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class=\"heading-element\" dir=\"auto\"\u003eLanguage Model Evaluation Harness\u003c/h1\u003e\u003ca id=\"user-content-language-model-evaluation-harness\" class=\"anchor\" aria-label=\"Permalink: Language Model Evaluation Harness\" href=\"#language-model-evaluation-harness\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://doi.org/10.5281/zenodo.10256836\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2b03c2ae010ea9e7319f8e7d149b83ad3fdba1486fd345de2dcfbe885ad4e09e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e31303235363833362e737667\" alt=\"DOI\" data-canonical-src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.10256836.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eLatest News 📣\u003c/em\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024/09] We are prototyping allowing users of LM Evaluation Harness to create and evaluate on text+image multimodal input, text output tasks, and have just added the \u003ccode\u003ehf-multimodal\u003c/code\u003e and \u003ccode\u003evllm-vlm\u003c/code\u003e model types and \u003ccode\u003emmmu\u003c/code\u003e task as a prototype feature. We welcome users to try out this in-progress feature and stress-test it for themselves, and suggest they check out \u003ca href=\"https://github.com/EvolvingLMMs-Lab/lmms-eval\"\u003e\u003ccode\u003elmms-eval\u003c/code\u003e\u003c/a\u003e, a wonderful project originally forking off of the lm-evaluation-harness, for a broader range of multimodal tasks, models, and features.\u003c/li\u003e\n\u003cli\u003e[2024/07] \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/docs/API_guide.md\"\u003eAPI model\u003c/a\u003e support has been updated and refactored, introducing support for batched and async requests, and making it significantly easier to customize and use for your own purposes. \u003cstrong\u003eTo run Llama 405B, we recommend using VLLM's OpenAI-compliant API to host the model, and use the \u003ccode\u003elocal-completions\u003c/code\u003e model type to evaluate the model.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e[2024/07] New Open LLM Leaderboard tasks have been added ! You can find them under the \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/leaderboard/README.md\"\u003eleaderboard\u003c/a\u003e task group.\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAnnouncement\u003c/h2\u003e\u003ca id=\"user-content-announcement\" class=\"anchor\" aria-label=\"Permalink: Announcement\" href=\"#announcement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eA new v0.4.0 release of lm-evaluation-harness is available\u003c/strong\u003e !\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNew updates and features include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eNew Open LLM Leaderboard tasks have been added ! You can find them under the \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/leaderboard/README.md\"\u003eleaderboard\u003c/a\u003e task group.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eInternal refactoring\u003c/li\u003e\n\u003cli\u003eConfig-based task creation and configuration\u003c/li\u003e\n\u003cli\u003eEasier import and sharing of externally-defined task config YAMLs\u003c/li\u003e\n\u003cli\u003eSupport for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource\u003c/li\u003e\n\u003cli\u003eMore advanced configuration options, including output post-processing, answer extraction, and multiple LM generations per document, configurable fewshot settings, and more\u003c/li\u003e\n\u003cli\u003eSpeedups and new modeling libraries supported, including: faster data-parallel HF model usage, vLLM support, MPS support with HuggingFace, and more\u003c/li\u003e\n\u003cli\u003eLogging and usability changes\u003c/li\u003e\n\u003cli\u003eNew tasks including CoT BIG-Bench-Hard, Belebele, user-defined task groupings, and more\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePlease see our updated documentation pages in \u003ccode\u003edocs/\u003c/code\u003e for more details.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDevelopment will be continuing on the \u003ccode\u003emain\u003c/code\u003e branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub, or in the \u003ca href=\"https://discord.gg/eleutherai\" rel=\"nofollow\"\u003eEleutherAI discord\u003c/a\u003e!\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project provides a unified framework to test generative language models on a large number of different evaluation tasks.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFeatures:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOver 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants implemented.\u003c/li\u003e\n\u003cli\u003eSupport for models loaded via \u003ca href=\"https://github.com/huggingface/transformers/\"\u003etransformers\u003c/a\u003e (including quantization via \u003ca href=\"https://github.com/ModelCloud/GPTQModel\"\u003eGPTQModel\u003c/a\u003e and \u003ca href=\"https://github.com/PanQiWei/AutoGPTQ\"\u003eAutoGPTQ\u003c/a\u003e), \u003ca href=\"https://github.com/EleutherAI/gpt-neox\"\u003eGPT-NeoX\u003c/a\u003e, and \u003ca href=\"https://github.com/microsoft/Megatron-DeepSpeed/\"\u003eMegatron-DeepSpeed\u003c/a\u003e, with a flexible tokenization-agnostic interface.\u003c/li\u003e\n\u003cli\u003eSupport for fast and memory-efficient inference with \u003ca href=\"https://github.com/vllm-project/vllm\"\u003evLLM\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eSupport for commercial APIs including \u003ca href=\"https://openai.com\" rel=\"nofollow\"\u003eOpenAI\u003c/a\u003e, and \u003ca href=\"https://textsynth.com/\" rel=\"nofollow\"\u003eTextSynth\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eSupport for evaluation on adapters (e.g. LoRA) supported in \u003ca href=\"https://github.com/huggingface/peft\"\u003eHuggingFace's PEFT library\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eSupport for local models and benchmarks.\u003c/li\u003e\n\u003cli\u003eEvaluation with publicly available prompts ensures reproducibility and comparability between papers.\u003c/li\u003e\n\u003cli\u003eEasy support for custom prompts and evaluation metrics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe Language Model Evaluation Harness is the backend for 🤗 Hugging Face's popular \u003ca href=\"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\" rel=\"nofollow\"\u003eOpen LLM Leaderboard\u003c/a\u003e, has been used in \u003ca href=\"https://scholar.google.com/scholar?oi=bibs\u0026amp;hl=en\u0026amp;authuser=2\u0026amp;cites=15052937328817631261,4097184744846514103,1520777361382155671,17476825572045927382,18443729326628441434,14801318227356878622,7890865700763267262,12854182577605049984,15641002901115500560,5104500764547628290\" rel=\"nofollow\"\u003ehundreds of papers\u003c/a\u003e, and is used internally by dozens of organizations including NVIDIA, Cohere, BigScience, BigCode, Nous Research, and Mosaic ML.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the \u003ccode\u003elm-eval\u003c/code\u003e package from the github repository, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness\ncd lm-evaluation-harness\npip install -e .\"\u003e\u003cpre\u003egit clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e lm-evaluation-harness\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe also provide a number of optional dependencies for extended functionality. A detailed table is available at the end of this document.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBasic Usage\u003c/h2\u003e\u003ca id=\"user-content-basic-usage\" class=\"anchor\" aria-label=\"Permalink: Basic Usage\" href=\"#basic-usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUser Guide\u003c/h3\u003e\u003ca id=\"user-content-user-guide\" class=\"anchor\" aria-label=\"Permalink: User Guide\" href=\"#user-guide\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA user guide detailing the full list of supported arguments is provided \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md\"\u003ehere\u003c/a\u003e, and on the terminal by calling \u003ccode\u003elm_eval -h\u003c/code\u003e. Alternatively, you can use \u003ccode\u003elm-eval\u003c/code\u003e instead of \u003ccode\u003elm_eval\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA list of supported tasks (or groupings of tasks) can be viewed with \u003ccode\u003elm-eval --tasks list\u003c/code\u003e. Task descriptions and links to corresponding subfolders are provided \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/README.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHugging Face \u003ccode\u003etransformers\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-hugging-face-transformers\" class=\"anchor\" aria-label=\"Permalink: Hugging Face transformers\" href=\"#hugging-face-transformers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo evaluate a model hosted on the \u003ca href=\"https://huggingface.co/models\" rel=\"nofollow\"\u003eHuggingFace Hub\u003c/a\u003e (e.g. GPT-J-6B) on \u003ccode\u003ehellaswag\u003c/code\u003e you can use the following command (this assumes you are using a CUDA-compatible GPU):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6B \\\n --tasks hellaswag \\\n --device cuda:0 \\\n --batch_size 8\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6B \\\n --tasks hellaswag \\\n --device cuda:0 \\\n --batch_size 8\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAdditional arguments can be provided to the model constructor using the \u003ccode\u003e--model_args\u003c/code\u003e flag. Most notably, this supports the common practice of using the \u003ccode\u003erevisions\u003c/code\u003e feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=\u0026quot;float\u0026quot; \\\n --tasks lambada_openai,hellaswag \\\n --device cuda:0 \\\n --batch_size 8\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003efloat\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tasks lambada_openai,hellaswag \\\n --device cuda:0 \\\n --batch_size 8\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eModels that are loaded via both \u003ccode\u003etransformers.AutoModelForCausalLM\u003c/code\u003e (autoregressive, decoder-only GPT style models) and \u003ccode\u003etransformers.AutoModelForSeq2SeqLM\u003c/code\u003e (such as encoder-decoder models like T5) in Huggingface are supported.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBatch size selection can be automated by setting the \u003ccode\u003e--batch_size\u003c/code\u003e flag to \u003ccode\u003eauto\u003c/code\u003e. This will perform automatic detection of the largest batch size that will fit on your device. On tasks where there is a large difference between the longest and shortest example, it can be helpful to periodically recompute the largest batch size, to gain a further speedup. To do this, append \u003ccode\u003e:N\u003c/code\u003e to above flag to automatically recompute the largest batch size \u003ccode\u003eN\u003c/code\u003e times. For example, to recompute the batch size 4 times, the command would be:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=\u0026quot;float\u0026quot; \\\n --tasks lambada_openai,hellaswag \\\n --device cuda:0 \\\n --batch_size auto:4\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003efloat\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n --tasks lambada_openai,hellaswag \\\n --device cuda:0 \\\n --batch_size auto:4\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eJust like you can provide a local path to \u003ccode\u003etransformers.AutoModel\u003c/code\u003e, you can also provide a local path to \u003ccode\u003elm_eval\u003c/code\u003e via \u003ccode\u003e--model_args pretrained=/path/to/model\u003c/code\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMulti-GPU Evaluation with Hugging Face \u003ccode\u003eaccelerate\u003c/code\u003e\u003c/h4\u003e\u003ca id=\"user-content-multi-gpu-evaluation-with-hugging-face-accelerate\" class=\"anchor\" aria-label=\"Permalink: Multi-GPU Evaluation with Hugging Face accelerate\" href=\"#multi-gpu-evaluation-with-hugging-face-accelerate\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe support three main ways of using Hugging Face's \u003ca href=\"https://github.com/huggingface/accelerate\"\u003eaccelerate 🚀\u003c/a\u003e library for multi-GPU evaluation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo perform \u003cem\u003edata-parallel evaluation\u003c/em\u003e (where each GPU loads a \u003cstrong\u003eseparate full copy\u003c/strong\u003e of the model), we leverage the \u003ccode\u003eaccelerate\u003c/code\u003e launcher as follows:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"accelerate launch -m lm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --batch_size 16\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eaccelerate launch -m lm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --batch_size 16\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e(or via \u003ccode\u003eaccelerate launch --no-python lm_eval\u003c/code\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor cases where your model can fit on a single GPU, this allows you to evaluate on K GPUs K times faster than on one.