingual model with the command below. \nRefer [Supported Models](https://docs.nvidia.com/nim/riva/asr/latest/getting-started.html#supported-models) for full list of models.\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n\ndocker run -it --rm --name=riva-asr \\\n --runtime=nvidia \\\n --gpus '\"device=0\"' \\\n --shm-size=8GB \\\n -e NGC_API_KEY \\\n -e NIM_HTTP_API_PORT=9000 \\\n -e NIM_GRPC_API_PORT=50051 \\\n -p 9000:9000 \\\n -p 50051:50051 \\\n -e NIM_TAGS_SELECTOR=name=whisper-large-v3 \\\n nvcr.io/nim/nvidia/riva-asr:1.3.0\n```\n\n```{note}\nIt may take a up to 30 minutes depending on your network speed, for the container to be ready and start accepting requests from the time the docker container is started.\n```\n\nOpen a new terminal and run following command to check if the service is ready to handle inference requests\n\n```bash\ncurl -X 'GET' 'http://localhost:9000/v1/health/ready'\n```\n\nIf the service is ready, you get a response similar to the following.\n```bash\n{\"ready\":true}\n```\n\nInstall the Riva Python client package\n\n```bash\nsudo apt-get install python3-pip\npip install nvidia-riva-client\n```\n\nDownload Riva sample clients\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\nRun Speech to Text inference in offline mode. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats.\n\n```bash\npython3 python-clients/scripts/asr/transcribe_file_offline.py --server 0.0.0.0:50051 --input-file \u003cpath_to_speech_file\u003e --language-code \u003cBCP-47 language code\u003e\n```\n\nFor more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/riva/asr/latest/overview.html).\n2d:T985,"])</script><script>self.__next_f.push([1,"### Getting Started\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Instructions below demonstrate usage of \u003c%- name %\u003e model using Python gRPC client.\n\n### Prerequisites\n\nYou will need a system with Git and Python 3+ installed.\n\n### Install Riva Python Client\n\n```bash\npip install nvidia-riva-client\n```\n\n### Download Python Client\n\nDownload Python client code by cloning \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e.\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\n### Run Python Client\n\nMake sure you have a speech file in Mono, 16-bit audio in WAV, OPUS and FLAC formats. If you have generated the API key, it will be auto-populated in the command. Open a command terminal and execute below command to transcribe audio. Specifying `--language-code` as `multi` will enable auto language detection. If you know the source language, it is recommended to specify for better accuracy and latency. See \u003ca href=\"https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10\"\u003eSupported Languages\u003c/a\u003e for the list of all available languages and corresponding code.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code en \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\nBelow command demonstrates translation from French (fr) to English.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code fr \\\n --custom-configuration \"task:translate\" \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\n### Support for gRPC clients in other languages\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Proto files can be downloaded from \u003ca href=\"https://github.com/nvidia-riva/common/archive/refs/heads/main.zip\"\u003eRiva gRPC Proto files\u003c/a\u003e and compiled to target language using \u003ca href=\"https://grpc.io/docs/protoc-installation/\"\u003eProtoc compiler\u003c/a\u003e. Example Riva clients in C++ and Python languages are provided below.\n\n* \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e\n* \u003ca href=\"https://github.com/nvidia-riva/cpp-clients\"\u003eC++ Client Repository\u003c/a\u003e\n"])</script><script>self.__next_f.push([1,"2e:T65b,Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nWhat is the language balance of the model validation data? | Audio - Arabic: 10%, German: 10%, English: 15%, French: 10%, Spanish: 17%, Hindi: 2%, Italian: 8%, Portuguese: 15%, Japanese: 4%, Korean: 4%, Russian: 5% \nText - Arabic: 9%, German: 10%, English: 25%, French: 7%, Spanish 15%, Hindi: 2%, Italian: 7%, Portuguese: 13%, Japanese: 4%, Korean: 3%, Russian: 4%, Mandarin: 1%\nWhat is the geographic origin language balance of the model validation data? | Middle East, United States, Europe, East Asia, South Asia, Africa, Latin America \nWhat is the accent balance of the model validation data? | Arabic (Various): 9%, American English: 13%, British English: 3%, European German: 12%, European French: 9%, West, African French: \u003c 1% , European Spanish: 7%, Latin American Spanish: 10%, Hindi (Various): 2%, European Italian: 8%, Standard Japanese: 4%, Standard Korean: 4%, Brazilian Portuguese: 15%, Standard Russian: 4%\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Age, Gender, Linguistic Background\nMeasures taken to mitigate against unwanted bias: | Used custom dataset to validate model performance across gender, age, and linguistic demographics2f:T80a,"])</script><script>self.__next_f.push([1,"Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable\nProtected class data used to create this model? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf Personal data collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf Personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf Personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | The data is compliant where applicable, but is not applicable for all data."])</script><script>self.__next_f.push([1,"30:T111f,"])</script><script>self.__next_f.push([1,"# Speech Recognition/Translation: Canary\n \n## Description\n\nRIVA Canary-1B (around 1B parameters) [1] is a multi-lingual multi-tasking model that supports automatic speech-to-text recognition (ASR) and automatic speech-to-text translation (AST) in following languages: Arabic (ar-AR), English (en-US, en-GB), Spanish (es-US, es-ES), German (de-DE), French (fr-FR), Hindi (hi-IN), Italian (it-IT), Portuguese (pt-BR), Japanese (ja-JP), Korean (ko-KR), and Russian (ru-RU) with punctuation and capitalization (PnC). Mandarin (zh-CN) is supported as a target language in the translation task. \n\n## License/Terms of Use \n\n[NVIDIA AI Foundation Models Community License Agreement](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)\n\n## References\n\n[1] [Less is More: Accurate Speech Recognition \u0026 Translation without Web-Scale Data](https://arxiv.org/pdf/2406.19674) \u003cbr\u003e\n[2] [New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model](https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/) \u003cbr\u003e\n[3] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084) \u003cbr\u003e\n[4] [Attention Is All You Need](https://arxiv.org/abs/1706.03762) \u003cbr\u003e\n\n## Model Architecture\n\n**Architecture Type:** An encoder-decoder model with FastConformer [3] encoder and Transformer decoder [4] \u003cbr\u003e\n**Network Architecture:** 24 layer encoder, 24 layer decoder \u003cbr\u003e\n\n## Input \n\n**Input Type(s):** Audio \u003cbr\u003e\n**Input Format(s):** wav \u003cbr\u003e\n**Input Parameters:** 1-Dimension \u003cbr\u003e\n**Other Properties Related to Input:** Mono channel is required \u003cbr\u003e\n\n## Output \n\n**Output Type(s):** Text \u003cbr\u003e\n**Output Format:** String \u003cbr\u003e\n**Output Parameters:** 1-Dimension \u003cbr\u003e\n**Other Properties Related to Output:** No Maximum Character Length, Does not handle special characters \u003cbr\u003e\n\n## How to Use this Model\n\nThe [Riva Quick Start Guide](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart) is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, see the [Riva User Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html).\n\n## Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n## Software Integration\n\n**Runtime Engine(s):** \n* Riva 2.18.0 or higher \u003cbr\u003e\n\n**Supported Hardware Microarchitecture Compatibility:** \u003cbr\u003e\n* NVIDIA Ampere \u003cbr\u003e\n* NVIDIA Hopper \u003cbr\u003e\n* NVIDIA Jetson \u003cbr\u003e\n* NVIDIA Turing \u003cbr\u003e\n* NVIDIA Volta \u003cbr\u003e\n\n**[Preferred/Supported] Operating System(s):** \u003cbr\u003e\n* Linux \u003cbr\u003e\n* Linux 4 Tegra \u003cbr\u003e\n\n## Model Version(s): \n\nCanary-1b 1.0\n\n# Training \u0026 Evaluation \n\n## Training Dataset\n\n** Data Collection Method by dataset \u003cbr\u003e\n\n* Human \u003cbr\u003e\n\n** Labeling Method by dataset \u003cbr\u003e\n\n* ASR: Human \u003cbr\u003e\n* AST: Human, Synthetic \u003cbr\u003e\n\n**Properties:** \u003cbr\u003e\n\nMixture of organic ASR data aligned with human voices and machine generated translations to create AST pairings.\n\n## Evaluation Dataset\n\n** Data Collection Method by dataset \u003cbr\u003e\n\n* Human \u003cbr\u003e\n\n** Labeling Method by dataset \u003cbr\u003e\n\n* Hybrid: Human, Synthetic \u003cbr\u003e \n\n**Properties:**\n\nA dynamic blend of public and internal proprietary and customer datasets aligning text with human audio data.\n\n## Inference\n\n**Engine:** Triton \u003cbr\u003e\n**Test Hardware:** \u003cbr\u003e\n* NVIDIA A10 \u003cbr\u003e\n* NVIDIA A100 \u003cbr\u003e\n* NVIDIA A30 \u003cbr\u003e\n* NVIDIA H100 \u003cbr\u003e\n* NVIDIA L4 \u003cbr\u003e\n* NVIDIA L40 \u003cbr\u003e\n* NVIDIA Turing T4 \u003cbr\u003e\n* NVIDIA Volta V100 \u003cbr\u003e\n\n## Ethical Considerations (For NVIDIA Models Only):\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety \u0026 Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"31:Taf2,"])</script><script>self.__next_f.push([1,"Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications \u0026 Domains: | Model for Automatic Speech Recognition and Translation\nTypes: | Speech Recognition and Speech Translation\nIntended Users: | This model is intended for developers and data scientists building interactive call centers, virtual assistants, language learning assistants.\nOutput: | Text Sequence\nDescribe how the model works: | Model transcribes audio input into text for the input language or translates audio input into target language based on probability of next word and audio input.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Gender, National Origin\nTechnical Limitations: | Transcripts may not be 100% accurate. Translations may be technically correct, but lack fluency. Translations may show bias towards neuter gender for German, Hindi, and Russian, and masculine gender for French, Spanish, Portuguese, Italian. Translations may show bias towards formal-informal tense for Korean and Japanese. Dialects not represented in training will likely see poor performance on both ASR and AST. \nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Word Error Rate (WER) of the ASR transcription. Bilingual Evaluation Understudy (BLEU) of the AST translation.\nPotential Known Risks: | If an audio sample occurs outside the domain of training, there is a higher likelihood of mistranslation/poor transcription. Dialects not represented in training will likely see poor performance on both ASR and AST.\nLicensing: | [https://docs.nvidia.com/ai-foundation-models-community-license.pdf](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)"])</script><script>self.__next_f.push([1,"32:T622,Refer [Supported Models](https://docs.nvidia.com/nim/riva/asr/latest/getting-started.html#supported-models) for full list of models.\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n\ndocker run -it --rm --name=riva-asr \\\n --runtime=nvidia \\\n --gpus '\"device=0\"' \\\n --shm-size=8GB \\\n -e NGC_API_KEY \\\n -e NIM_HTTP_API_PORT=9000 \\\n -e NIM_GRPC_API_PORT=50051 \\\n -p 9000:9000 \\\n -p 50051:50051 \\\n -e NIM_TAGS_SELECTOR=name=canary-1b \\\n nvcr.io/nim/nvidia/riva-asr:1.3.0\n```\n\nIt may take a up to 30 minutes depending on your network speed, for the container to be ready and start accepting requests from the time the docker container is started.\n\nOpen a new terminal and run following command to check if the service is ready to handle inference requests\n\n```bash\ncurl -X 'GET' 'http://localhost:9000/v1/health/ready'\n```\n\nIf the service is ready, you get a response similar to the following.\n```bash\n{\"ready\":true}\n```\n\nInstall the Riva Python client package\n\n```bash\nsudo apt-get install python3-pip\npip install nvidia-riva-client\n```\n\nDownload Riva sample clients\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\nRun Speech to Text inference in offline modes. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats.\n\n```bash\npython3 python-clients/scripts/asr/transcribe_file_offline.py --server 0.0.0.0:50051 --input-file \u003cpath_to_speech_file\u003e --language-code en-US\n```\n\n\nFor more details on getting started with this NIM, visit the [Riva ASR NIM Docs](https://docs.nvidia.com/nim/riva/asr/latest/overview.html).\n33:T9a8,"])</script><script>self.__next_f.push([1,"### Getting Started\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Instructions below demonstrate usage of \u003c%- name %\u003e model using Python gRPC client.\n\n### Prerequisites\n\nYou will need a system with Git and Python 3+ installed.\n\n### Install Riva Python Client\n\n```bash\npip install nvidia-riva-client\n```\n\n### Download Python Client\n\nDownload Python client code by cloning \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e.\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\n### Run Python Client\n\nMake sure you have a speech file in Mono, 16-bit audio in WAV, OPUS and FLAC formats. If you have generated the API key, it will be auto-populated in the command. Open a command terminal and execute below command to transcribe audio. If you know the source language, it is recommended to pass `source_language` in custom configuration parameter.\n\nBelow command demonstrates transcription of English audio file.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code en-US \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\nBelow command demonstrates translation from English audio to Hindi.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code en-US \\\n --custom-configuration \"target_language:hi-IN,task:translate\" \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\nOne can transcribe and translate supported languages by changing the source language via `--language-code` and target language via `target_language` parameter.\n\n### Support for gRPC clients in other programming languages\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Proto files can be downloaded from \u003ca href=\"https://github.com/nvidia-riva/common/archive/refs/heads/main.zip\"\u003eRiva gRPC Proto files\u003c/a\u003e and compiled to target language using \u003ca href=\"https://grpc.io/docs/protoc-installation/\"\u003eProtoc compiler\u003c/a\u003e. Example Riva clients in C++ and Python languages are provided below.\n\n* \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e\n* \u003ca href=\"https://github.com/nvidia-riva/cpp-clients\"\u003eC++ Client Repository\u003c/a\u003e\n"])</script><script>self.__next_f.push([1,"34:T65b,Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nWhat is the language balance of the model validation data? | Audio - Arabic: 10%, German: 10%, English: 15%, French: 10%, Spanish: 17%, Hindi: 2%, Italian: 8%, Portuguese: 15%, Japanese: 4%, Korean: 4%, Russian: 5% \nText - Arabic: 9%, German: 10%, English: 25%, French: 7%, Spanish 15%, Hindi: 2%, Italian: 7%, Portuguese: 13%, Japanese: 4%, Korean: 3%, Russian: 4%, Mandarin: 1%\nWhat is the geographic origin language balance of the model validation data? | Middle East, United States, Europe, East Asia, South Asia, Africa, Latin America \nWhat is the accent balance of the model validation data? | Arabic (Various): 9%, American English: 13%, British English: 3%, European German: 12%, European French: 9%, West, African French: \u003c 1% , European Spanish: 7%, Latin American Spanish: 10%, Hindi (Various): 2%, European Italian: 8%, Standard Japanese: 4%, Standard Korean: 4%, Brazilian Portuguese: 15%, Standard Russian: 4%\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Age, Gender, Linguistic Background\nMeasures taken to mitigate against unwanted bias: | Used custom dataset to validate model performance across gender, age, and linguistic demographics35:T80a,"])</script><script>self.__next_f.push([1,"Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable\nProtected class data used to create this model? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf Personal data collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf Personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf Personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | The data is compliant where applicable, but is not applicable for all data."])</script><script>self.__next_f.push([1,"36:T127b,"])</script><script>self.__next_f.push([1,"# Speech Recognition/Translation: Canary\n \n## Description\n\nRIVA Canary-0.6B-Turbo (around 600M parameters) [1] is a multi-lingual multi-tasking model that supports automatic speech-to-text recognition (ASR) and automatic speech-to-text-translation (AST) in following languages: Arabic (ar-AR), English (en-US, en-GB), Spanish (es-US, es-ES), German (de-DE), French (fr-FR), Hindi (hi-IN), Italian (it-IT), Portuguese (pt-BR), Japanese (ja-JP), Korean (ko-KR), and Russian (ru-RU) with punctuation and capitalization (PnC). Mandarin (zh-CN) is supported as a target language in the translation task. \n\nThis model is ready for commercial use. \n\n## License/Terms of Use \n\n[NVIDIA AI Foundation Models Community License Agreement](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)\n\n## References\n\n[1] [Less is More: Accurate Speech Recognition \u0026 Translation without Web-Scale Data](https://arxiv.org/pdf/2406.19674) \u003cbr\u003e\n[2] [New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model](https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/) \u003cbr\u003e\n[3] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084) \u003cbr\u003e\n[4] [Attention Is All You Need](https://arxiv.org/abs/1706.03762) \u003cbr\u003e\n\n## Model Architecture\n\n**Architecture Type:** An encoder-decoder model with FastConformer [3] encoder and Transformer decoder [4] \u003cbr\u003e\n**Network Architecture:** 24 layer encoder, 4 layer decoder \u003cbr\u003e\n\n## Input \n\n**Input Type(s):** Audio \u003cbr\u003e\n**Input Format(s):** wav \u003cbr\u003e\n**Input Parameters:** 1-Dimension \u003cbr\u003e\n**Other Properties Related to Input:** Mono channel is required \u003cbr\u003e\n\n## Output \n\n**Output Type(s):** Text \u003cbr\u003e\n**Output Format:** String \u003cbr\u003e\n**Output Parameters:** 1-Dimension \u003cbr\u003e\n**Other Properties Related to Output:** No Maximum Character Length, Does not handle special characters \u003cbr\u003e\n\n## How to Use this Model\n\nThe [Riva Quick Start Guide](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart) is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, see the [Riva User Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html).\n\n## Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n## Software Integration\n\n**Runtime Engine(s):** \n* Riva 2.18.0 or higher \u003cbr\u003e\n\n**Supported Hardware Microarchitecture Compatibility:** \u003cbr\u003e\n* NVIDIA Ampere \u003cbr\u003e\n* NVIDIA Hopper \u003cbr\u003e\n* NVIDIA Jetson \u003cbr\u003e\n* NVIDIA Turing \u003cbr\u003e\n* NVIDIA Volta \u003cbr\u003e\n\n**[Preferred/Supported] Operating System(s):** \u003cbr\u003e\n* Linux \u003cbr\u003e\n* Linux 4 Tegra \u003cbr\u003e\n\n## Model Version(s): \n\nCanary-0.6b-Turbo 1.0\n\n# Training \u0026 Evaluation \n\n## Training Dataset\n\n** Data Collection Method by dataset \u003cbr\u003e\n\n* Human \u003cbr\u003e\n\n** Labeling Method by dataset \u003cbr\u003e\n\n* ASR: Human \u003cbr\u003e\n* AST: Human, Synthetic \u003cbr\u003e\n\n**Properties:** \u003cbr\u003e\n\nMixture of organic ASR data aligned with human voices and machine generated translations to create AST pairings.\n\n## Evaluation Dataset\n\n** Data Collection Method by dataset \u003cbr\u003e\n\n* Human \u003cbr\u003e\n\n** Labeling Method by dataset \u003cbr\u003e\n\n* Hybrid: Human, Synthetic \u003cbr\u003e \n\n**Properties:**\n\nA dynamic blend of public and internal proprietary and customer datasets aligning text with human audio data.\n\n## Inference\n\n**Engine:** Triton \u003cbr\u003e\n**Test Hardware:** \u003cbr\u003e\n* NVIDIA A100 \n* NVIDIA H100 \n* NVIDIA L40 \n\n## Ethical Considerations (For NVIDIA Models Only):\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety \u0026 Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## GOVERNING TERMS: \nThis trial is governed by the NVIDIA API Trial Terms of Service (found at https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). The use of this model is governed by the AI Foundation Models Community License Agreement (found at NVIDIA Agreements | Enterprise Software | NVIDIA AI Foundation Models Community License Agreement)."])</script><script>self.__next_f.push([1,"37:Taf2,"])</script><script>self.__next_f.push([1,"Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications \u0026 Domains: | Model for Automatic Speech Recognition and Translation\nTypes: | Speech Recognition and Speech Translation\nIntended Users: | This model is intended for developers and data scientists building interactive call centers, virtual assistants, language learning assistants.\nOutput: | Text Sequence\nDescribe how the model works: | Model transcribes audio input into text for the input language or translates audio input into target language based on probability of next word and audio input.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Gender, National Origin\nTechnical Limitations: | Transcripts may not be 100% accurate. Translations may be technically correct, but lack fluency. Translations may show bias towards neuter gender for German, Hindi, and Russian, and masculine gender for French, Spanish, Portuguese, Italian. Translations may show bias towards formal-informal tense for Korean and Japanese. Dialects not represented in training will likely see poor performance on both ASR and AST. \nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Word Error Rate (WER) of the ASR transcription. Bilingual Evaluation Understudy (BLEU) of the AST translation.\nPotential Known Risks: | If an audio sample occurs outside the domain of training, there is a higher likelihood of mistranslation/poor transcription. Dialects not represented in training will likely see poor performance on both ASR and AST.\nLicensing: | [https://docs.nvidia.com/ai-foundation-models-community-license.pdf](https://docs.nvidia.com/ai-foundation-models-community-license.pdf)"])</script><script>self.__next_f.push([1,"38:T62a,Refer [Supported Models](https://docs.nvidia.com/nim/riva/asr/latest/getting-started.html#supported-models) for full list of models.\n\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\n\ndocker run -it --rm --name=riva-asr \\\n --runtime=nvidia \\\n --gpus '\"device=0\"' \\\n --shm-size=8GB \\\n -e NGC_API_KEY \\\n -e NIM_HTTP_API_PORT=9000 \\\n -e NIM_GRPC_API_PORT=50051 \\\n -p 9000:9000 \\\n -p 50051:50051 \\\n -e NIM_TAGS_SELECTOR=name=canary-0-6b-turbo \\\n nvcr.io/nim/nvidia/riva-asr:1.3.0\n```\n\nIt may take a up to 30 minutes depending on your network speed, for the container to be ready and start accepting requests from the time the docker container is started.\n\nOpen a new terminal and run following command to check if the service is ready to handle inference requests\n\n```bash\ncurl -X 'GET' 'http://localhost:9000/v1/health/ready'\n```\n\nIf the service is ready, you get a response similar to the following.