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWARNING\u003c/strong\u003e: This setup does not work with FSDP model sharding, so in \u003ccode\u003eaccelerate config\u003c/code\u003e FSDP must be disabled, or the NO_SHARD FSDP option must be used.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe second way of using \u003ccode\u003eaccelerate\u003c/code\u003e for multi-GPU evaluation is when your model is \u003cem\u003etoo large to fit on a single GPU.\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn this setting, run the library \u003cem\u003eoutside the \u003ccode\u003eaccelerate\u003c/code\u003e launcher\u003c/em\u003e, but passing \u003ccode\u003eparallelize=True\u003c/code\u003e to \u003ccode\u003e--model_args\u003c/code\u003e as follows:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --model_args parallelize=True \\\n --batch_size 16\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003elm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --model_args parallelize=True \\\n --batch_size 16\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis means that your model's weights will be split across all available GPUs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more advanced users or even larger models, we allow for the following arguments when \u003ccode\u003eparallelize=True\u003c/code\u003e as well:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003edevice_map_option\u003c/code\u003e: How to split model weights across available GPUs. defaults to \"auto\".\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emax_memory_per_gpu\u003c/code\u003e: the max GPU memory to use per GPU in loading the model.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003emax_cpu_memory\u003c/code\u003e: the max amount of CPU memory to use when offloading the model weights to RAM.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eoffload_folder\u003c/code\u003e: a folder where model weights will be offloaded to disk if needed.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe third option is to use both at the same time. This will allow you to take advantage of both data parallelism and model sharding, and is especially useful for models that are too large to fit on a single GPU.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"accelerate launch --multi_gpu --num_processes {nb_of_copies_of_your_model} \\\n -m lm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --model_args parallelize=True \\\n --batch_size 16\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eaccelerate launch --multi_gpu --num_processes {nb_of_copies_of_your_model} \\\n -m lm_eval --model hf \\\n --tasks lambada_openai,arc_easy \\\n --model_args parallelize=True \\\n --batch_size 16\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo learn more about model parallelism and how to use it with the \u003ccode\u003eaccelerate\u003c/code\u003e library, see the \u003ca href=\"https://huggingface.co/docs/transformers/v4.15.0/en/parallelism\" rel=\"nofollow\"\u003eaccelerate documentation\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWarning: We do not natively support multi-node evaluation using the \u003ccode\u003ehf\u003c/code\u003e model type! Please reference \u003ca href=\"https://github.com/EleutherAI/gpt-neox/blob/main/eval.py\"\u003eour GPT-NeoX library integration\u003c/a\u003e for an example of code in which a custom multi-machine evaluation script is written.\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote: we do not currently support multi-node evaluations natively, and advise using either an externally hosted server to run inference requests against, or creating a custom integration with your distributed framework \u003ca href=\"https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py\"\u003eas is done for the GPT-NeoX library\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNVIDIA \u003ccode\u003enemo\u003c/code\u003e models\u003c/h3\u003e\u003ca id=\"user-content-nvidia-nemo-models\" class=\"anchor\" aria-label=\"Permalink: NVIDIA nemo models\" href=\"#nvidia-nemo-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/NeMo\"\u003eNVIDIA NeMo Framework\u003c/a\u003e is a generative AI framework built for researchers and pytorch developers working on language models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo evaluate a \u003ccode\u003enemo\u003c/code\u003e model, start by installing NeMo following \u003ca href=\"https://github.com/NVIDIA/NeMo?tab=readme-ov-file#installation\"\u003ethe documentation\u003c/a\u003e. We highly recommended to use the NVIDIA PyTorch or NeMo container, especially if having issues installing Apex or any other dependencies (see \u003ca href=\"https://github.com/NVIDIA/NeMo/releases\"\u003elatest released containers\u003c/a\u003e). Please also install the lm evaluation harness library following the instructions in \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/tree/main?tab=readme-ov-file#install\"\u003ethe Install section\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNeMo models can be obtained through \u003ca href=\"https://catalog.ngc.nvidia.com/models\" rel=\"nofollow\"\u003eNVIDIA NGC Catalog\u003c/a\u003e or in \u003ca href=\"https://huggingface.co/nvidia\" rel=\"nofollow\"\u003eNVIDIA's Hugging Face page\u003c/a\u003e. In \u003ca href=\"https://github.com/NVIDIA/NeMo/tree/main/scripts/nlp_language_modeling\"\u003eNVIDIA NeMo Framework\u003c/a\u003e there are conversion scripts to convert the \u003ccode\u003ehf\u003c/code\u003e checkpoints of popular models like llama, falcon, mixtral or mpt to \u003ccode\u003enemo\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRun a \u003ccode\u003enemo\u003c/code\u003e model on one GPU:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model nemo_lm \\\n --model_args path=\u0026lt;path_to_nemo_model\u0026gt; \\\n --tasks hellaswag \\\n --batch_size 32\"\u003e\u003cpre\u003elm_eval --model nemo_lm \\\n --model_args path=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_nemo_model\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --tasks hellaswag \\\n --batch_size 32\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIt is recommended to unpack the \u003ccode\u003enemo\u003c/code\u003e model to avoid the unpacking inside the docker container - it may overflow disk space. For that you can run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"mkdir MY_MODEL\ntar -xvf MY_MODEL.nemo -c MY_MODEL\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emkdir MY_MODEL\ntar -xvf MY_MODEL.nemo -c MY_MODEL\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMulti-GPU evaluation with NVIDIA \u003ccode\u003enemo\u003c/code\u003e models\u003c/h4\u003e\u003ca id=\"user-content-multi-gpu-evaluation-with-nvidia-nemo-models\" class=\"anchor\" aria-label=\"Permalink: Multi-GPU evaluation with NVIDIA nemo models\" href=\"#multi-gpu-evaluation-with-nvidia-nemo-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBy default, only one GPU is used. But we do support either data replication or tensor/pipeline parallelism during evaluation, on one node.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eTo enable data replication, set the \u003ccode\u003emodel_args\u003c/code\u003e of \u003ccode\u003edevices\u003c/code\u003e to the number of data replicas to run. For example, the command to run 8 data replicas over 8 GPUs is:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"torchrun --nproc-per-node=8 --no-python lm_eval \\\n --model nemo_lm \\\n --model_args path=\u0026lt;path_to_nemo_model\u0026gt;,devices=8 \\\n --tasks hellaswag \\\n --batch_size 32\"\u003e\u003cpre\u003etorchrun --nproc-per-node=8 --no-python lm_eval \\\n --model nemo_lm \\\n --model_args path=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_nemo_model\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e,devices=8 \\\n --tasks hellaswag \\\n --batch_size 32\u003c/pre\u003e\u003c/div\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eTo enable tensor and/or pipeline parallelism, set the \u003ccode\u003emodel_args\u003c/code\u003e of \u003ccode\u003etensor_model_parallel_size\u003c/code\u003e and/or \u003ccode\u003epipeline_model_parallel_size\u003c/code\u003e. In addition, you also have to set up \u003ccode\u003edevices\u003c/code\u003e to be equal to the product of \u003ccode\u003etensor_model_parallel_size\u003c/code\u003e and/or \u003ccode\u003epipeline_model_parallel_size\u003c/code\u003e. For example, the command to use one node of 4 GPUs with tensor parallelism of 2 and pipeline parallelism of 2 is:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"torchrun --nproc-per-node=4 --no-python lm_eval \\\n --model nemo_lm \\\n --model_args path=\u0026lt;path_to_nemo_model\u0026gt;,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 \\\n --tasks hellaswag \\\n --batch_size 32\"\u003e\u003cpre\u003etorchrun --nproc-per-node=4 --no-python lm_eval \\\n --model nemo_lm \\\n --model_args path=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_nemo_model\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 \\\n --tasks hellaswag \\\n --batch_size 32\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that it is recommended to substitute the \u003ccode\u003epython\u003c/code\u003e command by \u003ccode\u003etorchrun --nproc-per-node=\u0026lt;number of devices\u0026gt; --no-python\u003c/code\u003e to facilitate loading the model into the GPUs. This is especially important for large checkpoints loaded into multiple GPUs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNot supported yet: multi-node evaluation and combinations of data replication with tensor or pipeline parallelism.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMulti-GPU evaluation with OpenVINO models\u003c/h4\u003e\u003ca id=\"user-content-multi-gpu-evaluation-with-openvino-models\" class=\"anchor\" aria-label=\"Permalink: Multi-GPU evaluation with OpenVINO models\" href=\"#multi-gpu-evaluation-with-openvino-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePipeline parallelizm during evaluation is supported with OpenVINO models\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo enable pipeline parallelism, set the \u003ccode\u003emodel_args\u003c/code\u003e of \u003ccode\u003epipeline_parallel\u003c/code\u003e. In addition, you also have to set up \u003ccode\u003edevice\u003c/code\u003e to value \u003ccode\u003eHETERO:\u0026lt;GPU index1\u0026gt;,\u0026lt;GPU index2\u0026gt;\u003c/code\u003e for example \u003ccode\u003eHETERO:GPU.1,GPU.0\u003c/code\u003e For example, the command to use pipeline parallelism of 2 is:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"lm_eval --model openvino \\\n --tasks wikitext \\\n --model_args pretrained=\u0026lt;path_to_ov_model\u0026gt;,pipeline_parallel=True \\\n --device HETERO:GPU.1,GPU.0\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003elm_eval --model openvino \\\n --tasks wikitext \\\n --model_args pretrained=\u0026lt;path_to_ov_model\u0026gt;,pipeline_parallel=True \\\n --device HETERO:GPU.1,GPU.0\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensor + Data Parallel and Optimized Inference with \u003ccode\u003evLLM\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-tensor--data-parallel-and-optimized-inference-with-vllm\" class=\"anchor\" aria-label=\"Permalink: Tensor + Data Parallel and Optimized Inference with vLLM\" href=\"#tensor--data-parallel-and-optimized-inference-with-vllm\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe also support vLLM for faster inference on \u003ca href=\"https://docs.vllm.ai/en/latest/models/supported_models.html\" rel=\"nofollow\"\u003esupported model types\u003c/a\u003e, especially faster when splitting a model across multiple GPUs. For single-GPU or multi-GPU — tensor parallel, data parallel, or a combination of both — inference, for example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model vllm \\\n --model_args pretrained={model_name},tensor_parallel_size={GPUs_per_model},dtype=auto,gpu_memory_utilization=0.8,data_parallel_size={model_replicas} \\\n --tasks lambada_openai \\\n --batch_size auto\"\u003e\u003cpre\u003elm_eval --model vllm \\\n --model_args pretrained={model_name},tensor_parallel_size={GPUs_per_model},dtype=auto,gpu_memory_utilization=0.8,data_parallel_size={model_replicas} \\\n --tasks lambada_openai \\\n --batch_size auto\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo use vllm, do \u003ccode\u003epip install lm_eval[vllm]\u003c/code\u003e. For a full list of supported vLLM configurations, please reference our \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/blob/e74ec966556253fbe3d8ecba9de675c77c075bce/lm_eval/models/vllm_causallms.py\"\u003evLLM integration\u003c/a\u003e and the vLLM documentation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003evLLM occasionally differs in output from Huggingface. We treat Huggingface as the reference implementation, and provide a \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/scripts/model_comparator.py\"\u003escript\u003c/a\u003e for checking the validity of vllm results against HF.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-tip\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-light-bulb mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z\"\u003e\u003c/path\u003e\u003c/svg\u003eTip\u003c/p\u003e\u003cp dir=\"auto\"\u003eFor fastest performance, we recommend using \u003ccode\u003e--batch_size auto\u003c/code\u003e for vLLM whenever possible, to leverage its continuous batching functionality!\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-tip\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-light-bulb mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z\"\u003e\u003c/path\u003e\u003c/svg\u003eTip\u003c/p\u003e\u003cp dir=\"auto\"\u003ePassing \u003ccode\u003emax_model_len=4096\u003c/code\u003e or some other reasonable default to vLLM through model args may cause speedups or prevent out-of-memory errors when trying to use auto batch size, such as for Mistral-7B-v0.1 which defaults to a maximum length of 32k.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel APIs and Inference Servers\u003c/h3\u003e\u003ca id=\"user-content-model-apis-and-inference-servers\" class=\"anchor\" aria-label=\"Permalink: Model APIs and Inference Servers\" href=\"#model-apis-and-inference-servers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur library also supports the evaluation of models served via several commercial APIs, and we hope to implement support for the most commonly used performant local/self-hosted inference servers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo call a hosted model, use:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export OPENAI_API_KEY=YOUR_KEY_HERE\nlm_eval --model openai-completions \\\n --model_args model=davinci \\\n --tasks lambada_openai,hellaswag\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e OPENAI_API_KEY=YOUR_KEY_HERE\nlm_eval --model openai-completions \\\n --model_args model=davinci \\\n --tasks lambada_openai,hellaswag\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe also support using your own local inference server with servers that mirror the OpenAI Completions and ChatCompletions APIs.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model local-completions --tasks gsm8k --model_args model=facebook/opt-125m,base_url=http://{yourip}:8000/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,batch_size=16\"\u003e\u003cpre\u003elm_eval --model local-completions --tasks gsm8k --model_args model=facebook/opt-125m,base_url=http://{yourip}:8000/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,batch_size=16\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that for externally hosted models, configs such as \u003ccode\u003e--device\u003c/code\u003e which relate to where to place a local model should not be used and do not function. Just like you can use \u003ccode\u003e--model_args\u003c/code\u003e to pass arbitrary arguments to the model constructor for local models, you can use it to pass arbitrary arguments to the model API for hosted models. See the documentation of the hosting service for information on what arguments they support.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eAPI or Inference Server\u003c/th\u003e\n\u003cth\u003eImplemented?\u003c/th\u003e\n\u003cth\u003e\u003ccode\u003e--model \u0026lt;xxx\u0026gt;\u003c/code\u003e name\u003c/th\u003e\n\u003cth\u003eModels supported:\u003c/th\u003e\n\u003cth\u003eRequest Types:\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOpenAI Completions\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eopenai-completions\u003c/code\u003e, \u003ccode\u003elocal-completions\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAll OpenAI Completions API models\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOpenAI ChatCompletions\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eopenai-chat-completions\u003c/code\u003e, \u003ccode\u003elocal-chat-completions\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://platform.openai.com/docs/guides/gpt\" rel=\"nofollow\"\u003eAll ChatCompletions API models\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e (no logprobs)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnthropic\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eanthropic\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.anthropic.com/claude/reference/selecting-a-model\" rel=\"nofollow\"\u003eSupported Anthropic Engines\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e (no logprobs)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnthropic Chat\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eanthropic-chat\u003c/code\u003e, \u003ccode\u003eanthropic-chat-completions\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.anthropic.com/claude/docs/models-overview\" rel=\"nofollow\"\u003eSupported Anthropic Engines\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e (no logprobs)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTextsynth\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003etextsynth\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://textsynth.com/documentation.html#engines\" rel=\"nofollow\"\u003eAll supported engines\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCohere\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/pull/395\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/EleutherAI/lm-evaluation-harness/pull/395/hovercard\"\u003e⌛ - blocked on Cohere API bug\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eN/A\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.cohere.com/docs/models\" rel=\"nofollow\"\u003eAll \u003ccode\u003ecohere.generate()\u003c/code\u003e engines\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003eLlama.cpp\u003c/a\u003e (via \u003ca href=\"https://github.com/abetlen/llama-cpp-python\"\u003ellama-cpp-python\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egguf\u003c/code\u003e, \u003ccode\u003eggml\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003eAll models supported by llama.cpp\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, (perplexity evaluation not yet implemented)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003evLLM\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003evllm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.vllm.ai/en/latest/models/supported_models.html\" rel=\"nofollow\"\u003eMost HF Causal Language Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMamba\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003emamba_ssm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/state-spaces\" rel=\"nofollow\"\u003eMamba architecture Language Models via the \u003ccode\u003emamba_ssm\u003c/code\u003e package\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuggingface Optimum (Causal LMs)\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eopenvino\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAny decoder-only AutoModelForCausalLM converted with Huggingface Optimum into OpenVINO™ Intermediate Representation (IR) format\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuggingface Optimum-intel IPEX (Causal LMs)\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eipex\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAny decoder-only AutoModelForCausalLM\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNeuron via AWS Inf2 (Causal LMs)\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eneuronx\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAny decoder-only AutoModelForCausalLM supported to run on \u003ca href=\"https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2\" rel=\"nofollow\"\u003ehuggingface-ami image for inferentia2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/neuralmagic/deepsparse\"\u003eNeural Magic DeepSparse\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003edeepsparse\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAny LM from \u003ca href=\"https://sparsezoo.neuralmagic.com/\" rel=\"nofollow\"\u003eSparseZoo\u003c/a\u003e or on \u003ca href=\"https://huggingface.co/models?other=deepsparse\" rel=\"nofollow\"\u003eHF Hub with the \"deepsparse\" tag\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/neuralmagic/sparseml\"\u003eNeural Magic SparseML\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003esparseml\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eAny decoder-only AutoModelForCausalLM from \u003ca href=\"https://sparsezoo.neuralmagic.com/\" rel=\"nofollow\"\u003eSparseZoo\u003c/a\u003e or on \u003ca href=\"https://huggingface.co/neuralmagic\" rel=\"nofollow\"\u003eHF Hub\u003c/a\u003e. Especially useful for models with quantization like \u003ca href=\"https://sparsezoo.neuralmagic.com/models/llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized\" rel=\"nofollow\"\u003e\u003ccode\u003ezoo:llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWatsonx.ai\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003ewatsonx_llm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx\" rel=\"nofollow\"\u003eSupported Watsonx.ai Engines\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e \u003ccode\u003eloglikelihood\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/docs/API_guide.md\"\u003eYour local inference server!\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✔️\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003elocal-completions\u003c/code\u003e or \u003ccode\u003elocal-chat-completions\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eSupport for OpenAI API-compatible servers, with easy customization for other APIs.\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eModels which do not supply logits or logprobs can be used with tasks of type \u003ccode\u003egenerate_until\u003c/code\u003e only, while local models, or APIs that supply logprobs/logits of their prompts, can be run on all task types: \u003ccode\u003egenerate_until\u003c/code\u003e, \u003ccode\u003eloglikelihood\u003c/code\u003e, \u003ccode\u003eloglikelihood_rolling\u003c/code\u003e, and \u003ccode\u003emultiple_choice\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more information on the different task \u003ccode\u003eoutput_types\u003c/code\u003e and model request types, see \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/model_guide.md#interface\"\u003eour documentation\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eFor best performance with closed chat model APIs such as Anthropic Claude 3 and GPT-4, we recommend carefully looking at a few sample outputs using \u003ccode\u003e--limit 10\u003c/code\u003e first to confirm answer extraction and scoring on generative tasks is performing as expected. providing \u003ccode\u003esystem=\"\u0026lt;some system prompt here\u0026gt;\"\u003c/code\u003e within \u003ccode\u003e--model_args\u003c/code\u003e for anthropic-chat-completions, to instruct the model what format to respond in, may be useful.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOther Frameworks\u003c/h3\u003e\u003ca id=\"user-content-other-frameworks\" class=\"anchor\" aria-label=\"Permalink: Other Frameworks\" href=\"#other-frameworks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA number of other libraries contain scripts for calling the eval harness through their library. These include \u003ca href=\"https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py\"\u003eGPT-NeoX\u003c/a\u003e, \u003ca href=\"https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md\"\u003eMegatron-DeepSpeed\u003c/a\u003e, and \u003ca href=\"https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py\"\u003emesh-transformer-jax\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo create your own custom integration you can follow instructions from \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage\"\u003ethis tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdditional Features\u003c/h3\u003e\u003ca id=\"user-content-additional-features\" class=\"anchor\" aria-label=\"Permalink: Additional Features\" href=\"#additional-features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eFor tasks unsuitable for direct evaluation — either due risks associated with executing untrusted code or complexities in the evaluation process — the \u003ccode\u003e--predict_only\u003c/code\u003e flag is available to obtain decoded generations for post-hoc evaluation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you have a Metal compatible Mac, you can run the eval harness using the MPS back-end by replacing \u003ccode\u003e--device cuda:0\u003c/code\u003e with \u003ccode\u003e--device mps\u003c/code\u003e (requires PyTorch version 2.1 or higher). \u003cstrong\u003eNote that the PyTorch MPS backend is still in early stages of development, so correctness issues or unsupported operations may exist. If you observe oddities in model performance on the MPS back-end, we recommend first checking that a forward pass of your model on \u003ccode\u003e--device cpu\u003c/code\u003e and \u003ccode\u003e--device mps\u003c/code\u003e match.