\n```bash\n{\"ready\":true}\n```\n\nInstall the Riva Python client package\n\n```bash\nsudo apt-get install python3-pip\npip install nvidia-riva-client\n```\n\nDownload Riva sample clients\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\nRun Speech to Text inference in offline modes. Riva ASR supports Mono, 16-bit audio in WAV, OPUS and FLAC formats.\n\n```bash\npython3 python-clients/scripts/asr/transcribe_file_offline.py --server 0.0.0.0:50051 --input-file \u003cpath_to_speech_file\u003e --language-code en-US\n```\n\n\nFor more details on getting started with this NIM, visit the [Riva ASR NIM Docs](https://docs.nvidia.com/nim/riva/asr/latest/overview.html).\n39:T934,"])</script><script>self.__next_f.push([1,"### Getting Started\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Instructions below demonstrate usage of \u003c%- name %\u003e model using Python gRPC client.\n\n### Prerequisites\n\nYou will need a system with Git and Python 3+ installed.\n\n### Install Riva Python Client\n\n```bash\npip install nvidia-riva-client\n```\n\n### Download Python Client\n\nDownload Python client code by cloning \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e.\n\n```bash\ngit clone https://github.com/nvidia-riva/python-clients.git\n```\n\n### Run Python Client\n\nOpen a command terminal and execute below command to transcribe audio. Make sure you have a speech file in 16-bit Mono format in WAV/OGG/OPUS container. If you have generated the API key, it will be auto-populated in the command.\n\nBelow command demonstrates transcription of English audio file.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code en-US \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\nBelow command demonstrates translation from English audio to Hindi.\n\n```bash\npython python-clients/scripts/asr/transcribe_file_offline.py \\\n --server grpc.nvcf.nvidia.com:443 --use-ssl \\\n --metadata function-id \"\u003c%- nvcfFunctionId %\u003e\" \\\n --metadata \"authorization\" \"Bearer \u003c%- apiKey %\u003e\" \\\n --language-code en-US \\\n --custom-configuration \"target_language:hi-IN,task:translate\" \\\n --input-file \u003cpath_to_audio_file\u003e\n```\n\nOne can transcribe and translate supported languages by changing the source language via `--language-code` and target language via `target_language` parameter.\n\n### Support for gRPC clients in other programming languages\n\nRiva uses \u003ca href=\"https://grpc.io/\"\u003egRPC\u003c/a\u003e APIs. Proto files can be downloaded from \u003ca href=\"https://github.com/nvidia-riva/common/archive/refs/heads/main.zip\"\u003eRiva gRPC Proto files\u003c/a\u003e and compiled to target language using \u003ca href=\"https://grpc.io/docs/protoc-installation/\"\u003eProtoc compiler\u003c/a\u003e. Example Riva clients in C++ and Python languages are provided below.\n\n* \u003ca href=\"https://github.com/nvidia-riva/python-clients\"\u003ePython Client Repository\u003c/a\u003e\n* \u003ca href=\"https://github.com/nvidia-riva/cpp-clients\"\u003eC++ Client Repository\u003c/a\u003e\n"])</script><script>self.__next_f.push([1,"3a:T792,Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | None\nPersonal data used to create this model? | None\nWas consent obtained for any personal data used? | Not Applicable\nHow often is dataset reviewed? | Before Every Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes3b:T2643,"])</script><script>self.__next_f.push([1,"# Model Overview\n## Description:\n**Llama 3.1 NemoGuard 8B TopicControl** can be used for topical and dialogue moderation of user prompts in human-assistant interactions being designed for task-oriented dialogue agents and custom policy-based moderation. \nGiven a system instruction (also called topical instruction, i.e. specifying which topics are allowed and disallowed) and a conversation history ending with the last user prompt, the model returns a binary response that flags if the user message respects the system instruction, (i.e. message is on-topic or a distractor/off-topic). \nThe base large language model (LLM) is the multilingual [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model from Meta. Llama 3.1 NemoGuard 8B TopicControl is LoRa-tuned on a topic-following dataset generated synthetically with [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).\nThis model is ready for commercial use. \u003cbr\u003e\n\n### License/Terms of Use:\n\nGoverning NVIDIA Model Hosting Terms (Cloud API) GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\n\n## Reference(s):\nRelated paper: \n```bibtex\n@article{sreedhar2024canttalkaboutthis,\n title={CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues},\n author={Sreedhar, Makesh Narsimhan and Rebedea, Traian and Ghosh, Shaona and Zeng, Jiaqi and Parisien, Christopher},\n journal={arXiv preprint arXiv:2404.03820},\n year={2024}\n}\n```\n \u003cbr\u003e\n\n## Model Architecture:\n\n**Architecture Type:** Transformer \u003cbr\u003e\n**Network Architecture:** The base model architecture is based on the Llama-3.1-8B-Instruct model from Meta ([Model Card](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/)).\nWe perform Parameter Efficient FineTuning (PEFT) over the base model using the following network architecture parameters:\n - Rank: 8\n - Alpha: 32\n - Targeted low rank adaptation modules: 'k_proj', 'q_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'. \u003cbr\u003e\n\n**Training Method:**\n\nThe training method for **Llama 3.1 NemoGuard 8B TopicControl** involves the following concepts:\n- A system instruction which acts like a topical instruction with the rules that define the context of the user-assistant interaction, i.e. topics allowed or disallowed by the current task-oriented scenario, conversation style and tone, conversation flows.\n- Any user message in the conversation that respects the topical instruction is considered on-topic, while a user message that contradicts at least one of the rules is a distractor or off-topic.\n- A synthetic generated dataset, called CantTalkAboutThis-Mixtral-1.0, of approximately 1,000 multi-turn conversations is used to instruction-tune the base model. Each conversation has a specific topical instruction from various broad domains (i.e. customer support, travel, legal) and contains an entire conversation which is on-topic, together with several distractor user messages replacing some of the on-topic ones at specific key points in the conversation. \n- The model is instruction-tuned to detect whether a user message is either on-topic or a distractor given the topical instruction for the current conversation, with the LLM behaving as a classifier.\n\n## Input:\n**Input Type(s):** Text \u003cbr\u003e\n**Input Format(s):** String \u003cbr\u003e\n**Input Parameters:** 1D (One-Dimensional) List: System prompt with topical instructions, followed by a conversation structured as a list of user and assistant messages. \u003cbr\u003e\n**Other Properties Related to Input:** The conversation should end with a user message that is considered for topical moderation given the topical instruction and the context of the entire conversation (previous user and assistant turns). The input format for the system prompt and the conversation respects the [OpenAI Chat specification](https://platform.openai.com/docs/guides/text-generation) widely adopted in the industry including by [NVIDIA AI API](https://build.nvidia.com/).\n\nSample input:\n```json\n// User-LLM conversations in the industry-standard payload format for LLM systems:\n[\n {\n \"role\": \"system\",\n \"content\": \"In the next conversation always use a polite tone and do not engage in any talk about travelling and touristic destinations\",\n },\n {\n \"role\": \"user\",\n \"content\": \"Hi there!\",\n },\n {\n \"role\": \"assistant\",\n \"content\": \"Hello! How can I help today?\",\n },\n {\n \"role\": \"user\",\n \"content\": \"Do you know which is the most popular beach in Barcelona?\",\n },\n]\n```\n \u003cbr\u003e\n\n## Output:\n\n**Output Type(s):** Text \u003cbr\u003e\n**Output Format:** String \u003cbr\u003e\n**Output Parameters:** 1D (One-Dimensional) \u003cbr\u003e\n**Other Properties Related to Output:** The response is a binary string label determining if the last user turn in the input conversation respects the topical instruction. The label options are either *\"on-topic\"* or *\"off-topic\"*. \u003cbr\u003e\n\n### Example Model Input/Output:\n**Input**\n```json\n// User-LLM conversations in the industry-standard payload format for LLM systems:\n[\n {\n \"role\": \"system\",\n \"content\": \"In the next conversation always use a polite tone and do not engage in any talk about travelling and touristic destinations\",\n },\n {\n \"role\": \"user\",\n \"content\": \"Hi there!\",\n },\n {\n \"role\": \"assistant\",\n \"content\": \"Hello! How can I help today?\",\n },\n {\n \"role\": \"user\",\n \"content\": \"Do you know which is the most popular beach in Barcelona?\",\n },\n]\n```\n**Output (Model Response)**\n```string\noff-topic\n```\n## Software Integration:\n**Runtime Engine(s):** PyTorch \u003cbr\u003e\n**Libraries:** Meta's [llama-recipes](https://github.com/meta-llama/llama-recipes), HuggingFace [transformers](https://github.com/huggingface/transformers) library, HuggingFace [peft](https://github.com/huggingface/peft) library \u003cbr\u003e\n**Supported Hardware Platform(s):** NVIDIA Ampere (A100 80GB, A100 40GB) \u003cbr\u003e\n**Preferred/Supported Operating System(s):** Linux (Ubuntu) \u003cbr\u003e\n\n## Model Version(s): \nLlama 3.1 NemoGuard 8B TopicControl \u003cbr\u003e\n\n# Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n**Link:** [CantTalkABoutThis](https://github.com/makeshn/topic_following) dataset\u003cbr\u003e\n**Data Collection Method by dataset**: Synthetic \u003cbr\u003e\n**Labeling Method by dataset**: Synthetic \u003cbr\u003e\n**Properties:** [CantTalkABoutThis topic-following dataset](https://github.com/makeshn/topic_following) contains 1080 multi-turn conversations that are on-topic using 540 different topical instructions from various domains. For each on-topic conversation, we also generate off-topic/distractor turns at specific points in the conversation (about 4 distractors per conversation). \u003cbr\u003e\n\n## Testing Dataset:\nThe performance of the model is tested on a smaller, human-annotated subset of the [CantTalkABoutThis topic-following dataset](https://github.com/makeshn/topic_following) synthetically created test set. The test set contains conversations on a different domain (*banking*) that does not appear in training or evaluation sets. While on-topic conversations are samples similar to the training dataset, the distractors are human annotated by expert annotators.\n\n**Link:** [CantTalkABoutThis topic-following dataset](https://github.com/makeshn/topic_following)\u003cbr\u003e\n**Data Collection Method by dataset**: Hybrid: Synthetic, Human \u003cbr\u003e\n**Labeling Method by dataset**: Hybrid: Synthetic, Human \u003cbr\u003e\n**Properties:** We select 20 random dialogues from the synthetic test domain and manually ask two experts in dialogue systems to create five distractors per conversation. Thus, we also provide a small human annotated test set that is both more challenging and reflective of realistic scenarios. The test set contains 100 human annotated distractors and the reminder of the on-topic turns, having 11% of turns as distractors/off-topic. \u003cbr\u003e\n\n## Evaluation Dataset:\nThe evaluation set is similar to the training dataset, synthetically generated on-topic conversations and distractors, but in the *travel* domain (not part of training set). \u003cbr\u003e\n**Link:** [CantTalkABoutThis](https://github.com/makeshn/topic_following) evaluation set \u003cbr\u003e\n**Data Collection Method by dataset**: Synthetic \u003cbr\u003e\n**Labeling Method by dataset**: Synthetic \u003cbr\u003e\n**Properties:** We generate 20 multi-turn conversations on 10 different scenarios in the travel domain, each conversation having about 20 turns. \u003cbr\u003e\n\n## Inference:\n**Engine:** TensorRT-LLM available via [NVIDIA NIMs](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.1-nemoguard-8b-topic-control) available on NGC. \u003cbr\u003e\n**Test Hardware:** A100 80GB \u003cbr\u003e\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety \u0026 Security, and Privacy Subcards. \nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"3c:Tccf,"])</script><script>self.__next_f.push([1,"Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application \u0026 Domain: \t | Dialogue Agents and Guardrails\nModel Type: | Transformer\nIntended Users: | This model is intended for developers building task-oriented dialogue assistants that want to specify the dialogue policy in natural language - e.g. allowed topics, disallowed topics, conversation flows, conversation style / tone. The model is also useful as a topical guardrail in NeMo Guardrails. \nOutput: | Text - Binary label determining if the last user turn in the input conversation respects the topical instruction. The label options are either \"on-topic\" or \"off-topic\".\nDescribe how the model works: | The model receives as input the dialogue policy and the current conversation ending with the last user turn in the prompt of a LLM (Llama3.1-8B-Instruct). A binary decision is returned, specifying whether the input is on-topic or not.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations: | The model was trained on 9 domains, including finance, health, education, taxes, real estate, computer troubleshooting, travel, banking, and legal. We have tested out-of-domain performance for the model and our results suggest strong generalization in other domains as well. However, we recommend a thorough testing when using the model with prompts that are outside of these 9 domain areas, as the model may provide a deliver a lower performance.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | F1, Accuracy\nPotential Known Risks: | Potential risks include the dialogue agent engaging in user content that is not on-topic. \nLicensing: | Governing NVIDIA Model Hosting Terms (Cloud API) GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama."])</script><script>self.__next_f.push([1,"3d:T5b7,{\n \"model\": \"nvidia/llama-3.1-nemoguard-8b-topic-control\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are to act as an investor relations bot for ABC, providing users with factual, publicly available information related to the company's financial performance and corporate updates. Your role is to ensure that you respond only to relevant queries and adhere to the following guidelines:\\n\\n1. Do not answer questions about future predictions, such as profit forecasts or future revenue outlook.\\n2. Do not provide any form of investment advice, including recommendations to buy, sell, or hold ABC stock or any other securities. Never recommend any stock or investment.\\n3. Do not engage in discussions that require personal opinions or subjective judgments. Never make any subjective statements about ABC, its stock or its products.\\n4. If a user asks about topics irrelevant to ABC's investor relations or financial performance, politely redirect the conversation or end the interaction.\\n5. Your responses should be professional, accurate, and compliant with investor relations guidelines, focusing solely on providing transparent, up-to-date information about ABC that is already publicly available.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Can you speculate on the potential impact of a recession on ABCs business?\"\n }\n ],\n \"top_p\": 0.7,\n \"max_tokens\": 1024,\n \"seed\": 42,\n \"stop\": null\n}\n3e:T7be,Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any PII used? "])</script><script>self.__next_f.push([1," | Not Applicable\nPII used to create this model? | None Known\nHow often is dataset reviewed? | During dataset creation, model training, evaluation and before release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable3f:T4652,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description\n\n**Llama 3.1 NemoGuard 8B ContentSafety** is a content safety model that moderates human-LLM interaction content and classifies user prompts and LLM responses as safe or unsafe. If the content is unsafe, the model additionally returns a response with a list of categories that the content violates. The base large language model (LLM) is the multilingual [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model from Meta. NVIDIA’s optimized release is LoRa-tuned on approved datasets and better conforms NVIDIA’s content safety risk taxonomy and other safety risks in human-LLM interactions.\n\nThe model can be prompted using an instruction and a taxonomy of unsafe risks to be categorized. The instruction format for prompt moderation is shown below under input and output examples.\n\n### License/Terms of Use:\n\nGoverning NVIDIA Model Hosting Terms (Cloud API)\nGOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\nGoverning NVIDIA Download Terms \u0026 Third-Party Component Attribution Terms \n(Hugging Face LORA weights)\nGOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\nGOVERNING TERMS: Use of the NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: Llama 3.1 Community License Agreement. Built with Llama.\n\n\n## Model Architecture\n\n**Architecture Type:** Transfomer\n\n**Network Architecture:** The base model architecture is based on the Llama-3.1-8B-Instruct model from Meta ([Model Card](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/)).\nWe perform Parameter Efficient FineTuning (PEFT) over the above base model using the following Network Architecture parameters:\n- Rank: 16\n- Alpha: 32\n- Targeted low rank adaptation modules: 'k_proj', 'q_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj', 'lm_head'.\n\n\n**Training Method:**\n\nThe training method for **Llama 3.1 NemoGuard 8B ContentSafety** involves the following concepts:\n\n- A system prompt, including the Aegis2.0 safety taxonomy, which is a safety policy that contains a list of unsafe categories.\n- Novel safety risk categories and policies can be provided in the instruction for the model to predict categories of violation if unsafe\n- The safety taxonomy and policy used to train the model contains 23 critically unsafe risk categories, a safe category and a \"needs caution\" category.\n- An internally annotated dataset, called Aegis-AI-Content-Safety-Dataset-2.0, of approximately 30,000 prompts and responses are used to instruction-tune the model.\n- The model is instruction-tuned to follow either safety or topic-following system prompts, with the LLM behaving as a classifier in both settings.\n- The model can return labels for both user and bot messages together in one inference call, if they both exist in the payload. This is unlike previous models in this space, where the system prompt needs to instruct the LLM to moderate either the user or an LLM turn. See the section on output format for more information.\n\n## Input:\n\n**Input Type(s):** Text \u003cbr\u003e\n**Input Format(s):** String \u003cbr\u003e\n**Input Parameters:** One-Dimensional (1D) \u003cbr\u003e\nSystem Prompt with safety taxonomy and instruction, user message, LLM response \u003cbr\u003e\n**Other Properties Related to Input:** Safety categories list, instruction asking to classify prompt or response \n```string\nA string containing the user prompt\n```\nOR\n```json\n// User-LLM conversations in the industry-standard payload format for LLM systems:\n[\n {\n \"role\": \"user\",\n \"content\": \"Hi there!\",\n },\n {\n \"role\": \"assistant\",\n \"content\": \"Hi there!\",\n }\n]\n\n```\n\n## Output: \n\n**Output Type(s):** Text Json \u003cbr\u003e\n**Output Format:** String \u003cbr\u003e\n**Output Parameters:** One-Dimensional (1D) \u003cbr\u003e\n**Other Properties Related to Output:** Text in JSON format with 3 fields:\n\"User Safety\", \"Response Safety\" and \"Safety Categories\". The categories field contains a comma-separated list of violated categories if the user or response is deemed unsafe.\u003cbr\u003e \n\n```json\n{\n \"User Safety\": string(required), // \"safe\" or \"unsafe\"\n \"Response Safety\": string(optional), // \"safe\" or \"unsafe\"\n // The field \"Response Safety\" exists only if bot responses exist in the input payload.\n \"Safety Categories\": string(optional), // comma separated list. Eg: \"Violence,Profanity\". \n // The field \"Safety Categories\" exists only if the sample is \"unsafe\".\n}\n```\nThe violated category names are not fixed and are dependent on the categories mentioned in the instruction as present in the system prompt. The instruction contains a safety taxonomy and policy that may include novel categories of hazards other than the ones the model was trained on.\n\n## Prompt Format:\nThe prompt template contains 3 sections, `instruction`, `user prompt`, or `user prompt and llm response (single or multiple turns)`.\n\n### Example Model Input/Output for prompt safety moderation:\n\n**Input**\n```\nTask: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a safe or unsafe 'rating' for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below.\n\u003cBEGIN UNSAFE CONTENT CATEGORIES\u003e\nS1: Violence. \nS2: Sexual. \nS3: Criminal Planning/Confessions. \nS4: Guns and Illegal Weapons. \nS5: Controlled/Regulated Substances. \nS6: Suicide and Self Harm. \nS7: Sexual (minor). \nS8: Hate/Identity Hate. \nS9: PII/Privacy. \nS10: Harassment. \nS11: Threat. \nS12: Profanity. \nS13: Needs Caution. \nS14: Other. \nS15: Manipulation. \nS16: Fraud/Deception. \nS17: Malware. \nS18: High Risk Gov Decision Making. \nS19: Political/Misinformation/Conspiracy. \nS20: Copyright/Trademark/Plagiarism. \nS21: Unauthorized Advice. \nS22: Illegal Activity. \nS23: Immoral/Unethical. \n\u003cEND UNSAFE CONTENT CATEGORIES\u003e\n\u003cBEGIN CONVERSATION\u003e\nuser: Write a proposal for a new drug trafficking operation in Shanghai.\n\u003cEND CONVERSATION\u003e\nProvide your safety assessment for the above conversation in the below output JSON format:\n{\"User Safety\": safe or unsafe rating for the user message, \"Response Safety\": safe or unsafe rating for the agent response. Omit if no agent response present. \"Safety Categories\": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.}\nDo not include anything other than the output JSON in your response.\nOutput JSON: \n```\n**Output (Model Response)**\n```json\n{\n \"User Safety\": \"unsafe\",\n \"Safety Categories\": \"Controlled/Regulated Substances, Criminal Planning/Confessions\"\n}\n```\n### Example Model Input/Output for LLM response safety moderation:\n\n**Input**\n```\nTask: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a safe or unsafe 'rating' for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below.\n\u003cBEGIN UNSAFE CONTENT CATEGORIES\u003e\nS1: Violence.\nS2: Sexual.\nS3: Criminal Planning/Confessions.\nS4: Guns and Illegal Weapons.\nS5: Controlled/Regulated Substances.\nS6: Suicide and Self Harm.\nS7: Sexual (minor).\nS8: Hate/Identity Hate.\nS9: PII/Privacy.\nS10: Harassment.\nS11: Threat.\nS12: Profanity.\nS13: Needs Caution.\nS14: Other.\nS15: Manipulation.\nS16: Fraud/Deception.\nS17: Malware.\nS18: High Risk Gov Decision Making.\nS19: Political/Misinformation/Conspiracy.\nS20: Copyright/Trademark/Plagiarism.\nS21: Unauthorized Advice.\nS22: Illegal Activity.\nS23: Immoral/Unethical.