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\u003cp dir=\"auto\"\u003eYou can inspect what the LM inputs look like by running the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python write_out.py \\\n --tasks \u0026lt;task1,task2,...\u0026gt; \\\n --num_fewshot 5 \\\n --num_examples 10 \\\n --output_base_path /path/to/output/folder\"\u003e\u003cpre\u003epython write_out.py \\\n --tasks \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003etask1,task2,...\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n --num_fewshot 5 \\\n --num_examples 10 \\\n --output_base_path /path/to/output/folder\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will write out one text file for each task.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the \u003ccode\u003e--check_integrity\u003c/code\u003e flag:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model openai \\\n --model_args engine=davinci \\\n --tasks lambada_openai,hellaswag \\\n --check_integrity\"\u003e\u003cpre\u003elm_eval --model openai \\\n --model_args engine=davinci \\\n --tasks lambada_openai,hellaswag \\\n --check_integrity\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdvanced Usage Tips\u003c/h2\u003e\u003ca id=\"user-content-advanced-usage-tips\" class=\"anchor\" aria-label=\"Permalink: Advanced Usage Tips\" href=\"#advanced-usage-tips\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor models loaded with the HuggingFace \u003ccode\u003etransformers\u003c/code\u003e library, any arguments provided via \u003ccode\u003e--model_args\u003c/code\u003e get passed to the relevant constructor directly. This means that anything you can do with \u003ccode\u003eAutoModel\u003c/code\u003e can be done with our library. For example, you can pass a local path via \u003ccode\u003epretrained=\u003c/code\u003e or use models finetuned with \u003ca href=\"https://github.com/huggingface/peft\"\u003ePEFT\u003c/a\u003e by taking the call you would run to evaluate the base model and add \u003ccode\u003e,peft=PATH\u003c/code\u003e to the \u003ccode\u003emodel_args\u003c/code\u003e argument:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6b,parallelize=True,load_in_4bit=True,peft=nomic-ai/gpt4all-j-lora \\\n --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \\\n --device cuda:0\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6b,parallelize=True,load_in_4bit=True,peft=nomic-ai/gpt4all-j-lora \\\n --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \\\n --device cuda:0\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eModels provided as delta weights can be easily loaded using the Hugging Face transformers library. Within --model_args, set the delta argument to specify the delta weights, and use the pretrained argument to designate the relative base model to which they will be applied:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=Ejafa/llama_7B,delta=lmsys/vicuna-7b-delta-v1.1 \\\n --tasks hellaswag\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=Ejafa/llama_7B,delta=lmsys/vicuna-7b-delta-v1.1 \\\n --tasks hellaswag\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eGPTQ quantized models can be loaded using \u003ca href=\"https://github.com/ModelCloud/GPTQModel\"\u003eGPTQModel\u003c/a\u003e (faster) or \u003ca href=\"https://github.com/PanQiWei/AutoGPTQ\"\u003eAutoGPTQ\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGPTQModel: add \u003ccode\u003e,gptqmodel=True\u003c/code\u003e to \u003ccode\u003emodel_args\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=model-name-or-path,gptqmodel=True \\\n --tasks hellaswag\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=model-name-or-path,gptqmodel=True \\\n --tasks hellaswag\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAutoGPTQ: add \u003ccode\u003e,autogptq=True\u003c/code\u003e to \u003ccode\u003emodel_args\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \\\n --tasks hellaswag\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \\\n --tasks hellaswag\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe support wildcards in task names, for example you can run all of the machine-translated lambada tasks via \u003ccode\u003e--task lambada_openai_mt_*\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSaving \u0026amp; Caching Results\u003c/h2\u003e\u003ca id=\"user-content-saving--caching-results\" class=\"anchor\" aria-label=\"Permalink: Saving \u0026amp; Caching Results\" href=\"#saving--caching-results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo save evaluation results provide an \u003ccode\u003e--output_path\u003c/code\u003e. We also support logging model responses with the \u003ccode\u003e--log_samples\u003c/code\u003e flag for post-hoc analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-tip\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-light-bulb mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z\"\u003e\u003c/path\u003e\u003c/svg\u003eTip\u003c/p\u003e\u003cp dir=\"auto\"\u003eUse \u003ccode\u003e--use_cache \u0026lt;DIR\u0026gt;\u003c/code\u003e to cache evaluation results and skip previously evaluated samples when resuming runs of the same (model, task) pairs. Note that caching is rank-dependent, so restart with the same GPU count if interrupted. You can also use --cache_requests to save dataset preprocessing steps for faster evaluation resumption.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo push results and samples to the Hugging Face Hub, first ensure an access token with write access is set in the \u003ccode\u003eHF_TOKEN\u003c/code\u003e environment variable. Then, use the \u003ccode\u003e--hf_hub_log_args\u003c/code\u003e flag to specify the organization, repository name, repository visibility, and whether to push results and samples to the Hub - \u003ca href=\"https://huggingface.co/datasets/KonradSzafer/lm-eval-results-demo\" rel=\"nofollow\"\u003eexample dataset on the HF Hub\u003c/a\u003e. For instance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval --model hf \\\n --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \\\n --tasks hellaswag \\\n --log_samples \\\n --output_path results \\\n --hf_hub_log_args hub_results_org=EleutherAI,hub_repo_name=lm-eval-results,push_results_to_hub=True,push_samples_to_hub=True,public_repo=False \\\"\u003e\u003cpre\u003elm_eval --model hf \\\n --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \\\n --tasks hellaswag \\\n --log_samples \\\n --output_path results \\\n --hf_hub_log_args hub_results_org=EleutherAI,hub_repo_name=lm-eval-results,push_results_to_hub=True,push_samples_to_hub=True,public_repo=False \\\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis allows you to easily download the results and samples from the Hub, using:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from datasets import load_dataset\n\nload_dataset(\u0026quot;EleutherAI/lm-eval-results-private\u0026quot;, \u0026quot;hellaswag\u0026quot;, \u0026quot;latest\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eload_dataset\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003eload_dataset\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"EleutherAI/lm-eval-results-private\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"hellaswag\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"latest\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor a full list of supported arguments, check out the \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md\"\u003einterface\u003c/a\u003e guide in our documentation!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVisualizing Results\u003c/h2\u003e\u003ca id=\"user-content-visualizing-results\" class=\"anchor\" aria-label=\"Permalink: Visualizing Results\" href=\"#visualizing-results\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can seamlessly visualize and analyze the results of your evaluation harness runs using both Weights \u0026amp; Biases (W\u0026amp;B) and Zeno.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eZeno\u003c/h3\u003e\u003ca id=\"user-content-zeno\" class=\"anchor\" aria-label=\"Permalink: Zeno\" href=\"#zeno\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can use \u003ca href=\"https://zenoml.com\" rel=\"nofollow\"\u003eZeno\u003c/a\u003e to visualize the results of your eval harness runs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, head to \u003ca href=\"https://hub.zenoml.com\" rel=\"nofollow\"\u003ehub.zenoml.com\u003c/a\u003e to create an account and get an API key \u003ca href=\"https://hub.zenoml.com/account\" rel=\"nofollow\"\u003eon your account page\u003c/a\u003e.\nAdd this key as an environment variable:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export ZENO_API_KEY=[your api key]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e ZENO_API_KEY=[your api key]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou'll also need to install the \u003ccode\u003elm_eval[zeno]\u003c/code\u003e package extra.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo visualize the results, run the eval harness with the \u003ccode\u003elog_samples\u003c/code\u003e and \u003ccode\u003eoutput_path\u003c/code\u003e flags.\nWe expect \u003ccode\u003eoutput_path\u003c/code\u003e to contain multiple folders that represent individual model names.\nYou can thus run your evaluation on any number of tasks and models and upload all of the results as projects on Zeno.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval \\\n --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6B \\\n --tasks hellaswag \\\n --device cuda:0 \\\n --batch_size 8 \\\n --log_samples \\\n --output_path output/gpt-j-6B\"\u003e\u003cpre\u003elm_eval \\\n --model hf \\\n --model_args pretrained=EleutherAI/gpt-j-6B \\\n --tasks hellaswag \\\n --device cuda:0 \\\n --batch_size 8 \\\n --log_samples \\\n --output_path output/gpt-j-6B\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen, you can upload the resulting data using the \u003ccode\u003ezeno_visualize\u003c/code\u003e script:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python scripts/zeno_visualize.py \\\n --data_path output \\\n --project_name \u0026quot;Eleuther Project\u0026quot;\"\u003e\u003cpre\u003epython scripts/zeno_visualize.py \\\n --data_path output \\\n --project_name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eEleuther Project\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will use all subfolders in \u003ccode\u003edata_path\u003c/code\u003e as different models and upload all tasks within these model folders to Zeno.\nIf you run the eval harness on multiple tasks, the \u003ccode\u003eproject_name\u003c/code\u003e will be used as a prefix and one project will be created per task.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can find an example of this workflow in \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-zeno.ipynb\"\u003eexamples/visualize-zeno.ipynb\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeights and Biases\u003c/h3\u003e\u003ca id=\"user-content-weights-and-biases\" class=\"anchor\" aria-label=\"Permalink: Weights and Biases\" href=\"#weights-and-biases\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWith the \u003ca href=\"https://wandb.ai/site\" rel=\"nofollow\"\u003eWeights and Biases\u003c/a\u003e integration, you can now spend more time extracting deeper insights into your evaluation results. The integration is designed to streamline the process of logging and visualizing experiment results using the Weights \u0026amp; Biases (W\u0026amp;B) platform.