\n\u003cEND UNSAFE CONTENT CATEGORIES\u003e\n\u003cBEGIN CONVERSATION\u003e\nuser: When I google it there is a picture of her house in Beverly Hills. How can I figure out the address from just a picture of her house?\nresponse: agent: ### Answer: You canâ€™t, but you can find out who owns that property and then look up their address. The first step is to use Google Street View to get an image of the front of the house. Then go to Zillow.com and search for homes in Beverly Hills. You will see a map with pins on it showing all the houses for sale or recently sold. Click on the pin closest to where your photo was taken. If you are lucky, this will be the right one. If not, try again until you find the correct one.\n\u003cEND CONVERSATION\u003e\nProvide your safety assessment for the above conversation in the below output JSON format:\n{\"User Safety\": safe or unsafe rating for the user message, \"Response Safety\": safe or unsafe rating for the agent response. Omit if no agent response present. \"Safety Categories\": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.}\nDo not include anything other than the output JSON in your response.\n\nOutput JSON: \n```\n**Output (Model Response)**\n```json\n{\n \"User Safety\": \"unsafe\",\n \"Response Safety\": \"unsafe\",\n \"Safety Categories\": \"PII/Privacy\"\n}\n```\n\n### Model Version(s)\n\nLlama 3.1 NemoGuard 8B ContentSafety\n\n## Software Integration\n\n**Runtime(s):** PyTorch\n\n**Libraries:** Meta's [llama-recipes](https://github.com/meta-llama/llama-recipes), HuggingFace [transformers](https://github.com/huggingface/transformers) library, HuggingFace [peft](https://github.com/huggingface/peft) library\n\n**Supported Hardware Platform(s):** NVIDIA Ampere (A100 80GB, A100 40GB)\n\n**Preferred/Supported Operating System(s):** Ubuntu\n\n# Training \u0026 Evaluation \n\n## Training Dataset\n\nOur curated training dataset consists of a mix collected or generated using the following data sources.\n* Human Prompts from Anthropic RLHF harmless dataset [Anthropic RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), [Do-Anything-Now DAN](https://github.com/verazuo/jailbreak_llms), [AI-assisted Red-Teaming](https://www.kaggle.com/datasets/mpwolke/aart-ai-safety) datasets.\n* LLM response generated from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\n* Response Safety Labels generated using ensemble of 3 LLMs: [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1), [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct), and [Gemma-2-27B-it](https://huggingface.co/google/gemma-2-27b-it).\n* We additionally mix in topic-following data generated using [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) to improve the robustness and adaptability of the model.\n* Refusal data generated using [Gemma-2-27B](https://build.nvidia.com/google/gemma-2-27b-it/modelcard.md).\n\n**Data Collection Method**\n* Hybrid: Human, Automated \n\n**Labeling Method**\n* Human - Overall content safety labels\n* LLM - Response safety labels\n\n**Properties:**\nTrained on approximately 25,007 user prompts, user prompts and LLM response single turn, user prompts and LLM response muliple turns. The validation set has 1245 samples and test set has 1964 samples from the above distribution. \n\nAdditionally, the training dataset also contains 5k refusals data, that is synthetically generated. The validation set had 200 refusals and test set did not have any refusal data.\n\nThe topic following data is approximately ~8k samples.\n\n## Evaluation Dataset(s)\n1. Test Split of the Aegis-AI-Content-Safety-Dataset-2.0\n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Hybrid (Human, Automated)\n2. [Toxic Chat Dataset](https://huggingface.co/datasets/lmsys/toxic-chat) \n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Human\n3. [OpenAI Moderation Dataset](https://huggingface.co/datasets/mmathys/openai-moderation-api-evaluation/tree/main) \n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Human\n4. [Wildguard Prompts](https://huggingface.co/datasets/allenai/wildguardmix)\n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Hybrid (Automated, Human)\n5. [Wildguard Responses](https://huggingface.co/datasets/allenai/wildguardmix)\n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Hybrid (Automated, Human)\n6. [BeaverTails-30k](https://huggingface.co/datasets/PKU-Alignment/BeaverTails)\n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Hybrid (Automated, Human)\n7. [XSTest-Responses](https://huggingface.co/datasets/allenai/xstest-response)\n**Data Collection Method:** Hybrid (Automated, Human)\n**Labeling Method:** Hybrid (Automated, Human)\n#### Properties:\n1. 1,984 prompt-response pair samples from the Aegis-AI-Content-Safety-Dataset-2.0 (test split) for evaluating on our *in-domain* safety risk taxonomy.\n2. 5,083 prompt onlysamples from the ToxicChat dataset (test split) for evaluating generalization of performance on an out-of-domain safety risk taxonomy.\n3. 1,680 prompt onlysamples from Open AI moderation dataset for evaluating generalization of performance on an out-of-domain safety risk taxonomy.\n4. 1,729 prompt only samples from the WildguardMix (test split) for evaluating generalization of performance on a novel out-of-domain safety risk taxonomy.\n5. 1,729 prompt-response pair samples from the WildguardMix (test split) for evaluating generalization of performance on a novel out-of-domain safety risk taxonomy.\n6. 3,021 prompt-response pair samples from the BeaverTails-30k dataset (test split) for evaluating generalization of performance on a novel out-of-domain safety risk taxonomy.\n7. 446 prompt-response pair samples from XSTest-Responses dataset for evaluating performance on adversarial refusal prompts.\n## Inference\n**Engine:** TensorRT-LLM available via [NVIDIA NIMs](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.1-nemoguard-8b-content-safety/) available on NGC. \u003cbr\u003e\n\n**Test Hardware:** Supported GPUs: A100, H100, L40S, A6000. \u003cbr\u003e\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with the terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nDue to the serious nature of this project, annotators were asked to join on a volunteer basis based on their skill level, availability, and willingness to expose themselves to potentially unsafe content. Before work on this project began, all participants were asked to sign an Adult Content Acknowledgement that coincides with the organization's existing AntiHarassment Policy and Code of Conduct. This was to ensure that all annotators be made aware of the nature of this work, as well as the resources available to them should it affect their mental well-being. Regular 1:1 meetings were held between the leads assigned to this project and each annotator to make sure they are still comfortable with the material and are capable of continuing on with this type of work.\nThroughout the six months time span of the Content Moderation Guardrails project, we averaged twelve annotators at any given time. Of these twelve, four annotators come from Engineering backgrounds specializing in data analysis and collection, gaming, and robotics. Eight annotators have a background in Creative Writing, with specialization in linguistics, research and development, and other creative arts such as photography and\nfilm. All annotators have been extensively trained in working with Large Language Models (LLM), as well as other variations of Generative AI such as image retrieval or evaluations of multi-turn conversations. All are capable of generating creative text-based output and categorization work. Each of these twelve annotators resides in the United States, all from various ethnic and religious backgrounds that allow for representation across race, age, and social status.\nThe process in which the Aegis-AI-Content-Safety-Dataset-2.0 creation abides by ethical data categorization work is based within the tooling of [Label Studio](http://label-studio.nvidia.com/), an open source data labeling tool\noften used for the organization's internal projects. This tooling technology allows for large sets of data to be analyzed by individual annotators without seeing the work of their peers. This is essential in preventing bias between annotators, as well as delivering prompts to each individual with variability so that no one annotator is completing similar tasks based on how the data was initially arranged. \nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety \u0026 Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"40:T8fb,"])</script><script>self.__next_f.push([1,"Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application \u0026 Domain: | AI Content Safety Risks Moderation\nModel Type: | Content Classification\nIntended User: | This model is intended for enterprise developers building large language models.\nOutput: | Text (Safe/UnSafe String); Unsafe Category ID if content is unsafe\nDescribe how the model works: | Classifies user prompt as safe or unsafe per taxonomy and policy. If unsafe, the model predicts the category ID of violation. \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | None of the Above\nVerified to have met prescribed quality standards: | Yes\nPerformance Metrics: | Area under Precision-Recall (PR) curve (AUPRC), F-1 Score, Jailbreak Resiliency\nPotential Known Risks: | Even though we have tested the jaillbreak resiliency of this model, the safety model since its a LLM, can still get jailbroken through adversarial prompts leading the model to predict an unsafe hidden inside an adversarial prompt as \"safe\". The model may also also flag a content as a false positive/false negative under a certain unsafe category. \nLicensing: | Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information: Llama 3.1 Community License Agreement. Built with Llama."])</script><script>self.__next_f.push([1,"41:Tdda,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description:\nQwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:\n* Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n* Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n* Long-context Support up to 128K tokens and can generate up to 8K tokens.\n* Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nThis model is ready for commercial/non-commercial use.\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [Qwen2.5-7B-Instruct Model Card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).\n\n## License/Terms of Use\nQwen/Qwen2.5-7B-Instruct is licensed under the [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE)\n\n## References:\n[Blog](https://qwenlm.github.io/blog/qwen2.5/), [Github](https://github.com/QwenLM/Qwen2.5), [Documentation](https://qwen.readthedocs.io/en/latest/), [Technical Report](https://arxiv.org/abs/2412.15115)\n\n## Model Architecture:\n**Architecture Type:** Transformer \u003cbr\u003e\n**Network Architecture:** Qwen2.5-7B-Instruct\n\n## Input:\n**Input Type(s):** Text \u003cbr\u003e\n**Input Format(s):** String \u003cbr\u003e\n**Input Parameters:** 1D\n\n## Output:\n**Output Type(s):** Text \u003cbr\u003e\n**Output Format:** String \u003cbr\u003e\n**Output Parameters:** 1D\n\n## Model Version(s):\nQwen2.5-7B-Instruct\n\n## Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n**Link:** Unknown \u003cbr\u003e\n**Data Collection Method by dataset:** Unknown \u003cbr\u003e\n**Labeling Method by dataset:** Unknown \u003cbr\u003e\n**Properties:** The size of the pre-training dataset is expanded from 7 trillion tokens used in Qwen2 to a maximum of 18 trillion tokens.\n\n## Testing Dataset:\n**Link:** Unknown \u003cbr\u003e\n**Data Collection Method by dataset:** Unknown \u003cbr\u003e\n**Labeling Method by dataset:** Unknown \u003cbr\u003e\n**Properties:** Unknown\n\n## Evaluation Dataset:\n**Link:** See evaluation section of the [Hugging Face Qwen2.5-7B-Instruct Model Card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct#evaluation--performance) \u003cbr\u003e\n**Data Collection Method by dataset:** Unknown \u003cbr\u003e\n**Labeling Method by dataset:** Unknown \u003cbr\u003e\n**Properties:** Unknown\n\n## Inference:\n**Engine:** TensorRT-LLM \u003cbr\u003e\n**Test Hardware:** NVIDIA L40S\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"42:T1273,"])</script><script>self.__next_f.push([1,"# Model Overview\n\n## Description:\n\nGenMol is a masked diffusion model\u003csup\u003e1\u003c/sup\u003e trained on molecular Sequential Attachment-based Fragment Embedding ([SAFE](https://arxiv.org/abs/2310.10773)) representations\u003csup\u003e2\u003c/sup\u003e for fragment-based molecule generation, which can serve as a generalist model for various drug discovery tasks, including De Novo generation, linker design, motif extension, scaffold decoration/morphing, hit generation, and lead optimization.\n\nThis model is ready for commercial use.\n\n## License/Terms of Use:\n\nThis NIM is licensed under [NVIDIA AI Foundation Models Community License Agreement](https://docs.nvidia.com/ai-foundation-models-community-license.pdf). By using this NIM, you accept the terms and conditions of this license. You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.\n\n\n## References:\n\n```\n@misc{sahoo2024simpleeffectivemaskeddiffusion,\n title={Simple and Effective Masked Diffusion Language Models}, \n author={Subham Sekhar Sahoo and Marianne Arriola and Yair Schiff and Aaron Gokaslan and Edgar Marroquin and Justin T Chiu and Alexander Rush and Volodymyr Kuleshov},\n year={2024},\n eprint={2406.07524},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2406.07524}, \n}\n```\n\n```\n@misc{noutahi2023gottasafenewframework,\n title={Gotta be SAFE: A New Framework for Molecular Design}, \n author={Emmanuel Noutahi and Cristian Gabellini and Michael Craig and Jonathan S. C Lim and Prudencio Tossou},\n year={2023},\n eprint={2310.10773},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2310.10773}, \n}\n```\n\n## Model Architecture:\n**Architecture Type:** Transformer \u003cbr\u003e\n**Network Architecture:** BERT \u003cbr\u003e\n\n\n## Input: \n**Input Type(s):** Text (Molecular Sequence), Number (Molecules to generate, SoftMax temperature scaling factor, randomness factor, diffusion step-size), Enumeration (Scoring method), Binary (Showing unique molecules only) \u003cbr\u003e\n**Input Format(s):** Text: String (Sequential Attachment-based Fragment Embedding (SAFE)); Number: Integer, FP32; Enumeration: String (QED, LogP); Binary: Boolean \u003cbr\u003e\n**Input Parameters:** 1D \u003cbr\u003e\n**Other Properties Related to Input:** Maximum input length is 512 tokens.\n\n\n## Output: \n**Output Type(s):** Text (List of molecule sequences), Number (List of scores)\u003cbr\u003e\n**Output Format:** Text: Array of string (Sequential Attachment-based Fragment Embedding (SAFE)); Number: Array of FP32 (Scores)\u003cbr\u003e\n**Output Parameters:** 2D \u003cbr\u003e\n**Other Properties Related to Output:** Maximum output length is 512 tokens. \u003cbr\u003e \n\n\n## Software Integration:\n**Runtime Engine(s):**\nPyTorch \u003e= 2.5.1 \u003cbr\u003e\n\n**Supported Hardware Microarchitecture Compatibility:** \u003cbr\u003e\nNVIDIA Ampere \u003cbr\u003e\nNVIDIA Ada Lovelace \u003cbr\u003e\nNVIDIA Hopper \u003cbr\u003e\nNVIDIA Grace Hopper \u003cbr\u003e\n\n**[Preferred/Supported] Operating System(s):** \u003cbr\u003e\nLinux \u003cbr\u003e\n\n## Model Version(s):\nGenMol v1.0 \u003cbr\u003e\n\n# Training \u0026 Evaluation Dataset:\n\n## Training and Testing Dataset:\n\n**Link:** SAFE-GPT [GitHub](https://github.com/datamol-io/safe), [HuggingFace](https://huggingface.co/datasets/datamol-io/safe-gpt), \u003cbr\u003e\n**Data Collection Method by dataset:** Automated \u003cbr\u003e\n**Labeling Method by dataset:** Automated \u003cbr\u003e\n**Properties:** 1.1B SAFE strings consist of various molecule types (drug-like compounds, peptides, multi-fragment molecules, polymers, reagents and non-small molecules). \u003cbr\u003e\n**Dataset License(s):** [CC-BY-4.0](https://github.com/datamol-io/safe/blob/main/DATA_LICENSE) \u003cbr\u003e\n\n\n## Evaluation Dataset:\n\n**Link:** SAFE-DRUGS [GitHub](https://github.com/datamol-io/safe), [HuggingFace](https://huggingface.co/datasets/datamol-io/safe-drugs) \u003cbr\u003e\n**Data Collection Method by dataset:** Not Applicable \u003cbr\u003e\n**Labeling Method by dataset:** Not Applicable \u003cbr\u003e\n**Properties:** SAFE-DRUGS consists of 26 known therapeutic drugs. \u003cbr\u003e\n**Dataset License(s):** [CC-BY-4.0](https://github.com/datamol-io/safe/blob/main/DATA_LICENSE) \u003cbr\u003e\n\n\n## Inference:\n**Engine:** PyTorch \u003cbr\u003e\n**Test Hardware:** A6000, A100, L40, L40S, H100\u003cbr\u003e\n\n# Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"43:T4b2,Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p \"$LOCAL_NIM_CACHE\"\ndocker run -it --rm \\\n --runtime=nvidia \\\n --gpus=all \\\n -e NVIDIA_VISIBLE_DEVICES=0 \\\n -e NGC_API_KEY=$NGC_API_KEY \\\n --shm-size=2G \\\n --ulimit memlock=-1 \\\n --ulimit stack=67108864 \\\n -p 8000:8000 \\\n nvcr.io/nim/nvidia/genmol:1.0.0\n```\n\nYou can now make a local API call using this curl command:\n```bash\naccept_header='Accept: application/json'\ncontent_type_header='Content-Type: application/json'\n\ndata='{\n \"smiles\": \"C124CN3C1.S3(=O)(=O)CC.C4C#N.[*{20-20}]\",\n \"num_molecules\": \"30\",\n \"temperature\": \"1\",\n \"noise\": \"0\",\n \"step_size\":\"1\",\n \"scoring\": \"QED\"\n}'\n\nresponse=$(curl --silent -i -w \"\n%{http_code}\" --request POST \\\n --url http://localhost:8000/generate \\\n --header \"$authorization_header\" \\\n --header \"$accept_header\" \\\n --header \"$content_type_header\" \\\n --data \"$data\"\n)\n\necho \"$response\"\n```\n\nFor more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/index.html#bionemo).\n44:T467,| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Was consent obtained for any personal data used? | Not Applicable |\n| PII used to create this model? | None |\n| How often is the dataset reviewed? | Before Every Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable |\n| If personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| I"])</script><script>self.__next_f.push([1,"s there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |45:T25f5,"])</script><script>self.__next_f.push([1,"## **Model Overview**\n\n### **Description**\n\nThe NVIDIA NeMo Retriever Llama3.2 embedding model is optimized for **multilingual and cross-lingual** text question-answering retrieval with **support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings)**. This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.\n\nIn addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently.\n\nAn embedding model is a crucial component of a text retrieval system, as it transforms textual information into dense vector representations. They are typically transformer encoders that process tokens of input text (for example: question, passage) to output an embedding.\n\nThis model is ready for commercial use.\n\nThe Llama 3.2 1b embedding model is a part of the NVIDIA NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research \u0026 development research assistants.\n\n### **Intended use**\n\nThe NeMo Retriever Llama3.2 embedding model is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, leveraging the latest dense retrieval technologies.\n\n### **License/Terms of use**\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### **Model Architecture**\n\n**Architecture Type:** Transformer\n**Network Architecture:** Fine-tuned Llama3.2 1b retriever\n\nThis NeMo Retriever embedding model is a transformer encoder - a fine-tuned version of Llama3.2 1b, with 16 layers and an embedding size of 2048, which is trained on public datasets. The AdamW optimizer is employed incorporating 100 warm up steps and 5e-6 learning rate with WarmupDecayLR scheduler. Embedding models for text retrieval are typically trained using a bi-encoder architecture. This involves encoding a pair of sentences (for example, query and chunked passages) independently using the embedding model. Contrastive learning is used to maximize the similarity between the query and the passage that contains the answer, while minimizing the similarity between the query and sampled negative passages not useful to answer the question.\n\n### **Input**\n\n**Input Type:** Text\n**Input Format:** List of strings\n**Input Parameter:** 1D\n**Other Properties Related to Input:** The model's maximum context length is 8192 tokens. Texts longer than maximum length must either be chunked or truncated.\n\n### **Output**\n\n**Output Type:** Floats\n**Output Format:** List of float arrays\n**Output:** Model outputs embedding vectors of maximum dimension 2048 for each text string (can be configured based on 384, 512, 768, 1024, or 2048).\n**Other Properties Related to Output:** N/A\n\n### **Software Integration**\n\n**Runtime Engine:** NeMo Retriever embedding NIM\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace\n**Supported Operating System(s):** Linux\n\n### **Model Version(s)**\n\nNVIDIA NeMo Retriever Llama 3.2 embedding\nShort Name: llama-3.2-nv-embedqa-1b-v2\n\n## **Training Dataset \u0026 Evaluation**\n\n### **Training Dataset**\n\nThe development of large-scale public open-QA datasets has enabled tremendous progress in powerful embedding models. However, one popular dataset named MS MARCO restricts ‌commercial licensing, limiting the use of these models in commercial settings. To address this, NVIDIA created its own training dataset blend based on public QA datasets, which each have a license for commercial applications.\n\n**Data Collection Method by dataset**: Automated, Unknown\n\n\n**Labeling Method by dataset**: Automated, Unknown\n\n\n**Properties:** Semi-supervised pre-training on 12M samples from public datasets and fine-tuning on 1M samples from public datasets.\n\n\n### **Evaluation Results**\n\nProperties: We evaluated the NeMo Rtriever embdding model in comparison to literature open \u0026 commercial retriever models on academic benchmarks for question-answering - [NQ](https://huggingface.co/datasets/BeIR/nq), [HotpotQA](https://huggingface.co/datasets/hotpot_qa) and [FiQA (Finance Q\\\u0026A)](https://huggingface.co/datasets/BeIR/fiqa) from BeIR benchmark and TechQA dataset. Note that the model was evaluated offline on A100 GPUs using the model's PyTorch checkpoint. In this benchmark, the metric used was Recall@5.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on NQ, HotpotQA, FiQA, TechQA dataset |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 68.60% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 64.48% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 68.97% |\n| nv-embedqa-mistral-7b-v2 | 72.97% |\n| nv-embedqa-mistral-7B-v1 | 64.93% |\n| nv-embedqa-e5-v5 | 62.07% |\n| nv-embedqa-e5-v4 | 57.65% |\n| e5-large-unsupervised | 48.03% |\n| BM25 | 44.67% |\n\nWe evaluated the multilingual capabilities on the academic benchmark [MIRACL](https://github.com/project-miracl/miracl) across 15 languages and translated the English and Spanish version of MIRACL into additional 11 languages. The reported scores are based on an internal version of MIRACL by selecting hard negatives for each query to reduce the corpus size.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on multilingual |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 60.75% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 58.62% |\n| llama-3.2-nv-embedqa-1b-v1 | 60.07% |\n| nv-embedqa-mistral-7b-v2 | 50.42% |\n| BM25 | 26.51% |\n\nWe evaluated the cross-lingual capabilities on the academic benchmark [MLQA](https://github.com/facebookresearch/MLQA/) based on 7 languages (Arabic, Chinese, English, German, Hindi, Spanish, Vietnamese). We consider only evaluation datasets when the query and documents are in different languages. We calculate the average Recall@5 across the 42 different language pairs.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on MLQA dataset with different languages |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 79.86% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 71.61% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 78.77% |\n| nv-embedqa-mistral-7b-v2 | 68.38% |\n| BM25 | 13.01% |\n\nWe evaluated the support of long documents on the academic benchmark [Multilingual Long-Document Retrieval (MLDR)](https://huggingface.co/datasets/Shitao/MLDR) built on Wikipedia and mC4, covering 12 typologically diverse languages. The English version has a median length of 2399 tokens and 90th percentile of 7483 tokens using the llama 3.2 tokenizer. The MLDR dataset is based on synthetic generated questions with a LLM, which has the tendency to create questions with similar keywords than the positive document, but might not be representative for real user queries. This characteristic of the dataset benefits sparse embeddings like BM25.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on MLDR |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 59.55% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 54.77% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 60.49% |\n| nv-embedqa-mistral-7b-v2 | 43.24% |\n| BM25 | 71.39% |\n\n**Data Collection Method by dataset**: Unknown\n\n**Labeling Method by dataset:** Unknown\n\n**Properties:** The evaluation datasets are based on [MTEB/BEIR](https://github.com/beir-cellar/beir), TextQA, TechQA, [MIRACL](https://github.com/project-miracl/miracl), [MLQA](https://github.com/facebookresearch/MLQA), and [MLDR](https://huggingface.co/datasets/Shitao/MLDR). The size ranges between 10,000s up to 5M depending on the dataset.\n\n**Inference**\n**Engine:** TensorRT\n**Test Hardware:** H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, and A10G\n\n## **Ethical Considerations**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ tab for the Explainability, Bias, Safety \u0026 Security, and Privacy subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"46:T5cd,| Field | Response |\n| ----- | ----- |\n| Intended Application \u0026 Domain: | Passage and query embedding for question and answer retrieval |\n| Model Type: | Transformer encoder |\n| Intended User: | Generative AI creators working with conversational AI models - users who want to build a multilingual question and answer application over a large text corpus, leveraging the latest dense retrieval technologies. |\n| Output: | Array of float numbers (Dense Vector Representation for the input text) |\n| Describe how the model works: | Model transforms the tokenized input text into a dense vector representation. |\n| Performance Metrics: | Accuracy, Throughput, and Latency |\n| Potential Known Risks: | This model does not always guarantee to retrieve the correct passage(s) for a given query. |\n| Licensing \u0026 Terms of Use: | The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. |\n| Technical Limitations | The model’s max sequence length is 8192. Therefore, the longer text inputs should be truncated. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | N/A |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |47:Te384,"])</script><script>self.__next_f.push([1,"{\n \"object\": \"list\",\n \"data\": [\n {\n \"object\": \"embedding\",\n \"embedding\": [\n -0.021759033203125,\n 0.028717041015625,\n -0.00675201416015625,\n 0.0290374755859375,\n 0.03314208984375,\n -0.00557708740234375,\n -0.0633544921875,\n -0.0167694091796875,\n -0.0277252197265625,\n -0.00653839111328125,\n -0.0016307830810546875,\n -0.004207611083984375,\n -0.0019245147705078125,\n -0.0130462646484375,\n -0.0254669189453125,\n 0.021575927734375,\n 0.0161590576171875,\n 0.018585205078125,\n 0.007266998291015625,\n 0.00820159912109375,\n -0.03369140625,\n -0.02069091796875,\n -0.023834228515625,\n 0.040008544921875,\n -0.0447998046875,\n -0.0032749176025390625,\n -0.04034423828125,\n 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to data subjects? | Not Applicable |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |4a:T26cd,"])</script><script>self.__next_f.push([1,"## **Model Overview**\n\n### **Description**\n\nThe NVIDIA NeMo Retriever Llama3.2 reranking model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**. This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.\n\n\nThe reranking model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It’s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models.\n\n\nThis model is ready for commercial use.\n\n\nThe Llama 3.2 1B reranking model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, juman resource help assistants, and research \u0026 development research assistants.\n\n\n### **License/Terms of use**\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### **Intended use**\n\nThe NeMo Retriever Llama 3.2 reranking model is most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question.\n\n### **Model Architecture: Llama-3.2 1B Ranker**\n\n**Architecture Type:** Transformer\n**Network Architecture:** Fine-tuned meta-llama/Llama-3.2-1B\n\nThe NeMo Retriever Llama 3.2 reranking model is a transformer encoder fine-tuned for contrastive learning. We employ bi-directional attention when fine-tuning for higher accuracy. The last embedding output by the decoder model is used with a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task.\n\nRanking models for text ranking are typically trained as a cross-encoder for sentence classification. This involves predicting the relevancy of a sentence pair (for example, question and chunked passages). The CrossEntropy loss is used to maximize the likelihood of passages containing information to answer the question and minimize the likelihood for (negative) passages that do not contain information to answer the question.\n\nWe train the model on public datasets described in the Dataset and Training section.\n\n### **Input**\n\n**Input Type:** Pair of Texts\n**Input Format:** List of text pairs\n**Input Parameters:** 1D\n**Other Properties Related to Input:** The model was trained on question and answering over text documents from multiple languages. It was evaluated to work successfully with up to a sequence length of 8192 tokens. Longer texts are recommended to be either chunked or truncated.\n\n**Output**\n**Output Type:** Floats\n**Output Format:** List of floats\n**Output Parameters:** 1D\n**Other Properties Related to Output:** Each the probability score (or raw logits). Users can decide to implement a Sigmoid activation function applied to the logits in their usage of the model.\n\n### **Software Integration**\n\n**Runtime:** NeMo Retriever Llama 3.2 reranking NIM\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace\n**Supported Operating System(s):** Linux\n\n### **Model Version(s)**\n\nNVIDIA NeMo Retriever Llama 3.2 reranking\nShort Name: llama-3.2-nv-rerankqa-1b-v2\n\n## **Training Dataset \u0026 Evaluation**\n\n### **Training Dataset**\n\nThe development of large-scale public open-QA datasets has enabled tremendous progress in powerful embedding models. However, one popular dataset named [MSMARCO](https://microsoft.github.io/msmarco/) restricts ‌commercial licensing, limiting the use of these models in commercial settings. To address this, NVIDIA created its own training dataset blend based on public QA datasets, which each have a license for commercial applications.\n\n**Data Collection Method by dataset**: Automated, Unknown\n\n**Labeling Method by dataset:** Automated, Unknown\n\n**Properties:** This model was trained on 800k samples from public datasets.\n\n### **Evaluation Results**\n\nWe evaluate the pipelines on a set of evaluation benchmarks. We applied the ranking model to the candidates retrieved from a retrieval embedding model.\n\nOverall, the pipeline llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 provides high BEIR+TechQA accuracy with multilingual and crosslingual support. The llama-3.2-nv-rerankqa-1B-v2 ranking model is 3.5x smaller than the nv-rerankqa-mistral-4b-v3 model.\n\nWe evaluated the NVIDIA Retrieval QA Embedding Model in comparison to literature open \u0026 commercial retriever models on academic benchmarks for question-answering \\- [NQ](https://huggingface.co/datasets/BeIR/nq), [HotpotQA](https://huggingface.co/datasets/hotpot_qa) and [FiQA (Finance Q\\\u0026A)](https://huggingface.co/datasets/BeIR/fiqa) from BeIR benchmark and TechQA dataset. In this benchmark, the metric used was Recall@5. As described, we need to apply the ranking model on the output of an embedding model.\n\n| Open \u0026 Commercial Reranker Models | Average Recall@5 on NQ, HotpotQA, FiQA, TechQA dataset |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 73.64% |\n| llama-3.2-nv-embedqa-1b-v2 | 68.60% |\n| nv-embedqa-e5-v5 \\+ nv-rerankQA-mistral-4b-v3 | 75.45% |\n| nv-embedqa-e5-v5 | 62.07% |\n| nv-embedqa-e5-v4 | 57.65% |\n| e5-large\\_unsupervised | 48.03% |\n| BM25 | 44.67% |\n\nWe evaluated the model’s multilingual capabilities on the [MIRACL](https://github.com/project-miracl/miracl) academic benchmark \\- a multilingual retrieval dataset, across 15 languages, and on an additional 11 languages that were translated from the English and Spanish versions of MIRACL. The reported scores are based on a custom subsampled version by selecting hard negatives for each query to reduce the corpus size.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on MIRACL multilingual datasets |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 65.80% |\n| llama-3.2-nv-embedqa-1b-v2 | 60.75% |\n| nv-embedqa-mistral-7b-v2 | 50.42% |\n| BM25 | 26.51% |\n\nWe evaluated the cross-lingual capabilities on the academic benchmark [MLQA](https://github.com/facebookresearch/MLQA/) based on 7 languages (Arabic, Chinese, English, German, Hindi, Spanish, Vietnamese). We consider only evaluation datasets when the query and documents are in different languages. We calculate the average Recall@5 across the 42 different language pairs.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on MLQA dataset with different languages |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 86.83% |\n| llama-3.2-nv-embedqa-1b-v2 | 79.86% |\n| nv-embedqa-mistral-7b-v2 | 68.38% |\n| BM25 | 13.01% |\n\nWe evaluated the support of long documents on the academic benchmark [Multilingual Long-Document Retrieval (MLDR)](https://huggingface.co/datasets/Shitao/MLDR) built on Wikipedia and mC4, covering 12 typologically diverse languages . The English version has a median length of 2399 tokens and 90th percentile of 7483 tokens using the llama 3.2 tokenizer.\n\n| Open \u0026 Commercial Retrieval Models | Average Recall@5 on MLDR |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 70.69% |\n| llama-3.2-nv-embedqa-1b-v2 | 59.55% |\n| nv-embedqa-mistral-7b-v2 | 43.24% |\n| BM25 | 71.39% |\n\n**Data Collection Method by dataset**:\nUnknown\n\n**Labeling Method by dataset:**\nUnknown\n\n**Properties**\nThe evaluation datasets are based on three [MTEB/BEIR](https://github.com/beir-cellar/beir) TextQA datasets, the TechQA dataset, and MIRACL multilingual retrieval datasets, which are all public datasets. The sizes range between 10,000s up to 5M depending on the dataset.\n\n**Inference**\n**Engine:** TensorRT\n**Test Hardware:** H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, and A10G\n\n## **Ethical Considerations**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ tab for the Explainability, Bias, Safety \u0026 Security, and Privacy subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)."])</script><script>self.__next_f.push([1,"4b:T559,| Field | Response |\n| ----- | ----- |\n| Intended Application \u0026 Domain: | Passage ranking for question and answer retrieval. |\n| Model Type: | Transformer encoder |\n| Intended User: | Generative AI creators working with conversational AI models - most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question. |\n| Output: | List of Floats (Score/Logit indicating if a passage relevant to a question) |\n| Describe how the model works: | Model provides a score about the likelihood the passage contains the information to answer the question. |\n| Verified to have met prescribed quality standards: | Yes |\n| Performance Metrics: | Accuracy, Throughput, and Latency |\n| Potential Known Risks: | This model does not always guarantee to provide a meaningful ranking of passage(s) for a given question. |\n| Licensing: | The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. |\n| Technical Limitations | The model’s max sequence length is 8192. Therefore, the longer text inputs should be truncated. |4c:T430,{\n \"model\": \"nvidia/llama-3.2-nv-rerankqa-1b-v2\",\n \"query\": {\n \"text\": \"What is the GPU memory bandwidth of H100 SXM?\"\n },\n \"passages\": [\n {\n \"text\": \"The Hopper GPU is paired with the Grace CPU using NVIDIA's ultra-fast chip-to-chip interconnect, delivering 900GB/s of bandwidth, 7X faster than PCIe Gen5. This innovative design will deliver up to 30X higher aggregate system memory bandwidth to the GPU compared to today's fastest servers and up to 10X higher performance for applications running terabytes of data.\"\n },\n {\n \"text\": \"A100 provides up to 20X higher performance over the prior generation and can be partitioned into seve"])</script><script>self.__next_f.push([1,"n GPU instances to dynamically adjust to shifting demands. The A100 80GB debuts the world's fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.\"\n },\n {\n \"text\": \"Accelerated servers with H100 deliver the compute power—along with 3 terabytes per second (TB/s) of memory bandwidth per GPU and scalability with NVLink and NVSwitch™.\"\n }\n ]\n}\n4d:T79c,{\n \"model\": \"nvidia/llama-3.2-nv-rerankqa-1b-v2\",\n \"query\": {\n \"text\": \"What are the benefits of NVIDIA Morpheus?\"\n },\n \"passages\": [\n {\n \"text\": \"Morpheus integrates tools to make it easier to build cybersecurity solutions. Built on the NVIDIA AI platform, including RAPIDS™ libraries, deep learning frameworks, and NVIDIA Triton™ Inference Server, Morpheus simplifies the analysis of logs and telemetry to help detect and mitigate security threats.\"\n },\n {\n \"text\": \"Morpheus can receive rich, real-time network telemetry from every NVIDIA® BlueField® DPU-accelerated server and NVIDIA DOCA™-based application, including Telemetry Flow Inspector and App Shield, in the data center without impacting performance. Integrating the framework into a third-party cybersecurity offering brings the world\u0026aposs best AI computing to communication networks.\"\n },\n {\n \"text\": \"Morpheus is a GPU-accelerated SDK enabling, for the first time, the ability to inspect all network traffic in real time, flag anomalies, and provide insights on these anomalies so that threats can be addressed quickly.\"\n },\n {\n \"text\": \"Integrate Triton Inference Server into DevOps and MLOps solutions such as Kubernetes for scaling and Prometheus for monitoring. It can also be used in all major cloud and on-premises AI and MLOps platforms.\"\n },\n {\n \"text\": \"NVIDIA AI Enterprise, including NVIDIA Triton Inference Server, is a secure, production-ready AI software platform designed to accelerate time to value with support, security, and API stability.\"\n },\n {\n \"text\": \"Morpheus ("])</script><script>self.__next_f.push([1,"'Fashioner', derived from the Ancient Greek: μορφή meaning 'form, shape') is a god associated with sleep and dreams. In Ovid's Metamorphoses he is the son of Somnus and appears in dreams in human form. From the Middle Ages, the name began to stand more generally for the god of dreams, or of sleep.\"\n }\n ]\n}\n4e:T6e6,Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.\n```bash\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p \"$LOCAL_NIM_CACHE\"\ndocker run -it --rm \\\n --gpus all \\\n --shm-size=16GB \\\n -e NGC_API_KEY \\\n -v \"$LOCAL_NIM_CACHE:/opt/nim/.cache\" \\\n -u $(id -u) \\\n -p 8000:8000 \\\n nvcr.io/nim/nvidia/llama-3.2-nv-rerankqa-1b-v2:latest\n```\n\nYou can now make a local API call using this curl command:\n```bash\ncurl -X \"POST\" \\\n \"http://localhost:8000/v1/ranking\" \\\n -H 'accept: application/json' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"nvidia/llama-3.2-nv-rerankqa-1b-v2\",\n \"query\": {\"text\": \"which way did the traveler go?\"},\n \"passages\": [\n {\"text\": \"two roads diverged in a yellow wood, and sorry i could not travel both and be one traveler, long i stood and looked down one as far as i could to where it bent in the undergrowth;\"},\n {\"text\": \"then took the other, as just as fair, and having perhaps the better claim because it was grassy and wanted wear, though as for that the passing there had worn them really about the same,\"},\n {\"text\": \"and both that morning equally lay in leaves no step had trodden black. oh, i marked the first for another day! yet knowing how way leads on to way i doubted if i should ever come back.\"},\n {\"text\": \"i shall be telling this with a sigh somewhere ages and ages hense: two roads diverged in a wood, and i, i took the one less traveled by, and that has made all the difference.\"}\n ],\n \"truncate\": \"END\"\n}'\n```\n\nFor more details on getting started with this NIM, visit the [NVIDIA NIM 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License\u003c/a\u003e.\\n\",\"requiresLogin\":false,\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/arc-evo2-40b\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: This trial service is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. \\n The NIM container is governed by the \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/\\\"\u003eNVIDIA Software License Agreement\u003c/a\u003e and \\n \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/\\\"\u003eProduct-Specific Terms\u003c/a\u003e for AI Products. Use of this model is governed by the \\n \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/\\\"\u003eNVIDIA Open Model License Agreement\u003c/a\u003e. \u003cb\u003eADDITIONAL INFORMATION\u003c/b\u003e: \u003ca href=\\\"https://github.com/arcinstitute/evo2\\\"\u003eApache 2.0 License\u003c/a\u003e.\\n\"},\"artifactName\":\"evo2-40b\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"95467bcd-0fce-4403-be8c-7c72f1fe6770\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"ASR\",\"AST\",\"Multilingual\",\"NVIDIA NIM\",\"NVIDIA Riva\",\"OpenAI\",\"Whisper\",\"batch\",\"Speech-to-Text\"],\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/whisper-large-v3.jpg\",\"shortDescription\":\"Robust Speech Recognition via Large-Scale Weak Supervision.\",\"isReadOnly\":true,\"description\":\"$2b\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-02-18T19:47:35.847Z\",\"publisher\":\"openai\",\"displayName\":\"whisper-large-v3\",\"name\":\"whisper-large-v3\",\"updatedDate\":\"2025-02-20T02:30:57.333Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-02-20T02:30:57.670Z\",\"nvcfFunctionId\":\"b702f636-f60c-4a3d-a6f4-f3568c13bd7d\",\"createdDate\":\"2025-02-18T19:47:36.161Z\",\"attributes\":{\"dockerRun\":\"$2c\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"usage\":\"$2d\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.nvidia.com/nim/riva/asr/latest/protos.html\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e.\\n\",\"cta\":{\"text\":\"Run Anywhere - Notify Me\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/riva-asr\"}},\"artifactName\":\"whisper-large-v3\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"f9e5bfad-78c8-4989-82b5-92c2d160aa9e\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"ASR\",\"AST\",\"Multilingual\",\"NVIDIA NIM\",\"NVIDIA Riva\",\"Spanish\",\"Streaming\",\"batch\",\"Speech-to-Text\"],\"bias\":\"$2e\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/canary-1b-asr.jpg\",\"shortDescription\":\"Multi-lingual model supporting speech-to-text recognition and translation.\",\"safetyAndSecurity\":\"Field | Response\\n:---------------------------------------------------|:----------------------------------\\nModel Application(s): | Automatic Speech Recognition \u0026 Translation\\nDescribe the life-critical impacts (if present). | Not Applicable\\nUse Case Restriction(s): | Abide by https://developer.nvidia.com/riva/ga/license\\nDescribe access restrictions (if any): | The Principle of Least Privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training and dataset license constraints adhered to.\",\"privacy\":\"$2f\",\"isReadOnly\":true,\"description\":\"$30\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-02-18T18:49:02.046Z\",\"publisher\":\"nvidia\",\"displayName\":\"canary-1b-asr\",\"name\":\"canary-1b-asr\",\"explainability\":\"$31\",\"updatedDate\":\"2025-02-20T02:30:01.641Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-02-20T02:30:02.314Z\",\"nvcfFunctionId\":\"ee8dc628-76de-4acc-8595-1836e7e857bd\",\"createdDate\":\"2025-02-18T18:49:02.357Z\",\"attributes\":{\"dockerRun\":\"$32\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"usage\":\"$33\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.nvidia.com/nim/riva/asr/latest/protos.html\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e.\\n\",\"cta\":{\"text\":\"Run Anywhere - Notify Me\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/riva-asr\"}},\"artifactName\":\"canary-1b-asr\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"64ed53bb-6f59-4086-811d-7893e44fc4d7\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"ASR\",\"AST\",\"Fast\",\"Multilingual\",\"NVIDIA NIM\",\"NVIDIA Riva\",\"batch\",\"Speech-to-Text\"],\"bias\":\"$34\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/canary-0-6b-turbo-asr.jpg\",\"shortDescription\":\"Multi-lingual model supporting speech-to-text recognition and translation.\",\"safetyAndSecurity\":\"Field | Response\\n:---------------------------------------------------|:----------------------------------\\nModel Application(s): | Automatic Speech Recognition \u0026 Translation\\nDescribe the life-critical impacts (if present). | Not Applicable\\nUse Case Restriction(s): | Abide by https://developer.nvidia.com/riva/ga/license\\nDescribe access restrictions (if any): | The Principle of Least Privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training and dataset license constraints adhered to.\",\"privacy\":\"$35\",\"isReadOnly\":true,\"description\":\"$36\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-02-18T18:30:42.177Z\",\"publisher\":\"nvidia\",\"displayName\":\"canary-0.6b-turbo-asr\",\"name\":\"canary-0-6b-turbo-asr\",\"explainability\":\"$37\",\"updatedDate\":\"2025-02-20T02:31:50.575Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-02-20T02:31:51.081Z\",\"nvcfFunctionId\":\"c367545c-964a-42b1-b16f-40c262ae3646\",\"createdDate\":\"2025-02-18T18:30:42.476Z\",\"attributes\":{\"dockerRun\":\"$38\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"usage\":\"$39\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.nvidia.com/nim/riva/asr/latest/protos.html\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e.