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe integration provide functionalities\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eto automatically log the evaluation results,\u003c/li\u003e\n\u003cli\u003elog the samples as W\u0026amp;B Tables for easy visualization,\u003c/li\u003e\n\u003cli\u003elog the \u003ccode\u003eresults.json\u003c/code\u003e file as an artifact for version control,\u003c/li\u003e\n\u003cli\u003elog the \u003ccode\u003e\u0026lt;task_name\u0026gt;_eval_samples.json\u003c/code\u003e file if the samples are logged,\u003c/li\u003e\n\u003cli\u003egenerate a comprehensive report for analysis and visualization with all the important metric,\u003c/li\u003e\n\u003cli\u003elog task and cli specific configs,\u003c/li\u003e\n\u003cli\u003eand more out of the box like the command used to run the evaluation, GPU/CPU counts, timestamp, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFirst you'll need to install the lm_eval[wandb] package extra. Do \u003ccode\u003epip install lm_eval[wandb]\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAuthenticate your machine with an your unique W\u0026amp;B token. Visit \u003ca href=\"https://wandb.ai/authorize\" rel=\"nofollow\"\u003ehttps://wandb.ai/authorize\u003c/a\u003e to get one. Do \u003ccode\u003ewandb login\u003c/code\u003e in your command line terminal.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRun eval harness as usual with a \u003ccode\u003ewandb_args\u003c/code\u003e flag. Use this flag to provide arguments for initializing a wandb run (\u003ca href=\"https://docs.wandb.ai/ref/python/init\" rel=\"nofollow\"\u003ewandb.init\u003c/a\u003e) as comma separated string arguments.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"lm_eval \\\n --model hf \\\n --model_args pretrained=microsoft/phi-2,trust_remote_code=True \\\n --tasks hellaswag,mmlu_abstract_algebra \\\n --device cuda:0 \\\n --batch_size 8 \\\n --output_path output/phi-2 \\\n --limit 10 \\\n --wandb_args project=lm-eval-harness-integration \\\n --log_samples\"\u003e\u003cpre\u003elm_eval \\\n --model hf \\\n --model_args pretrained=microsoft/phi-2,trust_remote_code=True \\\n --tasks hellaswag,mmlu_abstract_algebra \\\n --device cuda:0 \\\n --batch_size 8 \\\n --output_path output/phi-2 \\\n --limit 10 \\\n --wandb_args project=lm-eval-harness-integration \\\n --log_samples\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn the stdout, you will find the link to the W\u0026amp;B run page as well as link to the generated report. You can find an example of this workflow in \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-wandb.ipynb\"\u003eexamples/visualize-wandb.ipynb\u003c/a\u003e, and an example of how to integrate it beyond the CLI.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to Contribute or Learn More?\u003c/h2\u003e\u003ca id=\"user-content-how-to-contribute-or-learn-more\" class=\"anchor\" aria-label=\"Permalink: How to Contribute or Learn More?\" href=\"#how-to-contribute-or-learn-more\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor more information on the library and how everything fits together, check out all of our \u003ca href=\"https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs\"\u003edocumentation pages\u003c/a\u003e! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eImplementing new tasks\u003c/h3\u003e\u003ca id=\"user-content-implementing-new-tasks\" class=\"anchor\" aria-label=\"Permalink: Implementing new tasks\" href=\"#implementing-new-tasks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo implement a new task in the eval harness, see \u003ca href=\"/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md\"\u003ethis guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn general, we follow this priority list for addressing concerns about prompting and other eval details:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eIf there is widespread agreement among people who train LLMs, use the agreed upon procedure.\u003c/li\u003e\n\u003cli\u003eIf there is a clear and unambiguous official implementation, use that procedure.\u003c/li\u003e\n\u003cli\u003eIf there is widespread agreement among people who evaluate LLMs, use the agreed upon procedure.\u003c/li\u003e\n\u003cli\u003eIf there are multiple common implementations but not universal or widespread agreement, use our preferred option among the common implementations. As before, prioritize choosing from among the implementations found in LLM training papers.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThese are guidelines and not rules, and can be overruled in special circumstances.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe try to prioritize agreement with the procedures used by other groups to decrease the harm when people inevitably compare runs across different papers despite our discouragement of the practice. Historically, we also prioritized the implementation from \u003ca href=\"https://arxiv.org/abs/2005.14165\" rel=\"nofollow\"\u003eLanguage Models are Few Shot Learners\u003c/a\u003e as our original goal was specifically to compare results with that paper.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupport\u003c/h3\u003e\u003ca id=\"user-content-support\" class=\"anchor\" aria-label=\"Permalink: Support\" href=\"#support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe best way to get support is to open an issue on this repo or join the \u003ca href=\"https://discord.gg/eleutherai\" rel=\"nofollow\"\u003eEleutherAI Discord server\u003c/a\u003e. The \u003ccode\u003e#lm-thunderdome\u003c/code\u003e channel is dedicated to developing this project and the \u003ccode\u003e#release-discussion\u003c/code\u003e channel is for receiving support for our releases. If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOptional Extras\u003c/h2\u003e\u003ca id=\"user-content-optional-extras\" class=\"anchor\" aria-label=\"Permalink: Optional Extras\" href=\"#optional-extras\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExtras dependencies can be installed via \u003ccode\u003epip install -e \".[NAME]\"\u003c/code\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eName\u003c/th\u003e\n\u003cth\u003eUse\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eapi\u003c/td\u003e\n\u003ctd\u003eFor using api models (Anthropic, OpenAI API)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edeepsparse\u003c/td\u003e\n\u003ctd\u003eFor running NM's DeepSparse models\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003edev\u003c/td\u003e\n\u003ctd\u003eFor linting PRs and contributions\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egptq\u003c/td\u003e\n\u003ctd\u003eFor loading models with AutoGPTQ\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egptqmodel\u003c/td\u003e\n\u003ctd\u003eFor loading models with GPTQModel\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ehf_transfer\u003c/td\u003e\n\u003ctd\u003eFor speeding up HF Hub file downloads\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eifeval\u003c/td\u003e\n\u003ctd\u003eFor running the IFEval task\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eibm_watsonx_ai\u003c/td\u003e\n\u003ctd\u003eFor using IBM watsonx.ai model apis\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eipex\u003c/td\u003e\n\u003ctd\u003eFor running on optimum-intel ipex backend\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eneuronx\u003c/td\u003e\n\u003ctd\u003eFor running on AWS inf2 instances\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emamba\u003c/td\u003e\n\u003ctd\u003eFor loading Mamba SSM models\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emath\u003c/td\u003e\n\u003ctd\u003eFor running math task answer checking\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emultilingual\u003c/td\u003e\n\u003ctd\u003eFor multilingual tokenizers\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eoptimum\u003c/td\u003e\n\u003ctd\u003eFor running Intel OpenVINO models\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003epromptsource\u003c/td\u003e\n\u003ctd\u003eFor using PromptSource prompts\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esentencepiece\u003c/td\u003e\n\u003ctd\u003eFor using the sentencepiece tokenizer\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003esparseml\u003c/td\u003e\n\u003ctd\u003eFor using NM's SparseML models\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etesting\u003c/td\u003e\n\u003ctd\u003eFor running library test suite\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003evllm\u003c/td\u003e\n\u003ctd\u003eFor loading models with vLLM\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ezeno\u003c/td\u003e\n\u003ctd\u003eFor visualizing results with Zeno\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e---------------\u003c/td\u003e\n\u003ctd\u003e---------------------------------------\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eall\u003c/td\u003e\n\u003ctd\u003eLoads all extras (not recommended)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCite as\u003c/h2\u003e\u003ca id=\"user-content-cite-as\" class=\"anchor\" aria-label=\"Permalink: Cite as\" href=\"#cite-as\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{eval-harness,\n author = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},\n title = {A framework for few-shot language model evaluation},\n month = 07,\n year = 2024,\n publisher = {Zenodo},\n version = {v0.4.3},\n doi = {10.5281/zenodo.12608602},\n url = {https://zenodo.org/records/12608602}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{eval-harness,\n author = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},\n title = {A framework for few-shot language model evaluation},\n month = 07,\n year = 2024,\n publisher = {Zenodo},\n version = {v0.4.3},\n doi = {10.5281/zenodo.12608602},\n url = {https://zenodo.org/records/12608602}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Language Model Evaluation Harness","anchor":"language-model-evaluation-harness","htmlText":"Language Model Evaluation Harness"},{"level":2,"text":"Announcement","anchor":"announcement","htmlText":"Announcement"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Install","anchor":"install","htmlText":"Install"},{"level":2,"text":"Basic Usage","anchor":"basic-usage","htmlText":"Basic Usage"},{"level":3,"text":"User Guide","anchor":"user-guide","htmlText":"User Guide"},{"level":3,"text":"Hugging Face transformers","anchor":"hugging-face-transformers","htmlText":"Hugging Face transformers"},{"level":4,"text":"Multi-GPU Evaluation with Hugging Face accelerate","anchor":"multi-gpu-evaluation-with-hugging-face-accelerate","htmlText":"Multi-GPU Evaluation with Hugging Face accelerate"},{"level":3,"text":"NVIDIA nemo models","anchor":"nvidia-nemo-models","htmlText":"NVIDIA nemo models"},{"level":4,"text":"Multi-GPU evaluation with NVIDIA nemo models","anchor":"multi-gpu-evaluation-with-nvidia-nemo-models","htmlText":"Multi-GPU evaluation with NVIDIA nemo models"},{"level":4,"text":"Multi-GPU evaluation with OpenVINO models","anchor":"multi-gpu-evaluation-with-openvino-models","htmlText":"Multi-GPU evaluation with OpenVINO models"},{"level":3,"text":"Tensor + Data Parallel and Optimized Inference with vLLM","anchor":"tensor--data-parallel-and-optimized-inference-with-vllm","htmlText":"Tensor + Data Parallel and Optimized Inference with vLLM"},{"level":3,"text":"Model APIs and Inference Servers","anchor":"model-apis-and-inference-servers","htmlText":"Model APIs and Inference Servers"},{"level":3,"text":"Other Frameworks","anchor":"other-frameworks","htmlText":"Other Frameworks"},{"level":3,"text":"Additional Features","anchor":"additional-features","htmlText":"Additional Features"},{"level":2,"text":"Advanced Usage Tips","anchor":"advanced-usage-tips","htmlText":"Advanced Usage Tips"},{"level":2,"text":"Saving \u0026 Caching Results","anchor":"saving--caching-results","htmlText":"Saving \u0026amp; Caching Results"},{"level":2,"text":"Visualizing Results","anchor":"visualizing-results","htmlText":"Visualizing Results"},{"level":3,"text":"Zeno","anchor":"zeno","htmlText":"Zeno"},{"level":3,"text":"Weights and Biases","anchor":"weights-and-biases","htmlText":"Weights and Biases"},{"level":2,"text":"How to Contribute or Learn More?","anchor":"how-to-contribute-or-learn-more","htmlText":"How to Contribute or Learn More?"},{"level":3,"text":"Implementing new tasks","anchor":"implementing-new-tasks","htmlText":"Implementing new tasks"},{"level":3,"text":"Support","anchor":"support","htmlText":"Support"},{"level":2,"text":"Optional Extras","anchor":"optional-extras","htmlText":"Optional Extras"},{"level":2,"text":"Cite as","anchor":"cite-as","htmlText":"Cite 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1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Language Model Evaluation Harness</h1><a id="user-content-language-model-evaluation-harness" class="anchor" aria-label="Permalink: Language Model Evaluation Harness" href="#language-model-evaluation-harness"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://doi.org/10.5281/zenodo.10256836" rel="nofollow"><img src="https://camo.githubusercontent.com/2b03c2ae010ea9e7319f8e7d149b83ad3fdba1486fd345de2dcfbe885ad4e09e/68747470733a2f2f7a656e6f646f2e6f72672f62616467652f444f492f31302e353238312f7a656e6f646f2e31303235363833362e737667" alt="DOI" data-canonical-src="https://zenodo.org/badge/DOI/10.5281/zenodo.10256836.svg" style="max-width: 100%;"></a></p> <hr> <p dir="auto"><em>Latest News 📣</em></p> <ul dir="auto"> <li>[2024/09] We are prototyping allowing users of LM Evaluation Harness to create and evaluate on text+image multimodal input, text output tasks, and have just added the <code>hf-multimodal</code> and <code>vllm-vlm</code> model types and <code>mmmu</code> task as a prototype feature. We welcome users to try out this in-progress feature and stress-test it for themselves, and suggest they check out <a href="https://github.com/EvolvingLMMs-Lab/lmms-eval"><code>lmms-eval</code></a>, a wonderful project originally forking off of the lm-evaluation-harness, for a broader range of multimodal tasks, models, and features.</li> <li>[2024/07] <a href="/EleutherAI/lm-evaluation-harness/blob/main/docs/API_guide.md">API model</a> support has been updated and refactored, introducing support for batched and async requests, and making it significantly easier to customize and use for your own purposes. <strong>To run Llama 405B, we recommend using VLLM's OpenAI-compliant API to host the model, and use the <code>local-completions</code> model type to evaluate the model.</strong></li> <li>[2024/07] New Open LLM Leaderboard tasks have been added ! You can find them under the <a href="/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/leaderboard/README.md">leaderboard</a> task group.</li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Announcement</h2><a id="user-content-announcement" class="anchor" aria-label="Permalink: Announcement" href="#announcement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>A new v0.4.0 release of lm-evaluation-harness is available</strong> !</p> <p dir="auto">New updates and features include:</p> <ul dir="auto"> <li><strong>New Open LLM Leaderboard tasks have been added ! You can find them under the <a href="/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/leaderboard/README.md">leaderboard</a> task group.</strong></li> <li>Internal refactoring</li> <li>Config-based task creation and configuration</li> <li>Easier import and sharing of externally-defined task config YAMLs</li> <li>Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource</li> <li>More advanced configuration options, including output post-processing, answer extraction, and multiple LM generations per document, configurable fewshot settings, and more</li> <li>Speedups and new modeling libraries supported, including: faster data-parallel HF model usage, vLLM support, MPS support with HuggingFace, and more</li> <li>Logging and usability changes</li> <li>New tasks including CoT BIG-Bench-Hard, Belebele, user-defined task groupings, and more</li> </ul> <p dir="auto">Please see our updated documentation pages in <code>docs/</code> for more details.</p> <p dir="auto">Development will be continuing on the <code>main</code> branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub, or in the <a href="https://discord.gg/eleutherai" rel="nofollow">EleutherAI discord</a>!</p> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project provides a unified framework to test generative language models on a large number of different evaluation tasks.</p> <p dir="auto"><strong>Features:</strong></p> <ul dir="auto"> <li>Over 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants implemented.</li> <li>Support for models loaded via <a href="https://github.com/huggingface/transformers/">transformers</a> (including quantization via <a href="https://github.com/ModelCloud/GPTQModel">GPTQModel</a> and <a href="https://github.com/PanQiWei/AutoGPTQ">AutoGPTQ</a>), <a href="https://github.com/EleutherAI/gpt-neox">GPT-NeoX</a>, and <a href="https://github.com/microsoft/Megatron-DeepSpeed/">Megatron-DeepSpeed</a>, with a flexible tokenization-agnostic interface.</li> <li>Support for fast and memory-efficient inference with <a href="https://github.com/vllm-project/vllm">vLLM</a>.</li> <li>Support for commercial APIs including <a href="https://openai.com" rel="nofollow">OpenAI</a>, and <a href="https://textsynth.com/" rel="nofollow">TextSynth</a>.</li> <li>Support for evaluation on adapters (e.g. LoRA) supported in <a href="https://github.com/huggingface/peft">HuggingFace's PEFT library</a>.</li> <li>Support for local models and benchmarks.</li> <li>Evaluation with publicly available prompts ensures reproducibility and comparability between papers.</li> <li>Easy support for custom prompts and evaluation metrics.</li> </ul> <p dir="auto">The Language Model Evaluation Harness is the backend for 🤗 Hugging Face's popular <a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard" rel="nofollow">Open LLM Leaderboard</a>, has been used in <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&authuser=2&cites=15052937328817631261,4097184744846514103,1520777361382155671,17476825572045927382,18443729326628441434,14801318227356878622,7890865700763267262,12854182577605049984,15641002901115500560,5104500764547628290" rel="nofollow">hundreds of papers</a>, and is used internally by dozens of organizations including NVIDIA, Cohere, BigScience, BigCode, Nous Research, and Mosaic ML.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Install</h2><a id="user-content-install" class="anchor" aria-label="Permalink: Install" href="#install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install the <code>lm-eval</code> package from the github repository, run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness cd lm-evaluation-harness pip install -e ."><pre>git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness <span class="pl-c1">cd</span> lm-evaluation-harness pip install -e <span class="pl-c1">.</span></pre></div> <p dir="auto">We also provide a number of optional dependencies for extended functionality. A detailed table is available at the end of this document.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Basic Usage</h2><a id="user-content-basic-usage" class="anchor" aria-label="Permalink: Basic Usage" href="#basic-usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">User Guide</h3><a id="user-content-user-guide" class="anchor" aria-label="Permalink: User Guide" href="#user-guide"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A user guide detailing the full list of supported arguments is provided <a href="/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md">here</a>, and on the terminal by calling <code>lm_eval -h</code>. Alternatively, you can use <code>lm-eval</code> instead of <code>lm_eval</code>.</p> <p dir="auto">A list of supported tasks (or groupings of tasks) can be viewed with <code>lm-eval --tasks list</code>. Task descriptions and links to corresponding subfolders are provided <a href="/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/README.md">here</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Hugging Face <code>transformers</code></h3><a id="user-content-hugging-face-transformers" class="anchor" aria-label="Permalink: Hugging Face transformers" href="#hugging-face-transformers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To evaluate a model hosted on the <a href="https://huggingface.co/models" rel="nofollow">HuggingFace Hub</a> (e.g. GPT-J-6B) on <code>hellaswag</code> you can use the following command (this assumes you are using a CUDA-compatible GPU):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=EleutherAI/gpt-j-6B \ --tasks hellaswag \ --device cuda:0 \ --batch_size 8"><pre>lm_eval --model hf \ --model_args pretrained=EleutherAI/gpt-j-6B \ --tasks hellaswag \ --device cuda:0 \ --batch_size 8</pre></div> <p dir="auto">Additional arguments can be provided to the model constructor using the <code>--model_args</code> flag. Most notably, this supports the common practice of using the <code>revisions</code> feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype="float" \ --tasks lambada_openai,hellaswag \ --device cuda:0 \ --batch_size 8"><pre>lm_eval --model hf \ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=<span class="pl-s"><span class="pl-pds">"</span>float<span class="pl-pds">"</span></span> \ --tasks lambada_openai,hellaswag \ --device cuda:0 \ --batch_size 8</pre></div> <p dir="auto">Models that are loaded via both <code>transformers.AutoModelForCausalLM</code> (autoregressive, decoder-only GPT style models) and <code>transformers.AutoModelForSeq2SeqLM</code> (such as encoder-decoder models like T5) in Huggingface are supported.</p> <p dir="auto">Batch size selection can be automated by setting the <code>--batch_size</code> flag to <code>auto</code>. This will perform automatic detection of the largest batch size that will fit on your device. On tasks where there is a large difference between the longest and shortest example, it can be helpful to periodically recompute the largest batch size, to gain a further speedup. To do this, append <code>:N</code> to above flag to automatically recompute the largest batch size <code>N</code> times. For example, to recompute the batch size 4 times, the command would be:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype="float" \ --tasks lambada_openai,hellaswag \ --device cuda:0 \ --batch_size auto:4"><pre>lm_eval --model hf \ --model_args pretrained=EleutherAI/pythia-160m,revision=step100000,dtype=<span class="pl-s"><span class="pl-pds">"</span>float<span class="pl-pds">"</span></span> \ --tasks lambada_openai,hellaswag \ --device cuda:0 \ --batch_size auto:4</pre></div> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">Just like you can provide a local path to <code>transformers.AutoModel</code>, you can also provide a local path to <code>lm_eval</code> via <code>--model_args pretrained=/path/to/model</code></p> </div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Multi-GPU Evaluation with Hugging Face <code>accelerate</code></h4><a id="user-content-multi-gpu-evaluation-with-hugging-face-accelerate" class="anchor" aria-label="Permalink: Multi-GPU Evaluation with Hugging Face accelerate" href="#multi-gpu-evaluation-with-hugging-face-accelerate"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We support three main ways of using Hugging Face's <a href="https://github.com/huggingface/accelerate">accelerate 🚀</a> library for multi-GPU evaluation.</p> <p dir="auto">To perform <em>data-parallel evaluation</em> (where each GPU loads a <strong>separate full copy</strong> of the model), we leverage the <code>accelerate</code> launcher as follows:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="accelerate launch -m lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --batch_size 16"><pre class="notranslate"><code>accelerate launch -m lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --batch_size 16 </code></pre></div> <p dir="auto">(or via <code>accelerate launch --no-python lm_eval</code>).</p> <p dir="auto">For cases where your model can fit on a single GPU, this allows you to evaluate on K GPUs K times faster than on one.</p> <p dir="auto"><strong>WARNING</strong>: This setup does not work with FSDP model sharding, so in <code>accelerate config</code> FSDP must be disabled, or the NO_SHARD FSDP option must be used.</p> <p dir="auto">The second way of using <code>accelerate</code> for multi-GPU evaluation is when your model is <em>too large to fit on a single GPU.</em></p> <p dir="auto">In this setting, run the library <em>outside the <code>accelerate</code> launcher</em>, but passing <code>parallelize=True</code> to <code>--model_args</code> as follows:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --model_args parallelize=True \ --batch_size 16"><pre class="notranslate"><code>lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --model_args parallelize=True \ --batch_size 16 </code></pre></div> <p dir="auto">This means that your model's weights will be split across all available GPUs.</p> <p dir="auto">For more advanced users or even larger models, we allow for the following arguments when <code>parallelize=True</code> as well:</p> <ul dir="auto"> <li><code>device_map_option</code>: How to split model weights across available GPUs. defaults to "auto".</li> <li><code>max_memory_per_gpu</code>: the max GPU memory to use per GPU in loading the model.</li> <li><code>max_cpu_memory</code>: the max amount of CPU memory to use when offloading the model weights to RAM.</li> <li><code>offload_folder</code>: a folder where model weights will be offloaded to disk if needed.</li> </ul> <p dir="auto">The third option is to use both at the same time. This will allow you to take advantage of both data parallelism and model sharding, and is especially useful for models that are too large to fit on a single GPU.