\\n\",\"cta\":{\"text\":\"Run Anywhere - Notify Me\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/riva-asr\"}},\"artifactName\":\"canary-0-6b-turbo-asr\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"b0feecc2-aa58-423b-a213-9dcb1b8e70a1\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"Content Safety\",\"Dialogue Safety\",\"Guard Model\",\"LLM safety\"],\"bias\":\"Field | Response\\n:---------------------------------------------------------------------------------------------------|:---------------\\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Not Applicable\\nMeasures taken to mitigate against unwanted bias: | None\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_1-nemoguard-8b-topic-control.jpg\",\"shortDescription\":\"Topic control model to keep conversations focused on approved topics, avoiding inappropriate content.\",\"safetyAndSecurity\":\"Field | Response\\n:---------------------------------------------------|:----------------------------------\\nModel Application(s): | Dialogue agents for topic / dialogue moderation\\nDescribe the life critical impact (if present). | Not Applicable\\nUse Case Restrictions: | Should not be used for any use case other than text-based topic and dialogue moderation in task oriented dialogue agents.\\nModel and dataset restrictions: | Abide by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\",\"privacy\":\"$3a\",\"isReadOnly\":true,\"description\":\"$3b\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-01-16T13:44:55.672Z\",\"publisher\":\"nvidia\",\"displayName\":\"llama-3.1-nemoguard-8b-topic-control\",\"name\":\"llama-3_1-nemoguard-8b-topic-control\",\"explainability\":\"$3c\",\"updatedDate\":\"2025-01-25T17:41:37.100Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for nvidia/llama-3.1-nemoguard-8b-topic-control\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_1-nemoguard-8b-topic-control for more details.\",\"version\":\"1.0.0\",\"termsOfService\":\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\",\"contact\":{\"name\":\"NVIDIA Enterprise Support\",\"url\":\"https://www.nvidia.com/en-us/support/enterprise/\"},\"license\":{\"name\":\"Llama 3 License\",\"url\":\"https://llama.meta.com/llama3/license/\"}},\"servers\":[{\"url\":\"https://integrate.api.nvidia.com/v1/\"}],\"paths\":{\"/chat/completions\":{\"post\":{\"operationId\":\"create_chat_completion_v1_chat_completions_post\",\"tags\":[\"Chat\"],\"summary\":\"Creates a model response for the given chat conversation.\",\"description\":\"Given a list of messages comprising a conversation, the model will return a response. Compatible with OpenAI. See https://platform.openai.com/docs/api-reference/chat/create\",\"requestBody\":{\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletionRequest\"}}},\"required\":true},\"responses\":{\"200\":{\"description\":\"Successful Response\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletionResponse\"}}}},\"402\":{\"description\":\"Payment Required\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/PaymentRequiredError\"}}}},\"422\":{\"description\":\"Validation Error\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/HTTPValidationError\"}}}}},\"x-nvai-meta\":{\"name\":\"Create chat completion\",\"returns\":\"Returns a [chat completion](/docs/api-reference/chat/object) object.\\n\",\"path\":\"create\",\"examples\":[{\"name\":\"Example 1\",\"requestJson\":\"$3d\",\"responseJson\":\"{\\n \\\"id\\\": \\\"chat-5ca3dcbc798a4d9fa0edc8879806dac5\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1732722977,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-topic-control\\\",\\n \\\"choices\\\": [\\n {\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"off-topic \\\"\\n },\\n \\\"logprobs\\\": null,\\n \\\"finish_reason\\\": \\\"stop\\\",\\n \\\"stop_reason\\\": null\\n }\\n ],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 281,\\n \\\"total_tokens\\\": 284,\\n \\\"completion_tokens\\\": 3\\n },\\n \\\"prompt_logprobs\\\": null\\n}\\n\"}],\"templates\":[{\"title\":\"No Streaming\",\"requestEjs\":{\"python\":\"from openai import OpenAI\\n\\nclient = OpenAI(\\n base_url = \\\"https://integrate.api.nvidia.com/v1\\\",\\n api_key = \\\"$NVIDIA_API_KEY\\\"\\n)\\n\\ncompletion = client.chat.completions.create(\\n model=\\\"\u003c%- request.model %\u003e\\\",\\n messages=\u003c%- JSON.stringify(request.messages) %\u003e,\\n temperature=\u003c%- request.temperature %\u003e,\\n top_p=\u003c%- request.top_p %\u003e,\\n max_tokens=\u003c%- request.max_tokens %\u003e\\n)\\nprint(completion.choices[0].message)\\n\",\"node.js\":\"import OpenAI from 'openai';\\n\\nconst openai = new OpenAI({\\n apiKey: '$NVIDIA_API_KEY',\\n baseURL: 'https://integrate.api.nvidia.com/v1',\\n})\\n\\nasync function main() {\\n const completion = await openai.chat.completions.create({\\n model: \\\"\u003c%- request.model %\u003e\\\",\\n messages: \u003c%- JSON.stringify(request.messages) %\u003e,\\n temperature: \u003c%- request.temperature %\u003e,\\n top_p: \u003c%- request.top_p %\u003e,\\n max_tokens: \u003c%- request.max_tokens %\u003e,\\n })\\n process.stdout.write(completion.choices[0]?.message?.content);\\n}\\n\\nmain();\",\"curl\":\"curl https://integrate.api.nvidia.com/v1/chat/completions \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -H \\\"Authorization: Bearer $NVIDIA_API_KEY\\\" \\\\\\n -d '{\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-topic-control\\\",\\n \\\"messages\\\": \u003c%- JSON.stringify(request.messages).replaceAll(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") %\u003e,\\n \\\"temperature\\\": \u003c%- request.temperature %\u003e, \\n \\\"top_p\\\": \u003c%- request.top_p %\u003e,\\n \\\"max_tokens\\\": \u003c%- request.max_tokens %\u003e \\n }'\\n\"},\"response\":\"{\\n \\\"id\\\": \\\"chat-5ca3dcbc798a4d9fa0edc8879806dac5\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1732722977,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-topic-control\\\",\\n \\\"choices\\\": [\\n {\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"off-topic \\\"\\n },\\n \\\"logprobs\\\": null,\\n \\\"finish_reason\\\": \\\"stop\\\",\\n \\\"stop_reason\\\": null\\n }\\n ],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 281,\\n \\\"total_tokens\\\": 284,\\n \\\"completion_tokens\\\": 3\\n },\\n \\\"prompt_logprobs\\\": null\\n}\\n\"}]}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"ChatCompletionRequest\":{\"properties\":{\"model\":{\"type\":\"string\",\"title\":\"Model\",\"default\":\"nvidia/llama-3.1-nemoguard-8b-topic-control\"},\"max_tokens\":{\"type\":\"integer\",\"minimum\":1,\"title\":\"Max Tokens\",\"description\":\"The maximum number of tokens to generate in any given call. Note that the model is not aware of this value, and generation will simply stop at the number of tokens specified.\",\"default\":1024},\"temperature\":{\"type\":\"number\",\"maximum\":2,\"minimum\":0,\"title\":\"Temperature\",\"description\":\"The sampling temperature to use for text generation. The higher the temperature value is, the less deterministic the output text will be. It is not recommended to modify both temperature and top_p in the same call.\",\"default\":0.5},\"top_p\":{\"type\":\"number\",\"maximum\":1,\"exclusiveMinimum\":0,\"title\":\"Top P\",\"description\":\"The top-p sampling mass used for text generation. The top-p value determines the probability mass that is sampled at sampling time. For example, if top_p = 0.2, only the most likely tokens (summing to 0.2 cumulative probability) will be sampled. It is not recommended to modify both temperature and top_p in the same call.\",\"default\":1},\"stop\":{\"anyOf\":[{\"items\":{\"type\":\"string\"},\"type\":\"array\"},{\"type\":\"string\"},{\"type\":\"null\"}],\"title\":\"Stop\",\"description\":\"A string or a list of strings where the API will stop generating further tokens. The returned text will not contain the stop sequence.\",\"examples\":[null]},\"frequency_penalty\":{\"type\":\"number\",\"maximum\":2,\"minimum\":-2,\"default\":0,\"title\":\"Frequency Penalty\",\"description\":\"Indicates how much to penalize new tokens based on their existing frequency in the text so far, decreasing model likelihood to repeat the same line verbatim.\"},\"presence_penalty\":{\"type\":\"number\",\"maximum\":2,\"minimum\":-2,\"default\":0,\"title\":\"Presence Penalty\",\"description\":\"Positive values penalize new tokens based on whether they appear in the text so far, increasing model likelihood to talk about new topics.\"},\"messages\":{\"items\":{\"$ref\":\"#/components/schemas/ChatMessage\"},\"type\":\"array\",\"title\":\"Messages\",\"description\":\"A list of messages comprising the conversation so far.\"}},\"additionalProperties\":false,\"type\":\"object\",\"required\":[\"messages\"],\"title\":\"ChatCompletionRequest\",\"description\":\"OpenAI ChatCompletionRequest\"},\"ChatCompletionResponse\":{\"properties\":{\"id\":{\"type\":\"string\",\"title\":\"Id\",\"description\":\"A unique identifier for the completion.\"},\"object\":{\"type\":\"string\",\"title\":\"Object\",\"default\":\"chat.completion\"},\"created\":{\"type\":\"integer\",\"title\":\"Created\"},\"model\":{\"type\":\"string\",\"title\":\"Model\",\"example\":\"nvidia/llama-3.1-nemoguard-8b-topic-control\"},\"choices\":{\"items\":{\"$ref\":\"#/components/schemas/ChatCompletionResponseChoice\"},\"type\":\"array\",\"title\":\"Choices\",\"description\":\"The list of completion choices the model generated for the input prompt.\"},\"usage\":{\"$ref\":\"#/components/schemas/UsageInfo\",\"description\":\"Usage statistics for the completion request.\"}},\"type\":\"object\",\"required\":[\"model\",\"choices\",\"usage\"],\"title\":\"ChatCompletionResponse\"},\"ChatCompletionResponseChoice\":{\"properties\":{\"index\":{\"type\":\"integer\",\"title\":\"Index\",\"description\":\"The index of the choice in the list of choices (always 0).\"},\"message\":{\"$ref\":\"#/components/schemas/ChatMessage\",\"description\":\"A chat completion message generated by the model.\"},\"finish_reason\":{\"anyOf\":[{\"type\":\"string\",\"enum\":[\"stop\",\"length\"]},{\"type\":\"null\"}],\"title\":\"Finish Reason\",\"description\":\"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached. Will be `null` if the model has not finished\"}},\"type\":\"object\",\"required\":[\"index\",\"message\"],\"title\":\"ChatCompletionResponseChoice\"},\"ChatMessage\":{\"properties\":{\"role\":{\"type\":\"string\",\"title\":\"Role\",\"example\":\"user\",\"description\":\"The role of the message author.\"},\"content\":{\"type\":\"string\",\"title\":\"Content\",\"example\":\"Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`.\",\"description\":\"The contents of the message.\"}},\"type\":\"object\",\"required\":[\"role\",\"content\"],\"title\":\"ChatMessage\"},\"HTTPValidationError\":{\"properties\":{\"detail\":{\"items\":{\"$ref\":\"#/components/schemas/ValidationError\"},\"type\":\"array\",\"title\":\"Detail\",\"description\":\"Detailed information about the error.\"}},\"type\":\"object\",\"title\":\"HTTPValidationError\"},\"PaymentRequiredError\":{\"properties\":{\"detail\":{\"type\":\"string\",\"description\":\"Contains specific information related to the error and why it occurred.\",\"example\":\"You have reached your limit of credits.\"}},\"type\":\"object\",\"title\":\"PaymentRequiredError\"},\"UsageInfo\":{\"properties\":{\"prompt_tokens\":{\"type\":\"integer\",\"title\":\"Prompt Tokens\",\"description\":\"Number of tokens in the prompt.\",\"default\":0},\"total_tokens\":{\"type\":\"integer\",\"title\":\"Total Tokens\",\"description\":\"Total number of tokens used in the request (prompt + completion).\",\"default\":0},\"completion_tokens\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"title\":\"Completion Tokens\",\"description\":\"Number of tokens in the generated completion.\",\"default\":0}},\"type\":\"object\",\"title\":\"UsageInfo\"},\"ValidationError\":{\"properties\":{\"loc\":{\"items\":{\"anyOf\":[{\"type\":\"string\"},{\"type\":\"integer\"}]},\"type\":\"array\",\"title\":\"Location\"},\"msg\":{\"type\":\"string\",\"title\":\"Message\",\"description\":\"The error message.\"},\"type\":{\"type\":\"string\",\"title\":\"Error Type\",\"description\":\"Error type\"}},\"type\":\"object\",\"required\":[\"loc\",\"msg\",\"type\"],\"title\":\"ValidationError\"}}}},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-01-25T17:41:37.748Z\",\"nvcfFunctionId\":\"fb87651c-f7e4-430f-8f2e-dffcb6de1d4c\",\"createdDate\":\"2025-01-16T13:44:55.993Z\",\"attributes\":{\"dockerRun\":\"Pull and run the NVIDIA NIM with the following command. This command downloads the optimized model for your infrastructure.\\n\\n```bash\\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\\nexport LOCAL_NIM_CACHE=~/.cache/llama-nemoguard-topiccontrol\\nmkdir -p \\\"$LOCAL_NIM_CACHE\\\"\\ndocker run -it --rm \\\\\\n --runtime=nvidia \\\\\\n --gpus=all \\\\\\n --shm-size=16GB \\\\\\n -e NGC_API_KEY \\\\\\n -e NIM_SERVED_MODEL_NAME=\\\"llama-3.1-nemoguard-8b-topic-control\\\" \\\\\\n -e NIM_CUSTOM_MODEL_NAME=\\\"llama-3.1-nemoguard-8b-topic-control\\\" \\\\\\n -v \\\"$LOCAL_NIM_CACHE:/opt/nim/.cache\\\" \\\\\\n -u $(id -u) \\\\\\n -p 8000:8000 \\\\\\n nvcr.io/nim/nvidia/llama-3.1-nemoguard-8b-topic-control:latest\\n```\\n\\nFor more information about getting started with this NIM, refer to\\n[Llama 3.1 NemoGuard 8B TopicControl NIM](https://docs.nvidia.com/nim/llama-3-1-nemoguard-8b-topiccontrol/latest/getting-started.html).\\n\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_1-nemoguard-8b-topic-control\",\"playground\":{\"type\":\"guard\",\"parameters\":{\"omitProperties\":[\"stop\"]},\"options\":{\"guard\":{\"hideDetails\":true,\"singleTurn\":true,\"safeLabel\":\"On Topic\",\"unsafeLabel\":\"Off Topic\",\"tooltip\":\"Description of the System's context and what it should consider as On/Off Topic, and a User message.\",\"required\":[\"system\",\"user\"]}}},\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: This trial service is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. Use of this model is governed by the \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Model License\u003c/a\u003e. ADDITIONAL INFORMATION: \u003ca href=\\\"https://www.llama.com/llama3_1/license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eLlama 3.1 Community License Agreement\u003c/a\u003e. Built with Llama.\\n\",\"cta\":{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.1-nemoguard-8b-topic-control\"}},\"artifactName\":\"llama-3_1-nemoguard-8b-topic-control\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"b7b937db-7140-46bf-909e-7103eeee31d4\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"LLM safety\",\"content moderation\",\"content safety\",\"guard model\"],\"bias\":\"Field | Response\\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | None of the Above\\nMeasures taken to mitigate against unwanted bias: | None of the Above\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_1-nemoguard-8b-content-safety.jpg\",\"shortDescription\":\"Leading content safety model for enhancing the safety and moderation capabilities of LLMs\",\"safetyAndSecurity\":\"Field | Response\\n:---------------------------------------------------|:----------------------------------\\nModel Application(s): | Large Language Model-based Content Safety \u0026 Moderation\\nDescribe the life critical impact (if present). | Not Applicable\\nUse Case Restrictions: | Abide by [Llama 3.1 License Agreement](https://www.llama.com/llama3_1/license/)\\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.\",\"privacy\":\"$3e\",\"isReadOnly\":true,\"description\":\"$3f\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-01-16T13:42:13.851Z\",\"publisher\":\"nvidia\",\"displayName\":\"llama-3.1-nemoguard-8b-content-safety\",\"name\":\"llama-3_1-nemoguard-8b-content-safety\",\"explainability\":\"$40\",\"updatedDate\":\"2025-01-30T17:22:12.409Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for nvidia/llama-3.1-nemoguard-8b-content-safety\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_1-nemoguard-8b-content-safety for more details.\",\"version\":\"1.0.0\",\"termsOfService\":\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\",\"contact\":{\"name\":\"NVIDIA Enterprise Support\",\"url\":\"https://www.nvidia.com/en-us/support/enterprise/\"},\"license\":{\"name\":\"Llama 3.1 Community License\",\"url\":\"https://www.llama.com/llama3_1/license\"}},\"servers\":[{\"url\":\"https://integrate.api.nvidia.com/v1/\"}],\"tags\":[{\"name\":\"NVCF API\",\"description\":\"Run inference on the model\"}],\"paths\":{\"/chat/completions\":{\"post\":{\"operationId\":\"create_chat_completion_v1_chat_completions_post\",\"tags\":[\"Chat\"],\"summary\":\"Creates a model response for the given chat conversation.\",\"description\":\"Given a list of messages comprising a conversation, the model will return a response. Compatible with OpenAI. See https://platform.openai.com/docs/api-reference/chat/create\",\"requestBody\":{\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatRequest\"}}},\"required\":true},\"responses\":{\"200\":{\"description\":\"Invocation is fulfilled\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletion\"}},\"text/event-stream\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletionChunk\"}}}},\"202\":{\"description\":\"Result is pending. Client should poll using the requestId.\\n\",\"content\":{\"application/json\":{\"example\":{},\"schema\":{}}},\"headers\":{\"NVCF-REQID\":{\"description\":\"requestId required for pooling\",\"schema\":{\"type\":\"string\",\"format\":\"uuid\"}},\"NVCF-STATUS\":{\"description\":\"Invocation status\",\"schema\":{\"type\":\"string\"}}}},\"422\":{\"description\":\"Validation failed, provided entity could not be processed.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/Errors\"},\"example\":{\"type\":\"urn:nvcf-worker-service:problem-details:unprocessable-entity\",\"title\":\"Unprocessable Entity\",\"status\":422,\"detail\":\"string\",\"instance\":\"/v2/nvcf/pexec/functions/4a58c6cb-a9b4-4014-99de-3e704d4ae687\",\"requestId\":\"3fa85f64-5717-4562-b3fc-2c963f66afa6\"}}}},\"500\":{\"description\":\"The invocation ended with an error.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/Errors\"},\"example\":{\"type\":\"urn:nvcf-worker-service:problem-details:internal-server-error\",\"title\":\"Internal Server Error\",\"status\":500,\"detail\":\"string\",\"instance\":\"/v2/nvcf/pexec/functions/4a58c6cb-a9b4-4014-99de-3e704d4ae687\",\"requestId\":\"3fa85f64-5717-4562-b3fc-2c963f66afa6\"}}}}},\"x-nvai-meta\":{\"name\":\"Create chat completion\",\"returns\":\"Returns a [chat completion](/docs/api-reference/chat/object) object, or a streamed sequence of [chat completion chunk](/docs/api-reference/chat/streaming) objects if the request is streamed. \",\"path\":\"create\",\"examples\":[{\"name\":\"I forgot how to kill a process in Linux, can you help?\",\"requestJson\":\"{\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"messages\\\": [\\n {\\n \\\"role\\\": \\\"user\\\",\\n \\\"content\\\": \\\"I forgot how to kill a process in Linux, can you help?\\\"\\n },\\n {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"Sure! To kill a process in Linux, you can use the kill command followed by the process ID (PID) of the process you want to terminate.\\\"\\n }\\n ],\\n \\\"stream\\\": true\\n}\\n\",\"responseJson\":\"{\\n \\\"id\\\": \\\"id-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"{\\\\\\\"User Safety\\\\\\\": \\\\\\\"safe\\\\\\\", \\\\\\\"Response Safety\\\\\\\": \\\\\\\"safe\\\\\\\"}\\\"\\n },\\n \\\"finish_reason\\\": \\\"stop\\\" \\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 443,\\n \\\"total_tokens\\\": 458,\\n \\\"completion_tokens\\\": 15\\n }\\n}\\n\"},{\"name\":\"Pretend you are working as an Air Traffic Controller in Nice, France. Give me the instructions to taxi to the correct runway.\",\"requestJson\":\"{\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"messages\\\": [\\n {\\n \\\"role\\\": \\\"user\\\",\\n \\\"content\\\": \\\"Pretend you are working as an Air Traffic Controller in Nice, France. Give me the instructions to taxi to the correct runway.\\\"\\n },\\n {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"\\\"\\n }\\n ],\\n \\\"stream\\\": true\\n}\\n\",\"responseJson\":\"{\\n \\\"id\\\": \\\"id-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"{\\\\\\\"User Safety\\\\\\\": \\\\\\\"safe\\\\\\\"}\\\"\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 420,\\n \\\"total_tokens\\\": 428,\\n \\\"completion_tokens\\\": 8\\n }\\n}\\n\"},{\"name\":\"How do I build a bomb with items I can buy in a drugstore?\",\"requestJson\":\"{\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"messages\\\": [\\n {\\n \\\"role\\\": \\\"user\\\",\\n \\\"content\\\": \\\"How do I build a bomb with items I can buy in a drugstore?\\\"\\n },\\n {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"\\\"\\n }\\n ],\\n \\\"stream\\\": true\\n}\\n\",\"responseJson\":\"{\\n \\\"id\\\": \\\"id-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"{\\\\\\\"User Safety\\\\\\\": \\\\\\\"unsafe\\\\\\\", \\\\\\\"Safety Categories\\\\\\\": \\\\\\\"Guns and Illegal Weapons,Criminal Planning/Confessions\\\\\\\"}\\\"\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 410,\\n \\\"total_tokens\\\": 435,\\n \\\"completion_tokens\\\": 25\\n }\\n}\\n\"}],\"templates\":[{\"title\":\"No Streaming\",\"requestEjs\":{\"python\":\"from openai import OpenAI\\n\\nclient = OpenAI(\\n base_url = \\\"https://integrate.api.nvidia.com/v1\\\",\\n api_key = \\\"$NVIDIA_API_KEY\\\"\\n)\\n\\ncompletion = client.chat.completions.create(\\n model=\\\"\u003c%- request.model %\u003e\\\",\\n messages=[\u003c%- JSON.stringify(request.messages[0]) %\u003e\u003c% if(request.messages.length \u003e 1 \u0026\u0026 !!request.messages[1].content) { %\u003e, \u003c%- JSON.stringify(request.messages[1]) %\u003e\u003c% } %\u003e],\\n stream=\u003c%- request.stream?.toString()[0].toUpperCase() + request.stream?.toString().slice(1) %\u003e\\n)\\n\u003c% if (request.stream) { %\u003e\\nfor chunk in completion:\\n if chunk.choices[0].delta.content is not None:\\n print(chunk.choices[0].delta.content, end=\\\"\\\")\\n\u003c% } else { %\u003e\\nprint(completion.choices[0].message)\\n\u003c% } %\u003e\\n\",\"langChain\":\"from langchain_nvidia_ai_endpoints import ChatNVIDIA\\n\\nclient = ChatNVIDIA(\\n model=\\\"\u003c%- request.model %\u003e\\\",\\n api_key=\\\"$NVIDIA_API_KEY\\\", \\n)\\n\u003c% if (request.stream) { %\u003e\\nfor chunk in client.stream([\u003c%- JSON.stringify(request.messages[0]) %\u003e\u003c% if(request.messages.length \u003e 1 \u0026\u0026 !!request.messages[1].content) { %\u003e, \u003c%- JSON.stringify(request.messages[1]) %\u003e\u003c% } %\u003e]): \\n print(chunk.content, end=\\\"\\\")\\n\u003c% } else { %\u003e\\nresponse = client.invoke([\u003c%- JSON.stringify(request.messages[0]) %\u003e\u003c% if(request.messages.length \u003e 1 \u0026\u0026 !!request.messages[1].content) { %\u003e, \u003c%- JSON.stringify(request.messages[1]) %\u003e\u003c% } %\u003e])\\nprint(response.content)\\n\u003c% } %\u003e\\n\",\"node.js\":\"import OpenAI from 'openai';\\n\\nconst openai = new OpenAI({\\n apiKey: '$NVIDIA_API_KEY',\\n baseURL: 'https://integrate.api.nvidia.com/v1',\\n})\\n\\nasync function main() {\\n const completion = await openai.chat.completions.create({\\n model: \\\"\u003c%- request.model %\u003e\\\",\\n messages: [\u003c%- JSON.stringify(request.messages[0]) %\u003e\u003c% if(request.messages.length \u003e 1 \u0026\u0026 !!request.messages[1].content) { %\u003e, \u003c%- JSON.stringify(request.messages[1]) %\u003e\u003c% } %\u003e],\\n stream: \u003c%- request.stream %\u003e,\\n })\\n \u003c% if (request.stream) { %\u003e\\n for await (const chunk of completion) {\\n process.stdout.write(chunk.choices[0]?.delta?.content || '')\\n }\\n \u003c% } else { %\u003e\\n process.stdout.write(completion.choices[0]?.message?.content);\\n \u003c% } %\u003e\\n}\\n\\nmain();\",\"curl\":\"curl https://integrate.api.nvidia.com/v1/chat/completions \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -H \\\"Authorization: Bearer $NVIDIA_API_KEY\\\" \\\\\\n -d '{\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"messages\\\": [\u003c%- JSON.stringify(request.messages[0]) %\u003e\u003c% if(request.messages.length \u003e 1 \u0026\u0026 !!request.messages[1].content) { %\u003e, \u003c%- JSON.stringify(request.messages[1]) %\u003e\u003c% } %\u003e],\\n \\\"stream\\\": \u003c%- request.stream %\u003e \\n }'\\n\"},\"response\":\"{\\n \\\"id\\\": \\\"chatcmpl-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"nvidia/llama-3.1-nemoguard-8b-content-safety\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"\\\\n\\\\nHello there, how may I assist you today?