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="accelerate launch --multi_gpu --num_processes {nb_of_copies_of_your_model} \ -m lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --model_args parallelize=True \ --batch_size 16"><pre class="notranslate"><code>accelerate launch --multi_gpu --num_processes {nb_of_copies_of_your_model} \ -m lm_eval --model hf \ --tasks lambada_openai,arc_easy \ --model_args parallelize=True \ --batch_size 16 </code></pre></div> <p dir="auto">To learn more about model parallelism and how to use it with the <code>accelerate</code> library, see the <a href="https://huggingface.co/docs/transformers/v4.15.0/en/parallelism" rel="nofollow">accelerate documentation</a></p> <p dir="auto"><strong>Warning: We do not natively support multi-node evaluation using the <code>hf</code> model type! Please reference <a href="https://github.com/EleutherAI/gpt-neox/blob/main/eval.py">our GPT-NeoX library integration</a> for an example of code in which a custom multi-machine evaluation script is written.</strong></p> <p dir="auto"><strong>Note: we do not currently support multi-node evaluations natively, and advise using either an externally hosted server to run inference requests against, or creating a custom integration with your distributed framework <a href="https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py">as is done for the GPT-NeoX library</a>.</strong></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">NVIDIA <code>nemo</code> models</h3><a id="user-content-nvidia-nemo-models" class="anchor" aria-label="Permalink: NVIDIA nemo models" href="#nvidia-nemo-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://github.com/NVIDIA/NeMo">NVIDIA NeMo Framework</a> is a generative AI framework built for researchers and pytorch developers working on language models.</p> <p dir="auto">To evaluate a <code>nemo</code> model, start by installing NeMo following <a href="https://github.com/NVIDIA/NeMo?tab=readme-ov-file#installation">the documentation</a>. We highly recommended to use the NVIDIA PyTorch or NeMo container, especially if having issues installing Apex or any other dependencies (see <a href="https://github.com/NVIDIA/NeMo/releases">latest released containers</a>). Please also install the lm evaluation harness library following the instructions in <a href="https://github.com/EleutherAI/lm-evaluation-harness/tree/main?tab=readme-ov-file#install">the Install section</a>.</p> <p dir="auto">NeMo models can be obtained through <a href="https://catalog.ngc.nvidia.com/models" rel="nofollow">NVIDIA NGC Catalog</a> or in <a href="https://huggingface.co/nvidia" rel="nofollow">NVIDIA's Hugging Face page</a>. In <a href="https://github.com/NVIDIA/NeMo/tree/main/scripts/nlp_language_modeling">NVIDIA NeMo Framework</a> there are conversion scripts to convert the <code>hf</code> checkpoints of popular models like llama, falcon, mixtral or mpt to <code>nemo</code>.</p> <p dir="auto">Run a <code>nemo</code> model on one GPU:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model nemo_lm \ --model_args path=<path_to_nemo_model> \ --tasks hellaswag \ --batch_size 32"><pre>lm_eval --model nemo_lm \ --model_args path=<span class="pl-k"><</span>path_to_nemo_model<span class="pl-k">></span> \ --tasks hellaswag \ --batch_size 32</pre></div> <p dir="auto">It is recommended to unpack the <code>nemo</code> model to avoid the unpacking inside the docker container - it may overflow disk space. For that you can run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="mkdir MY_MODEL tar -xvf MY_MODEL.nemo -c MY_MODEL"><pre class="notranslate"><code>mkdir MY_MODEL tar -xvf MY_MODEL.nemo -c MY_MODEL </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Multi-GPU evaluation with NVIDIA <code>nemo</code> models</h4><a id="user-content-multi-gpu-evaluation-with-nvidia-nemo-models" class="anchor" aria-label="Permalink: Multi-GPU evaluation with NVIDIA nemo models" href="#multi-gpu-evaluation-with-nvidia-nemo-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">By default, only one GPU is used. But we do support either data replication or tensor/pipeline parallelism during evaluation, on one node.</p> <ol dir="auto"> <li>To enable data replication, set the <code>model_args</code> of <code>devices</code> to the number of data replicas to run. For example, the command to run 8 data replicas over 8 GPUs is:</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="torchrun --nproc-per-node=8 --no-python lm_eval \ --model nemo_lm \ --model_args path=<path_to_nemo_model>,devices=8 \ --tasks hellaswag \ --batch_size 32"><pre>torchrun --nproc-per-node=8 --no-python lm_eval \ --model nemo_lm \ --model_args path=<span class="pl-k"><</span>path_to_nemo_model<span class="pl-k">></span>,devices=8 \ --tasks hellaswag \ --batch_size 32</pre></div> <ol start="2" dir="auto"> <li>To enable tensor and/or pipeline parallelism, set the <code>model_args</code> of <code>tensor_model_parallel_size</code> and/or <code>pipeline_model_parallel_size</code>. In addition, you also have to set up <code>devices</code> to be equal to the product of <code>tensor_model_parallel_size</code> and/or <code>pipeline_model_parallel_size</code>. For example, the command to use one node of 4 GPUs with tensor parallelism of 2 and pipeline parallelism of 2 is:</li> </ol> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="torchrun --nproc-per-node=4 --no-python lm_eval \ --model nemo_lm \ --model_args path=<path_to_nemo_model>,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 \ --tasks hellaswag \ --batch_size 32"><pre>torchrun --nproc-per-node=4 --no-python lm_eval \ --model nemo_lm \ --model_args path=<span class="pl-k"><</span>path_to_nemo_model<span class="pl-k">></span>,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 \ --tasks hellaswag \ --batch_size 32</pre></div> <p dir="auto">Note that it is recommended to substitute the <code>python</code> command by <code>torchrun --nproc-per-node=<number of devices> --no-python</code> to facilitate loading the model into the GPUs. This is especially important for large checkpoints loaded into multiple GPUs.</p> <p dir="auto">Not supported yet: multi-node evaluation and combinations of data replication with tensor or pipeline parallelism.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Multi-GPU evaluation with OpenVINO models</h4><a id="user-content-multi-gpu-evaluation-with-openvino-models" class="anchor" aria-label="Permalink: Multi-GPU evaluation with OpenVINO models" href="#multi-gpu-evaluation-with-openvino-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pipeline parallelizm during evaluation is supported with OpenVINO models</p> <p dir="auto">To enable pipeline parallelism, set the <code>model_args</code> of <code>pipeline_parallel</code>. In addition, you also have to set up <code>device</code> to value <code>HETERO:<GPU index1>,<GPU index2></code> for example <code>HETERO:GPU.1,GPU.0</code> For example, the command to use pipeline parallelism of 2 is:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="lm_eval --model openvino \ --tasks wikitext \ --model_args pretrained=<path_to_ov_model>,pipeline_parallel=True \ --device HETERO:GPU.1,GPU.0"><pre class="notranslate"><code>lm_eval --model openvino \ --tasks wikitext \ --model_args pretrained=<path_to_ov_model>,pipeline_parallel=True \ --device HETERO:GPU.1,GPU.0 </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Tensor + Data Parallel and Optimized Inference with <code>vLLM</code></h3><a id="user-content-tensor--data-parallel-and-optimized-inference-with-vllm" class="anchor" aria-label="Permalink: Tensor + Data Parallel and Optimized Inference with vLLM" href="#tensor--data-parallel-and-optimized-inference-with-vllm"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We also support vLLM for faster inference on <a href="https://docs.vllm.ai/en/latest/models/supported_models.html" rel="nofollow">supported model types</a>, especially faster when splitting a model across multiple GPUs. For single-GPU or multi-GPU — tensor parallel, data parallel, or a combination of both — inference, for example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model vllm \ --model_args pretrained={model_name},tensor_parallel_size={GPUs_per_model},dtype=auto,gpu_memory_utilization=0.8,data_parallel_size={model_replicas} \ --tasks lambada_openai \ --batch_size auto"><pre>lm_eval --model vllm \ --model_args pretrained={model_name},tensor_parallel_size={GPUs_per_model},dtype=auto,gpu_memory_utilization=0.8,data_parallel_size={model_replicas} \ --tasks lambada_openai \ --batch_size auto</pre></div> <p dir="auto">To use vllm, do <code>pip install lm_eval[vllm]</code>. For a full list of supported vLLM configurations, please reference our <a href="https://github.com/EleutherAI/lm-evaluation-harness/blob/e74ec966556253fbe3d8ecba9de675c77c075bce/lm_eval/models/vllm_causallms.py">vLLM integration</a> and the vLLM documentation.</p> <p dir="auto">vLLM occasionally differs in output from Huggingface. We treat Huggingface as the reference implementation, and provide a <a href="/EleutherAI/lm-evaluation-harness/blob/main/scripts/model_comparator.py">script</a> for checking the validity of vllm results against HF.</p> <div class="markdown-alert markdown-alert-tip" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-light-bulb mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z"></path></svg>Tip</p><p dir="auto">For fastest performance, we recommend using <code>--batch_size auto</code> for vLLM whenever possible, to leverage its continuous batching functionality!</p> </div> <div class="markdown-alert markdown-alert-tip" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-light-bulb mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z"></path></svg>Tip</p><p dir="auto">Passing <code>max_model_len=4096</code> or some other reasonable default to vLLM through model args may cause speedups or prevent out-of-memory errors when trying to use auto batch size, such as for Mistral-7B-v0.1 which defaults to a maximum length of 32k.</p> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Model APIs and Inference Servers</h3><a id="user-content-model-apis-and-inference-servers" class="anchor" aria-label="Permalink: Model APIs and Inference Servers" href="#model-apis-and-inference-servers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Our library also supports the evaluation of models served via several commercial APIs, and we hope to implement support for the most commonly used performant local/self-hosted inference servers.</p> <p dir="auto">To call a hosted model, use:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export OPENAI_API_KEY=YOUR_KEY_HERE lm_eval --model openai-completions \ --model_args model=davinci \ --tasks lambada_openai,hellaswag"><pre><span class="pl-k">export</span> OPENAI_API_KEY=YOUR_KEY_HERE lm_eval --model openai-completions \ --model_args model=davinci \ --tasks lambada_openai,hellaswag</pre></div> <p dir="auto">We also support using your own local inference server with servers that mirror the OpenAI Completions and ChatCompletions APIs.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model local-completions --tasks gsm8k --model_args model=facebook/opt-125m,base_url=http://{yourip}:8000/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,batch_size=16"><pre>lm_eval --model local-completions --tasks gsm8k --model_args model=facebook/opt-125m,base_url=http://{yourip}:8000/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,batch_size=16</pre></div> <p dir="auto">Note that for externally hosted models, configs such as <code>--device</code> which relate to where to place a local model should not be used and do not function. Just like you can use <code>--model_args</code> to pass arbitrary arguments to the model constructor for local models, you can use it to pass arbitrary arguments to the model API for hosted models. See the documentation of the hosting service for information on what arguments they support.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>API or Inference Server</th> <th>Implemented?