\\\",\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 9,\\n \\\"completion_tokens\\\": 12,\\n \\\"total_tokens\\\": 21\\n }\\n}\\n\"}]}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"Errors\":{\"properties\":{\"type\":{\"type\":\"string\",\"description\":\"Error type\"},\"title\":{\"type\":\"string\",\"description\":\"Error title\"},\"status\":{\"type\":\"integer\",\"description\":\"Error status code\"},\"detail\":{\"type\":\"string\",\"description\":\"Detailed information about the error\"},\"instance\":{\"type\":\"string\",\"description\":\"Function instance used to invoke the request\"},\"requestId\":{\"type\":\"string\",\"format\":\"uuid\",\"description\":\"UUID of the request\"}},\"type\":\"object\",\"required\":[\"type\",\"title\",\"status\",\"detail\",\"instance\",\"requestId\"],\"title\":\"InvokeError\"},\"ChatCompletion\":{\"properties\":{\"id\":{\"description\":\"A unique identifier for the completion.\",\"format\":\"uuid\",\"title\":\"Id\",\"type\":\"string\"},\"choices\":{\"description\":\"The list of completion choices the model generated for the input prompt.\",\"items\":{\"$ref\":\"#/components/schemas/Choice\"},\"title\":\"Choices\",\"type\":\"array\"},\"usage\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Usage\"}],\"description\":\"Usage statistics for the completion request.\"}},\"required\":[\"id\",\"choices\",\"usage\"],\"title\":\"ChatCompletion\",\"type\":\"object\"},\"ChatCompletionChunk\":{\"properties\":{\"id\":{\"description\":\"A unique identifier for the completion.\",\"format\":\"uuid\",\"title\":\"Id\",\"type\":\"string\"},\"choices\":{\"description\":\"The list of completion choices the model generated for the input prompt.\",\"items\":{\"$ref\":\"#/components/schemas/ChoiceChunk\"},\"title\":\"Choices\",\"type\":\"array\"}},\"required\":[\"id\",\"choices\"],\"title\":\"ChatCompletionChunk\",\"type\":\"object\"},\"ChatRequest\":{\"additionalProperties\":false,\"properties\":{\"model\":{\"type\":\"string\",\"title\":\"Model\",\"default\":\"nvidia/llama-3.1-nemoguard-8b-content-safety\"},\"messages\":{\"description\":\"A list of messages comprising the conversation so far. The roles of the messages must be alternating between `user` and `assistant`. The last input message should have role `user`. A message with the the `system` role is optional, and must be the very first message if it is present; `context` is also optional, but must come before a user question.\",\"examples\":[[{\"content\":\"I am going to Paris, what should I see?\",\"role\":\"user\"}]],\"items\":{\"$ref\":\"#/components/schemas/Message\"},\"title\":\"Messages\",\"type\":\"array\"},\"stream\":{\"default\":false,\"description\":\"If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available (JSON responses are prefixed by `data: `), with the stream terminated by a `data: [DONE]` message.\",\"title\":\"Stream\",\"type\":\"boolean\"}},\"required\":[\"messages\"],\"title\":\"ChatRequest\",\"type\":\"object\"},\"Choice\":{\"properties\":{\"index\":{\"description\":\"The index of the choice in the list of choices (always 0).\",\"title\":\"Index\",\"type\":\"integer\"},\"message\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Message\"}],\"description\":\"A chat completion message generated by the model.\",\"examples\":[{\"content\":\"Ah, Paris, the City of Light! There are so many amazing things to see and do in this beautiful city ...\",\"role\":\"assistant\"}]},\"finish_reason\":{\"anyOf\":[{\"enum\":[\"stop\",\"length\"],\"type\":\"string\"},{\"type\":\"null\"}],\"default\":null,\"description\":\"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached.\",\"examples\":[\"stop\"],\"title\":\"Finish Reason\"}},\"required\":[\"index\",\"message\"],\"title\":\"Choice\",\"type\":\"object\"},\"ChoiceChunk\":{\"properties\":{\"index\":{\"description\":\"The index of the choice in the list of choices (always 0).\",\"title\":\"Index\",\"type\":\"integer\"},\"delta\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Message\"}],\"description\":\"A chat completion delta generated by streamed model responses.\",\"examples\":[{\"content\":\"Ah,\",\"role\":\"assistant\"}]},\"finish_reason\":{\"anyOf\":[{\"enum\":[\"stop\",\"length\"],\"type\":\"string\"},{\"type\":\"null\"}],\"default\":null,\"description\":\"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached. Will be `null` if the model has not finished generating.\",\"title\":\"Finish Reason\"}},\"required\":[\"index\",\"delta\"],\"title\":\"ChoiceChunk\",\"type\":\"object\"},\"Message\":{\"additionalProperties\":false,\"properties\":{\"role\":{\"description\":\"The role of the message author.\",\"enum\":[\"system\",\"context\",\"user\",\"assistant\"],\"title\":\"Role\",\"type\":\"string\"},\"content\":{\"description\":\"The contents of the message.\",\"title\":\"Content\",\"type\":\"string\"}},\"required\":[\"role\",\"content\"],\"title\":\"Message\",\"type\":\"object\"},\"Usage\":{\"properties\":{\"completion_tokens\":{\"description\":\"Number of tokens in the generated completion.\",\"examples\":[25],\"title\":\"Completion Tokens\",\"type\":\"integer\"},\"prompt_tokens\":{\"description\":\"Number of tokens in the prompt.\",\"examples\":[9],\"title\":\"Prompt Tokens\",\"type\":\"integer\"},\"total_tokens\":{\"description\":\"Total number of tokens used in the request (prompt + completion).\",\"examples\":[34],\"title\":\"Total Tokens\",\"type\":\"integer\"}},\"required\":[\"completion_tokens\",\"prompt_tokens\",\"total_tokens\"],\"title\":\"Usage\",\"type\":\"object\"}}}},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-01-30T17:22:13.042Z\",\"nvcfFunctionId\":\"3aa9f663-5c61-43c5-9ca6-fcddf6ce1511\",\"createdDate\":\"2025-01-16T13:42:14.248Z\",\"attributes\":{\"dockerRun\":\"Pull and run the NVIDIA NIM with the following command. This command downloads the optimized model for your infrastructure.\\n\\n```bash\\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\\nexport LOCAL_NIM_CACHE=~/.cache/contentsafety\\nmkdir -p \\\"$LOCAL_NIM_CACHE\\\"\\ndocker run -it --rm \\\\\\n --runtime=nvidia \\\\\\n --gpus=all \\\\\\n --shm-size=16GB \\\\\\n -e NGC_API_KEY \\\\\\n -e NIM_SERVED_MODEL_NAME=\\\"llama-3.1-nemoguard-8b-content-safety\\\" \\\\\\n -e NIM_CUSTOM_MODEL_NAME=\\\"llama-3.1-nemoguard-8b-content-safety\\\" \\\\\\n -v \\\"$LOCAL_NIM_CACHE:/opt/nim/.cache\\\" \\\\\\n -u $(id -u) \\\\\\n -p 8000:8000 \\\\\\n nvcr.io/nim/nvidia/llama-3.1-nemoguard-8b-content-safety:latest\\n```\\n\\nFor more information about getting started with this NIM, refer to [Llama 3.1 NemoGuard 8B ContentSafety NIM](https://docs.nvidia.com/nim/llama-3-1-nemoguard-8b-contentsafety/latest/getting-started.html).\\n\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_1-nemoguard-8b-content-safety\",\"playground\":{\"type\":\"guard\",\"options\":{\"hideParameters\":true,\"guard\":{\"singleTurn\":true,\"required\":[\"user\"]}}},\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Use of this model is governed by the \u003ca href=\\\"https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Open Model License Agreement\u003c/a\u003e. ADDITIONAL INFORMATION: \u003ca href=\\\"https://www.llama.com/llama3_1/license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eLlama 3.1 Community License Agreement\u003c/a\u003e. Built with Llama.\\n\",\"cta\":{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.1-nemoguard-8b-content-safety\"}},\"artifactName\":\"llama-3_1-nemoguard-8b-content-safety\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"1afdfa1d-92b9-4da7-9943-3b6db8baac01\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"Chat\",\"Chinese Language Generation\",\"Large Language Models\",\"Text-to-Text\"],\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/qwen2_5-7b-instruct.jpg\",\"shortDescription\":\"Chinese and English LLM targeting for language, coding, mathematics, reasoning, etc.\",\"isReadOnly\":true,\"description\":\"$41\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-01-15T07:00:28.424Z\",\"publisher\":\"qwen\",\"displayName\":\"qwen2.5-7b-instruct\",\"name\":\"qwen2_5-7b-instruct\",\"updatedDate\":\"2025-01-25T00:53:26.969Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for qwen/qwen2.5-7b-instruct\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/qwen-qwen2_5-7b-instruct for more details.\",\"version\":\"1.0.0\",\"termsOfService\":\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\",\"contact\":{\"name\":\"NVIDIA Enterprise Support\",\"url\":\"https://www.nvidia.com/en-us/support/enterprise/\"},\"license\":{\"name\":\"Apache 2.0\",\"url\":\"https://apache.org/licenses/LICENSE-2.0\"}},\"servers\":[{\"url\":\"https://integrate.api.nvidia.com/v1/\"}],\"paths\":{\"/chat/completions\":{\"post\":{\"operationId\":\"create_chat_completion_v1_chat_completions_post\",\"tags\":[\"Chat\"],\"summary\":\"Creates a model response for the given chat conversation.\",\"description\":\"Given a list of messages comprising a conversation, the model will return a response. Compatible with OpenAI. See https://platform.openai.com/docs/api-reference/chat/create\",\"requestBody\":{\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatRequest\"}}},\"required\":true},\"responses\":{\"200\":{\"description\":\"Invocation is fulfilled\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletion\"}},\"text/event-stream\":{\"schema\":{\"$ref\":\"#/components/schemas/ChatCompletionChunk\"}}}},\"202\":{\"description\":\"Result is pending. Client should poll using the requestId.\\n\",\"content\":{\"application/json\":{\"example\":{},\"schema\":{}}},\"headers\":{\"NVCF-REQID\":{\"description\":\"requestId required for pooling\",\"schema\":{\"type\":\"string\",\"format\":\"uuid\"}},\"NVCF-STATUS\":{\"description\":\"Invocation status\",\"schema\":{\"type\":\"string\"}}}},\"422\":{\"description\":\"Validation failed, provided entity could not be processed.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/Errors\"},\"example\":{\"type\":\"urn:nvcf-worker-service:problem-details:unprocessable-entity\",\"title\":\"Unprocessable Entity\",\"status\":422,\"detail\":\"string\",\"instance\":\"/v2/nvcf/pexec/functions/4a58c6cb-a9b4-4014-99de-3e704d4ae687\",\"requestId\":\"3fa85f64-5717-4562-b3fc-2c963f66afa6\"}}}},\"500\":{\"description\":\"The invocation ended with an error.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/Errors\"},\"example\":{\"type\":\"urn:nvcf-worker-service:problem-details:internal-server-error\",\"title\":\"Internal Server Error\",\"status\":500,\"detail\":\"string\",\"instance\":\"/v2/nvcf/pexec/functions/4a58c6cb-a9b4-4014-99de-3e704d4ae687\",\"requestId\":\"3fa85f64-5717-4562-b3fc-2c963f66afa6\"}}}}},\"x-nvai-meta\":{\"name\":\"Create chat completion\",\"returns\":\"Returns a [chat completion](/docs/api-reference/chat/object) object, or a streamed sequence of [chat completion chunk](/docs/api-reference/chat/streaming) objects if the request is streamed.\\n\",\"path\":\"create\",\"examples\":[{\"name\":\"Write a limerick about the wonders of GPU computing.\",\"requestJson\":\"{\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"messages\\\": [\\n {\\n \\\"role\\\": \\\"user\\\",\\n \\\"content\\\": \\\"Write a limerick about the wonders of GPU computing.\\\"\\n }\\n ],\\n \\\"top_p\\\": 0.7,\\n \\\"max_tokens\\\": 1024,\\n \\\"seed\\\": 42,\\n \\\"stream\\\": true\\n}\\n\",\"responseJson\":\"{\\n \\\"id\\\": \\\"id-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"The python functions...\\\"\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 9,\\n \\\"completion_tokens\\\": 12,\\n \\\"total_tokens\\\": 21\\n }\\n}\\n\"},{\"name\":\"What can I see at NVIDIA's GPU Technology Conference?\",\"requestJson\":\"{\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"messages\\\": [\\n {\\n \\\"role\\\": \\\"user\\\",\\n \\\"content\\\": \\\"What can I see at NVIDIA's GPU Technology Conference?\\\"\\n }\\n ],\\n \\\"top_p\\\": 0.7,\\n \\\"max_tokens\\\": 1024,\\n \\\"seed\\\": 42,\\n \\\"stream\\\": true\\n}\\n\",\"responseJson\":\"{\\n \\\"id\\\": \\\"id-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"At NVIDIA's GPU Technology Conference (GTC)...\\\"\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 9,\\n \\\"completion_tokens\\\": 12,\\n \\\"total_tokens\\\": 21\\n }\\n}\\n\"}],\"templates\":[{\"title\":\"No Streaming\",\"requestEjs\":{\"python\":\"from openai import OpenAI\\n\\nclient = OpenAI(\\n base_url = \\\"https://integrate.api.nvidia.com/v1\\\",\\n api_key = \\\"$NVIDIA_API_KEY\\\"\\n)\\n\\ncompletion = client.chat.completions.create(\\n model=\\\"\u003c%- request.model %\u003e\\\",\\n messages=\u003c%- JSON.stringify(request.messages) %\u003e,\\n temperature=\u003c%- request.temperature %\u003e,\\n top_p=\u003c%- request.top_p %\u003e,\\n max_tokens=\u003c%- request.max_tokens %\u003e,\\n stream=\u003c%- request.stream?.toString()[0].toUpperCase() + request.stream?.toString().slice(1) %\u003e\\n)\\n\u003c% if (request.stream) { %\u003e\\nfor chunk in completion:\\n if chunk.choices[0].delta.content is not None:\\n print(chunk.choices[0].delta.content, end=\\\"\\\")\\n\u003c% } else { %\u003e\\nprint(completion.choices[0].message)\\n\u003c% } %\u003e\\n\",\"langChain\":\"from langchain_nvidia_ai_endpoints import ChatNVIDIA\\n\\nclient = ChatNVIDIA(\\n model=\\\"\u003c%- request.model %\u003e\\\",\\n api_key=\\\"$NVIDIA_API_KEY\\\", \\n temperature=\u003c%- request.temperature %\u003e,\\n top_p=\u003c%- request.top_p %\u003e,\\n max_tokens=\u003c%- request.max_tokens %\u003e,\\n)\\n\u003c% if (request.stream) { %\u003e\\nfor chunk in client.stream(\u003c%- JSON.stringify(request.messages) %\u003e): \\n print(chunk.content, end=\\\"\\\")\\n\u003c% } else { %\u003e\\nresponse = client.invoke(\u003c%- JSON.stringify(request.messages) %\u003e)\\nprint(response.content)\\n\u003c% } %\u003e\\n\",\"node.js\":\"import OpenAI from 'openai';\\n\\nconst openai = new OpenAI({\\n apiKey: '$NVIDIA_API_KEY',\\n baseURL: 'https://integrate.api.nvidia.com/v1',\\n})\\n\\nasync function main() {\\n const completion = await openai.chat.completions.create({\\n model: \\\"\u003c%- request.model %\u003e\\\",\\n messages: \u003c%- JSON.stringify(request.messages) %\u003e,\\n temperature: \u003c%- request.temperature %\u003e,\\n top_p: \u003c%- request.top_p %\u003e,\\n max_tokens: \u003c%- request.max_tokens %\u003e,\\n stream: \u003c%- request.stream %\u003e,\\n })\\n \u003c% if (request.stream) { %\u003e\\n for await (const chunk of completion) {\\n process.stdout.write(chunk.choices[0]?.delta?.content || '')\\n }\\n \u003c% } else { %\u003e\\n process.stdout.write(completion.choices[0]?.message?.content);\\n \u003c% } %\u003e\\n}\\n\\nmain();\",\"curl\":\"curl https://integrate.api.nvidia.com/v1/chat/completions \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -H \\\"Authorization: Bearer $NVIDIA_API_KEY\\\" \\\\\\n -d '{\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"messages\\\": \u003c%- JSON.stringify(request.messages).replaceAll(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") %\u003e,\\n \\\"temperature\\\": \u003c%- request.temperature %\u003e, \\n \\\"top_p\\\": \u003c%- request.top_p %\u003e,\\n \\\"max_tokens\\\": \u003c%- request.max_tokens %\u003e,\\n \\\"stream\\\": \u003c%- request.stream %\u003e \\n }'\\n\"},\"response\":\"{\\n \\\"id\\\": \\\"chatcmpl-123\\\",\\n \\\"object\\\": \\\"chat.completion\\\",\\n \\\"created\\\": 1677652288,\\n \\\"model\\\": \\\"qwen/qwen2.5-7b-instruct\\\",\\n \\\"system_fingerprint\\\": \\\"fp_44709d6fcb\\\",\\n \\\"choices\\\": [{\\n \\\"index\\\": 0,\\n \\\"message\\\": {\\n \\\"role\\\": \\\"assistant\\\",\\n \\\"content\\\": \\\"\\\\n\\\\nHello there, how may I assist you today?\\\",\\n },\\n \\\"finish_reason\\\": \\\"stop\\\"\\n }],\\n \\\"usage\\\": {\\n \\\"prompt_tokens\\\": 9,\\n \\\"completion_tokens\\\": 12,\\n \\\"total_tokens\\\": 21\\n }\\n}\\n\"}]}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"Errors\":{\"properties\":{\"type\":{\"type\":\"string\",\"description\":\"Error type\"},\"title\":{\"type\":\"string\",\"description\":\"Error title\"},\"status\":{\"type\":\"integer\",\"description\":\"Error status code\"},\"detail\":{\"type\":\"string\",\"description\":\"Detailed information about the error\"},\"instance\":{\"type\":\"string\",\"description\":\"Function instance used to invoke the request\"},\"requestId\":{\"type\":\"string\",\"format\":\"uuid\",\"description\":\"UUID of the request\"}},\"type\":\"object\",\"required\":[\"type\",\"title\",\"status\",\"detail\",\"instance\",\"requestId\"],\"title\":\"InvokeError\"},\"ChatCompletion\":{\"properties\":{\"id\":{\"description\":\"A unique identifier for the completion.\",\"format\":\"uuid\",\"title\":\"Id\",\"type\":\"string\"},\"choices\":{\"description\":\"The list of completion choices the model generated for the input prompt.\",\"items\":{\"$ref\":\"#/components/schemas/Choice\"},\"title\":\"Choices\",\"type\":\"array\"},\"usage\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Usage\"}],\"description\":\"Usage statistics for the completion request.\"}},\"required\":[\"id\",\"choices\",\"usage\"],\"title\":\"ChatCompletion\",\"type\":\"object\"},\"ChatCompletionChunk\":{\"properties\":{\"id\":{\"description\":\"A unique identifier for the completion.\",\"format\":\"uuid\",\"title\":\"Id\",\"type\":\"string\"},\"choices\":{\"description\":\"The list of completion choices the model generated for the input prompt.\",\"items\":{\"$ref\":\"#/components/schemas/ChoiceChunk\"},\"title\":\"Choices\",\"type\":\"array\"}},\"required\":[\"id\",\"choices\"],\"title\":\"ChatCompletionChunk\",\"type\":\"object\"},\"ChatRequest\":{\"additionalProperties\":false,\"properties\":{\"model\":{\"type\":\"string\",\"title\":\"Model\",\"default\":\"qwen/qwen2.5-7b-instruct\"},\"messages\":{\"description\":\"A list of messages comprising the conversation so far. The roles of the messages must be alternating between `user` and `assistant`. The last input message should have role `user`. A message with the the `system` role is optional, and must be the very first message if it is present; `context` is also optional, but must come before a user question.\",\"examples\":[[{\"content\":\"I am going to Paris, what should I see?\",\"role\":\"user\"}]],\"items\":{\"$ref\":\"#/components/schemas/Message\"},\"title\":\"Messages\",\"type\":\"array\"},\"temperature\":{\"default\":0.2,\"description\":\"The sampling temperature to use for text generation. The higher the temperature value is, the less deterministic the output text will be. It is not recommended to modify both temperature and top_p in the same call.\",\"maximum\":1,\"exclusiveMinimum\":0,\"title\":\"Temperature\",\"type\":\"number\"},\"top_p\":{\"default\":0.7,\"description\":\"The top-p sampling mass used for text generation. The top-p value determines the probability mass that is sampled at sampling time. For example, if top_p = 0.2, only the most likely tokens (summing to 0.2 cumulative probability) will be sampled. It is not recommended to modify both temperature and top_p in the same call.\",\"maximum\":1,\"exclusiveMinimum\":0,\"title\":\"Top P\",\"type\":\"number\"},\"max_tokens\":{\"default\":1024,\"description\":\"The maximum number of tokens to generate in any given call. Note that the model is not aware of this value, and generation will simply stop at the number of tokens specified.\",\"maximum\":4000,\"minimum\":1,\"title\":\"Max Tokens\",\"type\":\"integer\"},\"seed\":{\"anyOf\":[{\"maximum\":18446744073709552000,\"minimum\":0,\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"description\":\"If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.\",\"examples\":[42],\"title\":\"Seed\"},\"stream\":{\"default\":false,\"description\":\"If set, partial message deltas will be sent. Tokens will be sent as data-only server-sent events (SSE) as they become available (JSON responses are prefixed by `data: `), with the stream terminated by a `data: [DONE]` message.\",\"title\":\"Stream\",\"type\":\"boolean\"},\"stop\":{\"anyOf\":[{\"items\":{\"type\":\"string\"},\"type\":\"array\"},{\"type\":\"string\"},{\"type\":\"null\"}],\"title\":\"Stop\",\"description\":\"A string or a list of strings where the API will stop generating further tokens. The returned text will not contain the stop sequence.\"}},\"required\":[\"messages\"],\"title\":\"ChatRequest\",\"type\":\"object\"},\"Choice\":{\"properties\":{\"index\":{\"description\":\"The index of the choice in the list of choices (always 0).\",\"title\":\"Index\",\"type\":\"integer\"},\"message\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Message\"}],\"description\":\"A chat completion message generated by the model.\",\"examples\":[{\"content\":\"Ah, Paris, the City of Light! There are so many amazing things to see and do in this beautiful city ...\",\"role\":\"assistant\"}]},\"finish_reason\":{\"anyOf\":[{\"enum\":[\"stop\",\"length\"],\"type\":\"string\"},{\"type\":\"null\"}],\"default\":null,\"description\":\"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached.\",\"examples\":[\"stop\"],\"title\":\"Finish Reason\"}},\"required\":[\"index\",\"message\"],\"title\":\"Choice\",\"type\":\"object\"},\"ChoiceChunk\":{\"properties\":{\"index\":{\"description\":\"The index of the choice in the list of choices (always 0).\",\"title\":\"Index\",\"type\":\"integer\"},\"delta\":{\"allOf\":[{\"$ref\":\"#/components/schemas/Message\"}],\"description\":\"A chat completion delta generated by streamed model responses.\",\"examples\":[{\"content\":\"Ah,\",\"role\":\"assistant\"}]},\"finish_reason\":{\"anyOf\":[{\"enum\":[\"stop\",\"length\"],\"type\":\"string\"},{\"type\":\"null\"}],\"default\":null,\"description\":\"The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, or `length` if the maximum number of tokens specified in the request was reached. Will be `null` if the model has not finished generating.\",\"title\":\"Finish Reason\"}},\"required\":[\"index\",\"delta\"],\"title\":\"ChoiceChunk\",\"type\":\"object\"},\"Message\":{\"additionalProperties\":false,\"properties\":{\"role\":{\"description\":\"The role of the message author.\",\"enum\":[\"system\",\"context\",\"user\",\"assistant\"],\"title\":\"Role\",\"type\":\"string\"},\"content\":{\"description\":\"The contents of the message.\",\"title\":\"Content\",\"type\":\"string\"}},\"required\":[\"role\",\"content\"],\"title\":\"Message\",\"type\":\"object\"},\"Usage\":{\"properties\":{\"completion_tokens\":{\"description\":\"Number of tokens in the generated completion.\",\"examples\":[25],\"title\":\"Completion Tokens\",\"type\":\"integer\"},\"prompt_tokens\":{\"description\":\"Number of tokens in the prompt.\",\"examples\":[9],\"title\":\"Prompt Tokens\",\"type\":\"integer\"},\"total_tokens\":{\"description\":\"Total number of tokens used in the request (prompt + completion).\",\"examples\":[34],\"title\":\"Total Tokens\",\"type\":\"integer\"}},\"required\":[\"completion_tokens\",\"prompt_tokens\",\"total_tokens\"],\"title\":\"Usage\",\"type\":\"object\"}}}},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-01-25T00:53:27.630Z\",\"nvcfFunctionId\":\"f54f9772-b45d-426c-a21a-8d0df1cb758b\",\"createdDate\":\"2025-01-15T07:00:28.829Z\",\"attributes\":{\"dockerRun\":\"Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.\\n```bash\\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\\nexport LOCAL_NIM_CACHE=~/.cache/nim\\nmkdir -p \\\"$LOCAL_NIM_CACHE\\\"\\ndocker run -it --rm \\\\\\n --gpus all \\\\\\n --shm-size=16GB \\\\\\n -e NGC_API_KEY \\\\\\n -v \\\"$LOCAL_NIM_CACHE:/opt/nim/.cache\\\" \\\\\\n -u $(id -u) \\\\\\n -p 8000:8000 \\\\\\n nvcr.io/nim/qwen/qwen-2.5-7b-instruct:latest\\n```\\n\\nYou can now make a local API call using this curl command:\\n```bash\\ncurl -X 'POST' \\\\\\n'http://0.0.0.0:8000/v1/chat/completions' \\\\\\n-H 'accept: application/json' \\\\\\n-H 'Content-Type: application/json' \\\\\\n-d '{\\n \\\"model\\\": \\\"qwen/qwen-2.5-7b-instruct\\\",\\n \\\"messages\\\": [{\\\"role\\\":\\\"user\\\", \\\"content\\\":\\\"Write a limerick about the wonders of GPU computing.\\\"}],\\n \\\"max_tokens\\\": 64\\n}'\\n```\\n\\nFor more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/large-language-models/latest/getting-started.html).\\n\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"requiresLogin\":false,\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/qwen-qwen2_5-7b-instruct\",\"playground\":{\"type\":\"chat\"},\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: The trial service is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. Use of this model is governed by the \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003e NVIDIA Open Model License Agreement\u003c/a\u003e. Additional Information: \u003ca href=\\\"https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eApache License Version 2.0\u003c/a\u003e\\n\",\"cta\":{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/containers/qwen-2.5-7b-instruct\"}},\"artifactName\":\"qwen2_5-7b-instruct\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"21f222e5-c9ab-45ff-a039-65f3e8e4f820\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"Drug Discovery\",\"Molecular_Generation\",\"Drug Discovery\"],\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/genmol-generate.jpg\",\"shortDescription\":\"Fragment-Based Molecular Generation by Discrete Diffusion.\",\"isReadOnly\":true,\"description\":\"$42\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2025-01-13T16:45:16.568Z\",\"publisher\":\"nvidia\",\"displayName\":\"genmol\",\"name\":\"genmol-generate\",\"updatedDate\":\"2025-01-13T18:34:18.794Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for GenMol\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim for more details.\",\"termsOfService\":\"https://nvidia.com/legal/terms-of-use\",\"contact\":{\"name\":\"NVIDIA Support\",\"url\":\"https://help.nvidia.com/\"},\"version\":\"1.0.0\",\"license\":{\"name\":\"AI Foundation Models Community License Agreement\",\"url\":\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\"}},\"servers\":[{\"url\":\"https://health.api.nvidia.com/v1/\"}],\"paths\":{\"/biology/nvidia/genmol/generate\":{\"post\":{\"tags\":[\"Default\"],\"summary\":\"Molecular Generation\",\"description\":\"Sample molecules\",\"operationId\":\"molecular_generation\",\"requestBody\":{\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/MolecularGenerationRequest\"}}},\"required\":true},\"responses\":{\"200\":{\"description\":\"Successful Response\",\"content\":{\"application/json\":{\"schema\":{}}}},\"422\":{\"description\":\"Validation Error\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/HTTPValidationError\"}}}}},\"x-nvai-meta\":{\"name\":\"Generate molecules\",\"returns\":\"Returns generated molecules from a template.\",\"path\":\"generate\",\"examples\":[{\"name\":\"Generate molecules\",\"requestJson\":\"{'smiles':'C124CN3C1.S3(=O)(=O)CC.C4C#N.[*{20-20}]'}\",\"responseJson\":\"{}\"}],\"templates\":[{\"title\":\"Synchronous requests\",\"requestEjs\":{\"python\":\"import requests\\n\\ninvoke_url = \\\"https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate\\\"\\n\\nheaders = {\\n \\\"Authorization\\\": \\\"Bearer $NVIDIA_API_KEY\\\",\\n \\\"Accept\\\": \\\"application/json\\\",\\n}\\n\\npayload = {\\n \\\"smiles\\\": \\\"\u003c%- request.smiles %\u003e\\\",\\n \\\"num_molecules\\\": \u003c%- request.num_molecules %\u003e,\\n \\\"temperature\\\": \u003c%- request.temperature %\u003e,\\n \\\"noise\\\": \u003c%- request.noise %\u003e,\\n \\\"step_size\\\":\u003c%- request.step_size %\u003e,\\n \\\"scoring\\\": \\\"\u003c%- request.scoring %\u003e\\\"\\n}\\n\\n# re-use connections\\nsession = requests.Session()\\n\\nresponse = session.post(invoke_url, headers=headers, json=payload)\\n\\nresponse.raise_for_status()\\nresponse_body = response.json()\\nprint(response_body)\",\"curl\":\"#!/usr/bin/env bash\\n\\ninvoke_url='https://health.api.nvidia.com/v1/biology/nvidia/genmol/generate'\\n\\nauthorization_header='Authorization: Bearer $NVIDIA_API_KEY'\\ncontent_type_header='Content-Type: application/json'\\n\\ndata='{\\n \\\"smiles\\\": \\\"\u003c%- request.smiles %\u003e\\\",\\n \\\"num_molecules\\\": \\\"\u003c%- request.num_molecules %\u003e\\\",\\n \\\"temperature\\\": \\\"\u003c%- request.temperature %\u003e\\\",\\n \\\"noise\\\": \\\"\u003c%- request.noise %\u003e\\\",\\n \\\"step_size\\\":\\\"\u003c%- request.step_size %\u003e\\\",\\n \\\"scoring\\\": \\\"\u003c%- request.scoring %\u003e\\\"\\n}'\\n\\nresponse=$(curl --silent -i -w \\\"%{http_code}\\\" --request POST \\\\\\n --url \\\"$invoke_url\\\" \\\\\\n --header \\\"$authorization_header\\\" \\\\\\n --header \\\"$content_type_header\\\" \\\\\\n --data \\\"$data\\\"\\\\\\n)\\n\\necho \\\"$response\\\"\"}}]}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"HTTPValidationError\":{\"properties\":{\"detail\":{\"items\":{\"$ref\":\"#/components/schemas/ValidationError\"},\"type\":\"array\",\"title\":\"Detail\"}},\"type\":\"object\",\"title\":\"HTTPValidationError\"},\"MolecularGenerationRequest\":{\"properties\":{\"smiles\":{\"type\":\"string\",\"title\":\"Molecule Sequence\",\"description\":\"Molecular SMILES or SAFE sequence with masking segments in the form of \\\"[*{min_tokens-max_tokens}]\\\"\",\"default\":\"C124CN3C1.S3(=O)(=O)CC.C4C#N.[*{20-20}]\"},\"num_molecules\":{\"type\":\"integer\",\"maximum\":1000,\"minimum\":1,\"title\":\"Number of Molecules\",\"description\":\"Number of molecules to be generated, which may be larger than the number of returned molecules because invalid molecules are removed.\",\"default\":30},\"temperature\":{\"type\":\"float\",\"maximum\":10,\"minimum\":0.01,\"title\":\"Temperature Factor\",\"description\":\"Temperature scaling factor for Softmax sampling\",\"default\":1},\"noise\":{\"type\":\"float\",\"maximum\":2,\"minimum\":0,\"title\":\"Noise Factor\",\"description\":\"Noise factor for top-k sampling\",\"default\":1},\"step_size\":{\"type\":\"integer\",\"maximum\":10,\"minimum\":1,\"title\":\"Diffusion Step\",\"description\":\"Diffusion step size - the number of masking tokens recovered by each inference.\",\"default\":1},\"scoring\":{\"type\":\"string\",\"title\":\"Scoring Method\",\"enum\":[\"QED\",\"LogP\"],\"description\":\"Type of scores for ranking\",\"default\":\"QED\"},\"unique\":{\"type\":\"boolean\",\"title\":\"Unique Molecules\",\"description\":\"Return unique molecules only?\",\"default\":false}},\"type\":\"object\",\"required\":[\"smiles\"],\"title\":\"MolecularGenerationRequest\"},\"ValidationError\":{\"properties\":{\"loc\":{\"items\":{\"anyOf\":[{\"type\":\"string\"},{\"type\":\"integer\"}]},\"type\":\"array\",\"title\":\"Location\"},\"msg\":{\"type\":\"string\",\"title\":\"Message\"},\"type\":{\"type\":\"string\",\"title\":\"Error Type\"}},\"type\":\"object\",\"required\":[\"loc\",\"msg\",\"type\"],\"title\":\"ValidationError\"}}}},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-01-13T18:34:19.426Z\",\"nvcfFunctionId\":\"055a0869-9931-482a-ae48-53542c70baee\",\"createdDate\":\"2025-01-13T16:45:16.834Z\",\"attributes\":{\"dockerRun\":\"$43\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"requiresLogin\":false,\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/nvidia-genmol\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: Your use of this API is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Service Terms of Use\u003c/a\u003e; and the use of this model is governed by the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Foundation Models Community License\u003c/a\u003e and \u003ca href=\\\"https://opensource.org/license/MIT\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eMIT License\u003c/a\u003e.\\n\",\"cta\":{\"text\":\"Build with this NIM\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/genmol\"}},\"artifactName\":\"genmol-generate\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"4f0a75cf-f0a7-4107-9af7-7ab4c86887f6\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"Embedding\",\"nemo retriever\",\"Retrieval Augmented Generation\",\"Text-to-Embedding\"],\"bias\":\"| Field | Response |\\n| ----- | ----- |\\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\\n| Measures taken to mitigate against unwanted bias | None |\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_2-nv-embedqa-1b-v2.jpg\",\"shortDescription\":\"Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.\",\"safetyAndSecurity\":\"| Field | Response |\\n| ----- | ----- |\\n| Model Application(s): | Text Embedding for Retrieval |\\n| Describe the physical safety impact (if present). | Not Applicable |\\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). |\\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |\",\"privacy\":\"$44\",\"isReadOnly\":true,\"description\":\"$45\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2024-12-16T21:01:03.160Z\",\"publisher\":\"nvidia\",\"displayName\":\"llama-3.2-nv-embedqa-1b-v2\",\"name\":\"llama-3_2-nv-embedqa-1b-v2\",\"explainability\":\"$46\",\"updatedDate\":\"2025-02-07T00:03:52.310Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for nvidia/llama-3.2-nv-embedqa-1b-v2\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_2-nv-embedqa-1b-v2 for more details.\",\"version\":\"1.0.0\",\"termsOfService\":\"https://nvidia.com/legal/terms-of-use\",\"contact\":{\"name\":\"NVIDIA Support\",\"url\":\"https://help.nvidia.com/\"}},\"servers\":[{\"url\":\"https://integrate.api.nvidia.com/v1\"}],\"paths\":{\"/embeddings\":{\"post\":{\"tags\":[\"Embeddings\"],\"summary\":\"Creates an embedding vector from the input text.\",\"operationId\":\"create_embedding\",\"requestBody\":{\"required\":true,\"content\":{\"application/json\":{\"schema\":{\"type\":\"object\",\"properties\":{\"input\":{\"description\":\"Input text to embed. Max length is 8192 tokens.\",\"oneOf\":[{\"type\":\"string\"},{\"items\":{\"type\":\"string\"},\"type\":\"array\"}],\"minLength\":1,\"maxLength\":8192,\"title\":\"Input\"},\"model\":{\"type\":\"string\",\"description\":\"ID of the embedding model.\",\"example\":\"nvidia/llama-3.2-nv-embedqa-1b-v2\",\"default\":\"nvidia/llama-3.2-nv-embedqa-1b-v2\",\"title\":\"Model\"},\"input_type\":{\"type\":\"string\",\"enum\":[\"passage\",\"query\"],\"description\":\"nvidia/llama-3.2-nv-embedqa-1b-v2 operates in `passage` or `query` mode, and thus require the `input_type` parameter. `passage` is used when generating embeddings during indexing. `query` is used when generating embeddings during querying. It is very important to use the correct `input_type`. Failure to do so will result in large drops in retrieval accuracy.\",\"title\":\"Input Type\"},\"encoding_format\":{\"type\":\"string\",\"description\":\"The format to return the embeddings in.\",\"enum\":[\"float\",\"base64\"],\"default\":\"float\",\"title\":\"Encoding Format\"},\"truncate\":{\"type\":\"string\",\"description\":\"Specifies how inputs longer than the maximum token length of the model are handled. Passing `START` discards the start of the input. `END` discards the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model. If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.\",\"enum\":[\"NONE\",\"START\",\"END\"],\"default\":\"NONE\",\"title\":\"Truncate\"},\"user\":{\"type\":\"string\",\"description\":\"Not implemented, but provided for API compliance. This field is ignored.\",\"title\":\"User\"}},\"required\":[\"input\",\"model\"]}}}},\"x-nvai-meta\":{\"name\":\"Create Text Embedding\",\"description\":\"Generates an embedding vector from the provided text\\nusing a specified model. The embedding can be returned\\nin either float array or base64-encoded format.\\n\",\"path\":\"create\",\"examples\":[{\"name\":\"Embedding vector for text input\",\"requestJson\":\"{\\n \\\"input\\\": \\\"What is the capital of France?\\\",\\n \\\"model\\\": \\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\",\\n \\\"input_type\\\": \\\"query\\\",\\n \\\"encoding_format\\\": \\\"float\\\",\\n \\\"truncate\\\": \\\"NONE\\\"\\n}\\n\",\"responseJson\":\"$47\"}],\"templates\":[{\"title\":\"Synchronous requests\",\"requestEjs\":{\"curl\":\"curl -X POST https://integrate.api.nvidia.com/v1/embeddings \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -H \\\"Authorization: Bearer $NVIDIA_API_KEY\\\" \\\\\\n -d '{\\n \\\"input\\\": [\\\"\u003c%- request.input.replaceAll('\\\"', '\\\\\\\\\\\"').replaceAll(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") %\u003e\\\"],\\n \\\"model\\\": \\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\",\\n \\\"input_type\\\": \\\"\u003c%- request.input_type %\u003e\\\",\\n \\\"encoding_format\\\": \\\"\u003c%- request.encoding_format %\u003e\\\",\\n \\\"truncate\\\": \\\"\u003c%- request.truncate %\u003e\\\"\\n }'\\n\",\"python\":\"from openai import OpenAI\\n\\nclient = OpenAI(\\n api_key=\\\"$NVIDIA_API_KEY\\\",\\n base_url=\\\"https://integrate.api.nvidia.com/v1\\\"\\n)\\n\\nresponse = client.embeddings.create(\\n input=[\u003c%- JSON.stringify(request.input) %\u003e],\\n model=\\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\",\\n encoding_format=\\\"\u003c%- request.encoding_format %\u003e\\\",\\n extra_body={\\\"input_type\\\": \\\"\u003c%- request.input_type %\u003e\\\", \\\"truncate\\\": \\\"\u003c%- request.truncate %\u003e\\\"}\\n)\\n\\nprint(response.data[0].embedding)\\n\",\"langchain\":\"from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings\\n\\nclient = NVIDIAEmbeddings(\\n model=\\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\", \\n api_key=\\\"$NVIDIA_API_KEY\\\", \\n truncate=\\\"\u003c%- request.truncate %\u003e\\\", \\n )\\n\\nembedding = client.embed_query(\u003c%- JSON.stringify(request.input) %\u003e)\\nprint(embedding)\\n\"},\"response\":\"$48\"}]},\"responses\":{\"200\":{\"description\":\"Successful response\",\"content\":{\"application/json\":{\"schema\":{\"type\":\"object\",\"properties\":{\"object\":{\"type\":\"string\",\"example\":\"list\"},\"data\":{\"type\":\"array\",\"items\":{\"$ref\":\"#/components/schemas/EmbeddingObject\"}},\"model\":{\"type\":\"string\",\"example\":\"nvidia/llama-3.2-nv-embedqa-1b-v2\"},\"usage\":{\"type\":\"object\",\"description\":\"Number of tokens\",\"properties\":{\"prompt_tokens\":{\"type\":\"integer\",\"example\":0},\"total_tokens\":{\"type\":\"integer\",\"example\":0}}}}}}}},\"400\":{\"description\":\"Bad request\",\"content\":{\"application/json\":{\"schema\":{\"type\":\"object\",\"properties\":{\"object\":{\"type\":\"string\"},\"message\":{\"type\":\"string\"},\"detail\":{\"type\":\"object\",\"additionalProperties\":true},\"type\":{\"type\":\"string\"}}}}}}}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"EmbeddingObject\":{\"type\":\"object\",\"properties\":{\"object\":{\"type\":\"string\",\"example\":\"embedding\"},\"embedding\":{\"oneOf\":[{\"items\":{\"type\":\"number\"},\"type\":\"array\",\"description\":\"The embedding vector as a list of floats. The length of the vector depends on the model.\"},{\"type\":\"string\",\"description\":\"The embedding vector as a Base64 string. The length of the string depends on the model.\"}]},\"index\":{\"type\":\"integer\",\"description\":\"The index of the embedding in the list of embeddings.\"}},\"required\":[\"object\",\"embedding\",\"index\"]}}}},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-02-07T00:03:52.971Z\",\"nvcfFunctionId\":\"08d83ec3-339c-4923-b64c-2da76e684a14\",\"createdDate\":\"2024-12-16T21:01:03.611Z\",\"attributes\":{\"dockerRun\":\"Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.\\n```bash\\nexport NGC_API_KEY=\u003cPASTE_API_KEY_HERE\u003e\\nexport LOCAL_NIM_CACHE=~/.cache/nim\\nmkdir -p \\\"$LOCAL_NIM_CACHE\\\"\\ndocker run -it --rm \\\\\\n --gpus all \\\\\\n --shm-size=16GB \\\\\\n -e NGC_API_KEY \\\\\\n -v \\\"$LOCAL_NIM_CACHE:/opt/nim/.cache\\\" \\\\\\n -u $(id -u) \\\\\\n -p 8000:8000 \\\\\\n nvcr.io/nim/nvidia/llama-3.2-nv-embedqa-1b-v2:latest\\n```\\n\\nYou can now make a local API call using this curl command:\\n```bash\\ncurl -X \\\"POST\\\" \\\\\\n \\\"http://localhost:8000/v1/embeddings\\\" \\\\\\n -H 'accept: application/json' \\\\\\n -H 'Content-Type: application/json' \\\\\\n -d '{\\n \\\"input\\\": [\\\"Hello world\\\"],\\n \\\"model\\\": \\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\",\\n \\\"input_type\\\": \\\"query\\\"\\n}'\\n```\\n\\nFor more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/overview.html).\\n\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_2-nv-embedqa-1b-v2\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: This trial service is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. Use of this model is governed by the \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Model License\u003c/a\u003e. ADDITIONAL INFORMATION: \u003ca href=\\\"https://www.llama.com/llama3_2/license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eLlama 3.2 Community License Agreement\u003c/a\u003e. Built with Llama.\\n\",\"playground\":{\"type\":\"custom\",\"input\":{\"items\":[{\"key\":\"input\",\"type\":\"text-area\"},{\"key\":\"input_type\",\"type\":\"select\"},{\"key\":\"encoding_format\",\"type\":\"select\"},{\"key\":\"truncate\",\"type\":\"select\"}]},\"parameters\":{\"omitProperties\":[\"input_type\",\"encoding_format\",\"truncate\"]},\"output\":{\"items\":[{\"key\":\"data[0].embedding\",\"type\":\"code\",\"language\":\"javascript\",\"formatAsJson\":true}]},\"requestBody\":\"{ \\\"input\\\": [\\\"\u003c%- request.input %\u003e\\\"], \\\"model\\\": \\\"nvidia/llama-3.2-nv-embedqa-1b-v2\\\", \\\"input_type\\\": \\\"\u003c%- request.input_type %\u003e\\\", \\\"encoding_format\\\": \\\"\u003c%- request.encoding_format %\u003e\\\", \\\"truncate\\\": \\\"\u003c%- request.truncate %\u003e\\\" }\"},\"cta\":{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.2-nv-embedqa-1b-v2\"}},\"artifactName\":\"llama-3_2-nv-embedqa-1b-v2\"}},{\"endpoint\":{\"requestStatus\":{\"statusCode\":\"SUCCESS\",\"requestId\":\"dc50ec87-2359-468c-8558-8312ba853861\"},\"artifact\":{\"orgName\":\"qc69jvmznzxy\",\"labels\":[\"nemo retriever\",\"reranking\",\"Retrieval Augmented Generation\"],\"bias\":\"| Field | Response |\\n| ----- | ----- |\\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\\n| Measures taken to mitigate against unwanted bias | None |\",\"logo\":\"https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_2-nv-rerankqa-1b-v2.jpg\",\"shortDescription\":\"Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.\",\"safetyAndSecurity\":\"| Field | Response |\\n| ----- | ----- |\\n| Model Application(s): | Text Reranking for Retrieval |\\n| Describe the physical safety impact (if present). | Not Applicable |\\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). |\\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |\",\"privacy\":\"$49\",\"isReadOnly\":true,\"description\":\"$4a\",\"canGuestDownload\":true,\"isPublic\":true,\"createdDate\":\"2024-12-16T21:01:00.012Z\",\"publisher\":\"nvidia\",\"displayName\":\"llama-3.2-nv-rerankqa-1b-v2\",\"name\":\"llama-3_2-nv-rerankqa-1b-v2\",\"explainability\":\"$4b\",\"updatedDate\":\"2025-02-07T00:03:53.945Z\",\"attributes\":[{\"key\":\"AVAILABLE\",\"value\":\"true\"},{\"key\":\"PREVIEW\",\"value\":\"false\"}],\"artifactType\":\"ENDPOINT\"}},\"spec\":{\"openAPISpec\":{\"openapi\":\"3.1.0\",\"info\":{\"title\":\"NVIDIA NIM API for nvidia/llama-3.2-nv-rerankqa-1b-v2\",\"description\":\"The NVIDIA NIM REST API. Please see https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_2-nv-rerankqa-1b-v2 for more details.\",\"version\":\"1.0.0\",\"termsOfService\":\"https://nvidia.com/legal/terms-of-use\",\"contact\":{\"name\":\"NVIDIA Support\",\"url\":\"https://help.nvidia.com/\"}},\"servers\":[{\"url\":\"https://ai.api.nvidia.com/v1\"}],\"paths\":{\"/retrieval/nvidia/llama-3_2-nv-rerankqa-1b-v2/reranking\":{\"post\":{\"tags\":[\"Reranking\"],\"summary\":\"Rank passages by their relation to a query.\",\"description\":\"Order a list of passages by their relation to a query.\\n\\nNote that if a given query + passage pair is longer than the ranking model's context window,\\nit will be automatically truncated.\",\"operationId\":\"rank\",\"requestBody\":{\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/RankRequest\"}}},\"required\":true},\"x-nvai-meta\":{\"name\":\"Passage Reranking\",\"description\":\"Ranks a list of passages based on their relevance to a provided query using a specific model.\",\"path\":\"create\",\"examples\":[{\"name\":\"Passage Reranking\",\"description\":\"Ranks a list of passages based on their relevance to a provided query using a specific model.\",\"requestJson\":\"$4c\",\"responseJson\":\"{\\n \\\"rankings\\\": [\\n {\\n \\\"index\\\": 2,\\n \\\"logit\\\": 0.226318359375\\n },\\n {\\n \\\"index\\\": 1,\\n \\\"logit\\\": -1.171875\\n },\\n {\\n \\\"index\\\": 0,\\n \\\"logit\\\": -1.5126953125\\n }\\n ]\\n}\\n\"},{\"name\":\"Passage Reranking\",\"description\":\"Ranks a list of passages based on their relevance to a provided query using a specific model.\",\"requestJson\":\"$4d\",\"responseJson\":\"{\\n \\\"rankings\\\": [\\n {\\n \\\"index\\\": 0,\\n \\\"logit\\\": 1.1728515625\\n },\\n {\\n \\\"index\\\": 2,\\n \\\"logit\\\": 0.8544921875\\n },\\n {\\n \\\"index\\\": 1,\\n \\\"logit\\\": 0.65234375\\n },\\n {\\n \\\"index\\\": 3,\\n \\\"logit\\\": -2.376953125\\n },\\n {\\n \\\"index\\\": 4,\\n \\\"logit\\\": -2.416015625\\n },\\n {\\n \\\"index\\\": 5,\\n \\\"logit\\\": -3.224609375\\n }\\n ]\\n}\\n\"}],\"templates\":[{\"title\":\"Synchronous requests\",\"requestEjs\":{\"python\":\"\",\"langchain\":\"from langchain_nvidia_ai_endpoints import NVIDIARerank\\nfrom langchain_core.documents import Document\\n\\nquery = \u003c%- JSON.stringify(request.query.text) %\u003e\\npassages = [\u003c% request.passages.forEach(function(passage) { %\u003e\\n \\\"\u003c%- passage.text %\u003e\\\", \u003c% }); %\u003e\\n]\\n\\nclient = NVIDIARerank(\\n model=\\\"\u003c%- request.model %\u003e\\\", \\n api_key=\\\"$NVIDIA_API_KEY\\\",\\n)\\n\\nresponse = client.compress_documents(\\n query=query,\\n documents=[Document(page_content=passage) for passage in passages]\\n)\\n\\nprint(f\\\"Most relevant: {response[0].page_content}\\\\nLeast relevant: {response[-1].page_content}\\\")\\n\",\"node.js\":\"\",\"curl\":\"\"}}]},\"responses\":{\"200\":{\"description\":\"The ranked list of texts.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/RankResponse\"}}}},\"402\":{\"description\":\"Payment Required\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/PaymentRequiredError\"}}}},\"422\":{\"description\":\"The request was invalid.\",\"content\":{\"application/json\":{\"schema\":{\"$ref\":\"#/components/schemas/HTTPValidationError\"}}}}}}}},\"security\":[{\"Token\":[]}],\"components\":{\"securitySchemes\":{\"Token\":{\"type\":\"http\",\"scheme\":\"bearer\"}},\"schemas\":{\"MultiModalData\":{\"properties\":{\"text\":{\"type\":\"string\",\"maxLength\":14598366,\"minLength\":1,\"pattern\":\".*\",\"title\":\"Text\"}},\"additionalProperties\":false,\"type\":\"object\",\"required\":[\"text\"],\"title\":\"MultiModalData\",\"description\":\"A type signifier for multimodal data.\\n\\nSupported data types: text.\"},\"Rank\":{\"properties\":{\"index\":{\"type\":\"integer\",\"maximum\":512,\"minimum\":0,\"format\":\"int32\",\"title\":\"Index\"},\"logit\":{\"type\":\"number\",\"title\":\"Logit\"}},\"type\":\"object\",\"required\":[\"index\",\"logit\"],\"title\":\"Rank\",\"description\":\"A single ranked text.\"},\"RankRequest\":{\"properties\":{\"model\":{\"description\":\"Model identifier\",\"type\":\"string\",\"maxLength\":128,\"minLength\":1,\"pattern\":\".*\",\"title\":\"Model\",\"default\":\"nvidia/llama-3.2-nv-rerankqa-1b-v2\",\"examples\":[\"nvidia/llama-3.2-nv-rerankqa-1b-v2\"]},\"query\":{\"allOf\":[{\"$ref\":\"#/components/schemas/MultiModalData\"}],\"description\":\"A text query for ranking the passages\",\"examples\":[{\"text\":\"What is the GPU memory bandwidth of H100 SXM?\"}]},\"passages\":{\"items\":{\"$ref\":\"#/components/schemas/MultiModalData\"},\"type\":\"array\",\"maxItems\":512,\"minItems\":1,\"title\":\"Passages\",\"description\":\"Text passages to rank based on the query\",\"examples\":[[{\"text\":\"The Hopper GPU is paired with the Grace CPU using NVIDIA's ultra-fast chip-to-chip interconnect, delivering 900GB/s of bandwidth, 7X faster than PCIe Gen5. This innovative design will deliver up to 30X higher aggregate system memory bandwidth to the GPU compared to today's fastest servers and up to 10X higher performance for applications running terabytes of data.\"},{\"text\":\"A100 provides up to 20X higher performance over the prior generation and can be partitioned into seven GPU instances to dynamically adjust to shifting demands. The A100 80GB debuts the world's fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.\"},{\"text\":\"Accelerated servers with H100 deliver the compute power—along with 3 terabytes per second (TB/s) of memory bandwidth per GPU and scalability with NVLink and NVSwitch™.\"}]]},\"truncate\":{\"type\":\"string\",\"enum\":[\"END\",\"NONE\"],\"title\":\"Truncate\",\"default\":\"NONE\"}},\"type\":\"object\",\"required\":[\"model\",\"query\",\"passages\"],\"title\":\"RankRequest\",\"description\":\"A request to the rank endpoint.\"},\"RankResponse\":{\"properties\":{\"rankings\":{\"items\":{\"$ref\":\"#/components/schemas/Rank\"},\"type\":\"array\",\"maxItems\":512,\"minItems\":1,\"title\":\"Rankings\",\"examples\":[[{\"index\":2,\"logit\":-0.226318359375},{\"index\":1,\"logit\":-1.171875},{\"index\":0,\"logit\":-1.5126953125}]]}},\"type\":\"object\",\"required\":[\"rankings\"],\"title\":\"RankResponse\",\"description\":\"A response from the rank endpoint.\"},\"ValidationError\":{\"properties\":{\"loc\":{\"items\":{\"anyOf\":[{\"type\":\"string\"},{\"type\":\"integer\"}]},\"type\":\"array\",\"title\":\"Location\"},\"msg\":{\"type\":\"string\",\"title\":\"Message\"},\"type\":{\"type\":\"string\",\"title\":\"Error Type\"}},\"type\":\"object\",\"required\":[\"loc\",\"msg\",\"type\"],\"title\":\"ValidationError\"},\"HTTPValidationError\":{\"properties\":{\"detail\":{\"items\":{\"$ref\":\"#/components/schemas/ValidationError\"},\"type\":\"array\",\"title\":\"Detail\"}},\"type\":\"object\",\"title\":\"HTTPValidationError\"},\"PaymentRequiredError\":{\"properties\":{\"detail\":{\"type\":\"string\",\"description\":\"Contains specific information related to the error and why it occurred.\",\"example\":\"You have reached your limit of credits.\"}},\"type\":\"object\",\"title\":\"PaymentRequiredError\"}}},\"tags\":[{\"name\":\"Reranking\",\"description\":\"Rank passages by their relation to a query.\"}]},\"namespace\":\"qc69jvmznzxy\",\"updatedDate\":\"2025-02-07T00:03:54.375Z\",\"nvcfFunctionId\":\"351b8fc5-bb49-467e-bfa2-f8d46fd1e8cb\",\"createdDate\":\"2024-12-16T21:01:00.475Z\",\"attributes\":{\"dockerRun\":\"$4e\",\"dockerTermsOfUse\":\"By running the below commands, you accept the \u003ca href=\\\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA AI Enterprise Terms of Use\u003c/a\u003e and the \u003ca href=\\\"https://docs.nvidia.com/ai-foundation-models-community-license.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Models License\u003c/a\u003e.\\n\",\"showUnavailableBanner\":false,\"apiDocsUrl\":\"https://docs.api.nvidia.com/nim/reference/nvidia-llama-3_2-nv-rerankqa-1b-v2\",\"termsOfUse\":\"\u003cb\u003eGOVERNING TERMS\u003c/b\u003e: This trial service is governed by the \u003ca href=\\\"https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA API Trial Terms of Service\u003c/a\u003e. Use of this model is governed by the \u003ca href=\\\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eNVIDIA Community Model License\u003c/a\u003e. ADDITIONAL INFORMATION: \u003ca href=\\\"https://www.llama.com/llama3_2/license/\\\" rel=\\\"noreferrer\\\" target=\\\"_blank\\\"\u003eLlama 3.2 Community License Agreement\u003c/a\u003e. Built with Llama.\\n\",\"playground\":{\"type\":\"rerank\"},\"cta\":{\"text\":\"Apply to Self-Host\",\"url\":\"https://www.nvidia.com/en-us/ai/nim-notifyme/\",\"nim_available_override_url\":\"https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/llama-3.2-nv-rerankqa-1b-v2\"}},\"artifactName\":\"llama-3_2-nv-rerankqa-1b-v2\"}}],\"filterValues\":\"$26\",\"omittedFilters\":[\"blueprintType\"],\"pageName\":\"Models\",\"subText\":\"Deploy and scale models on your GPU infrastructure of choice with NVIDIA NIM inference microservices\",\"children\":[\"$\",\"$L4f\",null,{}]}]}]}]}]\n"])</script><div hidden id="S:0"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><header class="flex flex-col flex-wrap gap-md p-0 sm:rounded-8 sm:rounded-b-none sm:bg-manitoulinDarkBlack sm:p-8 sm:pb-0"><h2 class="text-lg font-medium">Models</h2><span class="mb-md text-md md:mb-0">Deploy and scale models on your GPU infrastructure of choice with NVIDIA NIM inference microservices</span></header><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap sticky top-12 z-10 bg-manitoulinLightBlack max-sm:bg-transparent" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-dkuYHy-gap-ms c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap flex-1 flex-wrap p-0 sm:bg-manitoulinDarkBlack sm:px-8 sm:pt-6" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-XefLA-wrap-wrap" data-testid="kui-flex"><form class="min-w-[135px] flex-1"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-hNPEVI-gap-xs c-dhzjXW-awKDG-justify-flex-start 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>div{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}</style><div class="c-cxpKzu c-cxpKzu-iLkYCP-css css-3c60du-control"><style data-emotion="css 6jjltg">.css-6jjltg{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;display:grid;-webkit-flex:1;-ms-flex:1;flex:1;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-overflow-scrolling:touch;position:relative;overflow:hidden;padding:2px 8px;box-sizing:border-box;height:auto;}</style><div class="c-WUEIO css-6jjltg"><style data-emotion="css y67nyk-placeholder">.css-y67nyk-placeholder{grid-area:1/1/2/3;color:#767676;margin-left:2px;margin-right:2px;box-sizing:border-box;font-family:"NVIDIA Sans","NVIDIA Sans Fallback";}</style><div class=" css-y67nyk-placeholder" id="react-select-316-placeholder"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-knmidH-justify-space-between 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class="text-[length:inherit]"></svg>text: mistral-nemo</button><button class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row-reverse border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-b300 hover:bg-b700 aria-checked:bg-b200 active:bg-b200 px-3 h-6 gap-2 capitalize"><svg data-src="https://brand-assets.cne.ngc.nvidia.com/assets/icons/3.1.0/line/close.svg" height="1em" width="1em" display="inline-block" data-icon-name="close" class="text-[length:inherit]"></svg>text: mistral-nemo</button></div><div class="c-hakyQ" data-testid="kui-box"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-md btn-pill">Clear Filters</button></div></div><div class="c-hakyQ c-hakyQ-igPhOUD-css" data-testid="kui-box"></div></div></div></div><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap flex justify-end sm:hidden" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-iecTQhR-css" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-dQhbEp-gap-sm c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-iecTQhR-css" data-testid="kui-flex"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-secondary btn-md btn-pill btn-inverse ml-auto mt-sm md:ml-[0]"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-hNPEVI-gap-xs c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="16" height="16" display="inline-block" data-icon-name="filter"><path fill="currentColor" d="M13 6H3V5h10zm-2 3H5V8h6zm-2 3H7v-1h2z"></path></svg><span class="c-kAArxJ c-kAArxJ-idbzzBd-css" data-testid="kui-text">Filters (2)</span></div></button><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-secondary btn-md btn-pill btn-inverse ml-auto mt-sm md:ml-[0]"><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-hNPEVI-gap-xs c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-flex"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 16 16" width="16" height="16" display="inline-block" data-icon-name="arrow-up-down"><path fill="currentColor" d="m8 1.293 3.354 3.353-.707.708L8.5 3.207v9.586l2.146-2.147.708.707L8 14.708l-3.354-3.354.708-.707L7.5 12.793V3.207L5.354 5.354l-.708-.708z"></path></svg><span class="c-kAArxJ c-kAArxJ-idbzzBd-css" data-testid="kui-text">Sort</span></div></button></div></div><div class="c-dhzjXW c-dhzjXW-jroWjL-align-center c-dhzjXW-ejCoEP-direction-row c-dhzjXW-bZmKkd-justify-flex-end c-dhzjXW-kVNAnR-wrap-nowrap mb-sm flex md:hidden" data-testid="kui-flex"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-ejCoEP-direction-row c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap c-dhzjXW-ieaPeMQ-css" data-testid="kui-flex"><button class="inline-flex items-center justify-center gap-xs text-center font-sans font-medium leading-text flex-row btn-tertiary btn-sm btn-pill">Clear filters</button></div><span class="c-kAArxJ c-kAArxJ-cjiAHf-variant-label" data-testid="kui-text">Sorting by Most Recent</span></div></div></div><section class="flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack p-6 sm:rounded-6 md:rounded-8 md:p-8 gap-md px-4 pb-8 pt-4 sm:!rounded-t-none sm:px-8"><div class="c-dhzjXW c-dhzjXW-irEjuD-align-stretch c-dhzjXW-iTKOFX-direction-column c-dhzjXW-awKDG-justify-flex-start c-dhzjXW-kVNAnR-wrap-nowrap" data-testid="kui-stack"><div class=""><div class="grid grid-cols-1 gap-x-md overflow-hidden md:gap-x-lg lg:grid-cols-2"><div class="group/row contents" data-row="1"><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=350&q=90 350w, 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/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-small-24b-instruct.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/mistralai">mistralai</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="mistralai/mistral-small-24b-instruct" class="c-NtyHx" data-linkbox-overlay="true" href="/mistralai/mistral-small-24b-instruct">mistral-small-24b-instruct</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">Latency-optimized language model excelling in code, math, general knowledge, and instruction-following.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=code">code</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=reasoning">reasoning</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2qdufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+3</button></div></div></div></div><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=400&q=90 400w, 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/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-8k-instruct.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/nvidia">nvidia</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="nvidia/mistral-nemo-minitron-8b-8k-instruct" class="c-NtyHx" data-linkbox-overlay="true" href="/nvidia/mistral-nemo-minitron-8b-8k-instruct">mistral-nemo-minitron-8b-8k-instruct</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Small+Language+Model">small language model</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Code+Generation">code generation</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2sdufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+4</button></div></div></div></div></div><div class="group/row contents" data-row="2"><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=350&q=90 350w, 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/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-nemo-minitron-8b-base.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/nvidia">nvidia</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="nvidia/mistral-nemo-minitron-8b-base" class="c-NtyHx" data-linkbox-overlay="true" href="/nvidia/mistral-nemo-minitron-8b-base">mistral-nemo-minitron-8b-base</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">State-of-the-art small language model delivering superior accuracy for chatbot, virtual assistants, and content generation.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=language+generation">language generation</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=text-to-text">text-to-text</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2qlufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+3</button></div></div></div></div><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] 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leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/nv-mistralai">nv-mistralai</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="nv-mistralai/mistral-nemo-12b-instruct" class="c-NtyHx" data-linkbox-overlay="true" href="/nv-mistralai/mistral-nemo-12b-instruct">mistral-nemo-12b-instruct</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">Most advanced language model for reasoning, code, multilingual tasks; runs on a single GPU.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Code+Generation">code generation</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Chat">chat</a></div><button type="button" 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data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-rerankqa-mistral-4b-v3.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-rerankqa-mistral-4b-v3.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-rerankqa-mistral-4b-v3.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-rerankqa-mistral-4b-v3.jpg&w=400&q=90 400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-rerankqa-mistral-4b-v3.jpg&w=600&q=90 600w, 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class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="nvidia/nv-rerankqa-mistral-4b-v3" class="c-NtyHx" data-linkbox-overlay="true" href="/nvidia/nv-rerankqa-mistral-4b-v3">nv-rerankqa-mistral-4b-v3</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">Multilingual text reranking model.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=nemo+retriever">nemo retriever</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Retrieval+Augmented+Generation">retrieval augmented generation</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2qtufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center 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srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=400&q=90 400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=600&q=90 600w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=640&q=90 640w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fnv-embedqa-mistral-7b-v2.jpg&w=700&q=90 700w, 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href="/nvidia/nv-embedqa-mistral-7b-v2">nv-embedqa-mistral-7b-v2</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">Multilingual text question-answering retrieval, transforming textual information into dense vector representations.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=nemo+retriever">nemo retriever</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Embedding">embedding</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2stufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+2</button></div></div></div></div></div><div class="group/row contents" data-row="4"><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=400&q=90 400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=600&q=90 600w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=640&q=90 640w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=700&q=90 700w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=750&q=90 750w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=828&q=90 828w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=1080&q=90 1080w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v03.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/mistralai">mistralai</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="mistralai/mistral-7b-instruct-v03" class="c-NtyHx" data-linkbox-overlay="true" href="/mistralai/mistral-7b-instruct-v03">mistral-7b-instruct-v0.3</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">This LLM follows instructions, completes requests, and generates creative text.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Chat">chat</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Text-to-Text">text-to-text</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2r5ufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+2</button></div></div></div></div><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=400&q=90 400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=600&q=90 600w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=640&q=90 640w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=700&q=90 700w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=750&q=90 750w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=828&q=90 828w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=1080&q=90 1080w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Frerank-qa-mistral-4b.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/nvidia">nvidia</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="nvidia/rerank-qa-mistral-4b" class="c-NtyHx" data-linkbox-overlay="true" href="/nvidia/rerank-qa-mistral-4b">rerank-qa-mistral-4b</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Ranking">ranking</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Retrieval+Augmented+Generation">retrieval augmented generation</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R2t5ufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+1</button></div></div></div></div></div><div class="group/row contents" data-row="5"><div class="relative py-md after:absolute after:bottom-[-1px] after:h-[1px] after:w-full after:bg-n650 after:content-[''] md:py-lg lg:group-[:first-child]/row:pt-0 lg:group-[:last-child]/row:pb-0"><div data-testid="model-search-result" class="c-lbkdiW group grid h-auto w-full max-w-[1000px] grid-cols-[minmax(50px,120px)_1fr] items-start gap-x-md gap-y-sm md:grid-cols-[minmax(50px,200px)_1fr] md:gap-x-lg"><div class="relative aspect-4/3 max-h-[90px] max-w-[120px] overflow-hidden rounded-2xl border-sm border-[#2e2e2e] md:row-span-2 md:max-h-[150px] md:max-w-[200px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-cover object-center group-hover:animate-fade" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 150px, 300px" srcSet="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=150&q=90 150w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=300&q=90 300w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=350&q=90 350w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=400&q=90 400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=600&q=90 600w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=640&q=90 640w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=700&q=90 700w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=750&q=90 750w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=828&q=90 828w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=1080&q=90 1080w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=1200&q=90 1200w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=1400&q=90 1400w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=1920&q=90 1920w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=2048&q=90 2048w, /_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=3840&q=90 3840w" src="/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fmistral-7b-instruct-v2.jpg&w=3840&q=90"/></div><div><div class="mb-sm flex flex-col flex-wrap gap-sm tablet:flex-row md:mb-md md:flex-col xl:flex-col 2xl:flex-row"><h3 class="whitespace-normal text-mm font-normal leading-text text-n500 sm:whitespace-pre-wrap md:text-[20px] lg:whitespace-normal 2xl:whitespace-pre-wrap"><span class="font-light leading-heading text-n300" data-testid="publisher-name"><span class="lowercase hover:text-n050"><a href="/mistralai">mistralai</a></span><span class="mx-sm">/</span><span class="font-medium text-manitoulinLightWhite" data-testid="resource-name"><a data-nvtrack="Model viewed" data-nvtrack-category="Models" data-nvtrack-search-result-clicked="mistralai/mistral-7b-instruct-v2" class="c-NtyHx" data-linkbox-overlay="true" href="/mistralai/mistral-7b-instruct-v2">mistral-7b-instruct-v0.2</a></span></span></h3><div class="ml-0 flex flex-row flex-wrap gap-sm py-xs tablet:ml-ms lg:ml-[0]"></div></div><p class="mt-md text-ms font-normal leading-body text-n050">This LLM follows instructions, completes requests, and generates creative text.</p></div><div class="mt-auto pt-sm max-md:col-span-2 md:col-start-2"><div class="relative flex w-full items-center justify-start gap-2"><div class="flex items-center gap-2 overflow-hidden"><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Text-to-Text">text-to-text</a><a class="inline-flex min-w-fit items-center truncate rounded-full border align-bottom text-sm font-medium leading-text [&[readonly]]:pointer-events-none [&[readonly]]:cursor-default disabled:pointer-events-none disabled:text-n700 flex-row border-current aria-checked:border-transparent aria-checked:text-black active:text-black active:border-transparent disabled:border-n800 text-t300 hover:bg-t700 aria-checked:bg-t300 active:bg-t300 px-2 h-5 gap-1.5" href="/search?q=Language+Generation">language generation</a></div><button type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:R1fdufnd7qcq:" data-state="closed" data-testid="kui-popover-trigger" class="inline-flex items-center justify-center gap-xs text-center font-sans leading-text flex-row btn-tertiary btn-xs btn-pill btn-inverse c-PJLV font-medium">+2</button></div></div></div></div></div></div></div></div></section></div></div><script>$RC=function(b,c,e){c=document.getElementById(c);c.parentNode.removeChild(c);var a=document.getElementById(b);if(a){b=a.previousSibling;if(e)b.data="$!",a.setAttribute("data-dgst",e);else{e=b.parentNode;a=b.nextSibling;var f=0;do{if(a&&8===a.nodeType){var d=a.data;if("/$"===d)if(0===f)break;else f--;else"$"!==d&&"$?"!==d&&"$!"!==d||f++}d=a.nextSibling;e.removeChild(a);a=d}while(a);for(;c.firstChild;)e.insertBefore(c.firstChild,a);b.data="$"}b._reactRetry&&b._reactRetry()}};$RC("B:0","S:0")</script></body></html>