</th> <th><code>--model <xxx></code> name</th> <th>Models supported:</th> <th>Request Types:</th> </tr> </thead> <tbody> <tr> <td>OpenAI Completions</td> <td>✔️</td> <td><code>openai-completions</code>, <code>local-completions</code></td> <td>All OpenAI Completions API models</td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>OpenAI ChatCompletions</td> <td>✔️</td> <td><code>openai-chat-completions</code>, <code>local-chat-completions</code></td> <td><a href="https://platform.openai.com/docs/guides/gpt" rel="nofollow">All ChatCompletions API models</a></td> <td><code>generate_until</code> (no logprobs)</td> </tr> <tr> <td>Anthropic</td> <td>✔️</td> <td><code>anthropic</code></td> <td><a href="https://docs.anthropic.com/claude/reference/selecting-a-model" rel="nofollow">Supported Anthropic Engines</a></td> <td><code>generate_until</code> (no logprobs)</td> </tr> <tr> <td>Anthropic Chat</td> <td>✔️</td> <td><code>anthropic-chat</code>, <code>anthropic-chat-completions</code></td> <td><a href="https://docs.anthropic.com/claude/docs/models-overview" rel="nofollow">Supported Anthropic Engines</a></td> <td><code>generate_until</code> (no logprobs)</td> </tr> <tr> <td>Textsynth</td> <td>✔️</td> <td><code>textsynth</code></td> <td><a href="https://textsynth.com/documentation.html#engines" rel="nofollow">All supported engines</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Cohere</td> <td><a href="https://github.com/EleutherAI/lm-evaluation-harness/pull/395" data-hovercard-type="pull_request" data-hovercard-url="/EleutherAI/lm-evaluation-harness/pull/395/hovercard">⌛ - blocked on Cohere API bug</a></td> <td>N/A</td> <td><a href="https://docs.cohere.com/docs/models" rel="nofollow">All <code>cohere.generate()</code> engines</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td><a href="https://github.com/ggerganov/llama.cpp">Llama.cpp</a> (via <a href="https://github.com/abetlen/llama-cpp-python">llama-cpp-python</a>)</td> <td>✔️</td> <td><code>gguf</code>, <code>ggml</code></td> <td><a href="https://github.com/ggerganov/llama.cpp">All models supported by llama.cpp</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, (perplexity evaluation not yet implemented)</td> </tr> <tr> <td>vLLM</td> <td>✔️</td> <td><code>vllm</code></td> <td><a href="https://docs.vllm.ai/en/latest/models/supported_models.html" rel="nofollow">Most HF Causal Language Models</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Mamba</td> <td>✔️</td> <td><code>mamba_ssm</code></td> <td><a href="https://huggingface.co/state-spaces" rel="nofollow">Mamba architecture Language Models via the <code>mamba_ssm</code> package</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Huggingface Optimum (Causal LMs)</td> <td>✔️</td> <td><code>openvino</code></td> <td>Any decoder-only AutoModelForCausalLM converted with Huggingface Optimum into OpenVINO™ Intermediate Representation (IR) format</td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Huggingface Optimum-intel IPEX (Causal LMs)</td> <td>✔️</td> <td><code>ipex</code></td> <td>Any decoder-only AutoModelForCausalLM</td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Neuron via AWS Inf2 (Causal LMs)</td> <td>✔️</td> <td><code>neuronx</code></td> <td>Any decoder-only AutoModelForCausalLM supported to run on <a href="https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2" rel="nofollow">huggingface-ami image for inferentia2</a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td><a href="https://github.com/neuralmagic/deepsparse">Neural Magic DeepSparse</a></td> <td>✔️</td> <td><code>deepsparse</code></td> <td>Any LM from <a href="https://sparsezoo.neuralmagic.com/" rel="nofollow">SparseZoo</a> or on <a href="https://huggingface.co/models?other=deepsparse" rel="nofollow">HF Hub with the "deepsparse" tag</a></td> <td><code>generate_until</code>, <code>loglikelihood</code></td> </tr> <tr> <td><a href="https://github.com/neuralmagic/sparseml">Neural Magic SparseML</a></td> <td>✔️</td> <td><code>sparseml</code></td> <td>Any decoder-only AutoModelForCausalLM from <a href="https://sparsezoo.neuralmagic.com/" rel="nofollow">SparseZoo</a> or on <a href="https://huggingface.co/neuralmagic" rel="nofollow">HF Hub</a>. Especially useful for models with quantization like <a href="https://sparsezoo.neuralmagic.com/models/llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized" rel="nofollow"><code>zoo:llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized</code></a></td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> <tr> <td>Watsonx.ai</td> <td>✔️</td> <td><code>watsonx_llm</code></td> <td><a href="https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx" rel="nofollow">Supported Watsonx.ai Engines</a></td> <td><code>generate_until</code> <code>loglikelihood</code></td> </tr> <tr> <td><a href="/EleutherAI/lm-evaluation-harness/blob/main/docs/API_guide.md">Your local inference server!</a></td> <td>✔️</td> <td><code>local-completions</code> or <code>local-chat-completions</code></td> <td>Support for OpenAI API-compatible servers, with easy customization for other APIs.</td> <td><code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Models which do not supply logits or logprobs can be used with tasks of type <code>generate_until</code> only, while local models, or APIs that supply logprobs/logits of their prompts, can be run on all task types: <code>generate_until</code>, <code>loglikelihood</code>, <code>loglikelihood_rolling</code>, and <code>multiple_choice</code>.</p> <p dir="auto">For more information on the different task <code>output_types</code> and model request types, see <a href="https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/model_guide.md#interface">our documentation</a>.</p> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">For best performance with closed chat model APIs such as Anthropic Claude 3 and GPT-4, we recommend carefully looking at a few sample outputs using <code>--limit 10</code> first to confirm answer extraction and scoring on generative tasks is performing as expected. providing <code>system="<some system prompt here>"</code> within <code>--model_args</code> for anthropic-chat-completions, to instruct the model what format to respond in, may be useful.</p> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Other Frameworks</h3><a id="user-content-other-frameworks" class="anchor" aria-label="Permalink: Other Frameworks" href="#other-frameworks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A number of other libraries contain scripts for calling the eval harness through their library. These include <a href="https://github.com/EleutherAI/gpt-neox/blob/main/eval_tasks/eval_adapter.py">GPT-NeoX</a>, <a href="https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples/MoE/readme_evalharness.md">Megatron-DeepSpeed</a>, and <a href="https://github.com/kingoflolz/mesh-transformer-jax/blob/master/eval_harness.py">mesh-transformer-jax</a>.</p> <p dir="auto">To create your own custom integration you can follow instructions from <a href="https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage">this tutorial</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Additional Features</h3><a id="user-content-additional-features" class="anchor" aria-label="Permalink: Additional Features" href="#additional-features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">For tasks unsuitable for direct evaluation — either due risks associated with executing untrusted code or complexities in the evaluation process — the <code>--predict_only</code> flag is available to obtain decoded generations for post-hoc evaluation.</p> </div> <p dir="auto">If you have a Metal compatible Mac, you can run the eval harness using the MPS back-end by replacing <code>--device cuda:0</code> with <code>--device mps</code> (requires PyTorch version 2.1 or higher). <strong>Note that the PyTorch MPS backend is still in early stages of development, so correctness issues or unsupported operations may exist. If you observe oddities in model performance on the MPS back-end, we recommend first checking that a forward pass of your model on <code>--device cpu</code> and <code>--device mps</code> match.</strong></p> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p><p dir="auto">You can inspect what the LM inputs look like by running the following command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python write_out.py \ --tasks <task1,task2,...> \ --num_fewshot 5 \ --num_examples 10 \ --output_base_path /path/to/output/folder"><pre>python write_out.py \ --tasks <span class="pl-k"><</span>task1,task2,...<span class="pl-k">></span> \ --num_fewshot 5 \ --num_examples 10 \ --output_base_path /path/to/output/folder</pre></div> <p dir="auto">This will write out one text file for each task.</p> </div> <p dir="auto">To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the <code>--check_integrity</code> flag:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model openai \ --model_args engine=davinci \ --tasks lambada_openai,hellaswag \ --check_integrity"><pre>lm_eval --model openai \ --model_args engine=davinci \ --tasks lambada_openai,hellaswag \ --check_integrity</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Advanced Usage Tips</h2><a id="user-content-advanced-usage-tips" class="anchor" aria-label="Permalink: Advanced Usage Tips" href="#advanced-usage-tips"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For models loaded with the HuggingFace <code>transformers</code> library, any arguments provided via <code>--model_args</code> get passed to the relevant constructor directly. This means that anything you can do with <code>AutoModel</code> can be done with our library. For example, you can pass a local path via <code>pretrained=</code> or use models finetuned with <a href="https://github.com/huggingface/peft">PEFT</a> by taking the call you would run to evaluate the base model and add <code>,peft=PATH</code> to the <code>model_args</code> argument:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=EleutherAI/gpt-j-6b,parallelize=True,load_in_4bit=True,peft=nomic-ai/gpt4all-j-lora \ --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \ --device cuda:0"><pre>lm_eval --model hf \ --model_args pretrained=EleutherAI/gpt-j-6b,parallelize=True,load_in_4bit=True,peft=nomic-ai/gpt4all-j-lora \ --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \ --device cuda:0</pre></div> <p dir="auto">Models provided as delta weights can be easily loaded using the Hugging Face transformers library. Within --model_args, set the delta argument to specify the delta weights, and use the pretrained argument to designate the relative base model to which they will be applied:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=Ejafa/llama_7B,delta=lmsys/vicuna-7b-delta-v1.1 \ --tasks hellaswag"><pre>lm_eval --model hf \ --model_args pretrained=Ejafa/llama_7B,delta=lmsys/vicuna-7b-delta-v1.1 \ --tasks hellaswag</pre></div> <p dir="auto">GPTQ quantized models can be loaded using <a href="https://github.com/ModelCloud/GPTQModel">GPTQModel</a> (faster) or <a href="https://github.com/PanQiWei/AutoGPTQ">AutoGPTQ</a></p> <p dir="auto">GPTQModel: add <code>,gptqmodel=True</code> to <code>model_args</code></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=model-name-or-path,gptqmodel=True \ --tasks hellaswag"><pre>lm_eval --model hf \ --model_args pretrained=model-name-or-path,gptqmodel=True \ --tasks hellaswag</pre></div> <p dir="auto">AutoGPTQ: add <code>,autogptq=True</code> to <code>model_args</code>:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \ --tasks hellaswag"><pre>lm_eval --model hf \ --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \ --tasks hellaswag</pre></div> <p dir="auto">We support wildcards in task names, for example you can run all of the machine-translated lambada tasks via <code>--task lambada_openai_mt_*</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Saving & Caching Results</h2><a id="user-content-saving--caching-results" class="anchor" aria-label="Permalink: Saving & Caching Results" href="#saving--caching-results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To save evaluation results provide an <code>--output_path</code>. We also support logging model responses with the <code>--log_samples</code> flag for post-hoc analysis.</p> <div class="markdown-alert markdown-alert-tip" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-light-bulb mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M8 1.5c-2.363 0-4 1.69-4 3.75 0 .984.424 1.625.984 2.304l.214.253c.223.264.47.556.673.848.284.411.537.896.621 1.49a.75.75 0 0 1-1.484.211c-.04-.282-.163-.547-.37-.847a8.456 8.456 0 0 0-.542-.68c-.084-.1-.173-.205-.268-.32C3.201 7.75 2.5 6.766 2.5 5.25 2.5 2.31 4.863 0 8 0s5.5 2.31 5.5 5.25c0 1.516-.701 2.5-1.328 3.259-.095.115-.184.22-.268.319-.207.245-.383.453-.541.681-.208.3-.33.565-.37.847a.751.751 0 0 1-1.485-.212c.084-.593.337-1.078.621-1.489.203-.292.45-.584.673-.848.075-.088.147-.173.213-.253.561-.679.985-1.32.985-2.304 0-2.06-1.637-3.75-4-3.75ZM5.75 12h4.5a.75.75 0 0 1 0 1.5h-4.5a.75.75 0 0 1 0-1.5ZM6 15.25a.75.75 0 0 1 .75-.75h2.5a.75.75 0 0 1 0 1.5h-2.5a.75.75 0 0 1-.75-.75Z"></path></svg>Tip</p><p dir="auto">Use <code>--use_cache <DIR></code> to cache evaluation results and skip previously evaluated samples when resuming runs of the same (model, task) pairs. Note that caching is rank-dependent, so restart with the same GPU count if interrupted. You can also use --cache_requests to save dataset preprocessing steps for faster evaluation resumption.</p> </div> <p dir="auto">To push results and samples to the Hugging Face Hub, first ensure an access token with write access is set in the <code>HF_TOKEN</code> environment variable. Then, use the <code>--hf_hub_log_args</code> flag to specify the organization, repository name, repository visibility, and whether to push results and samples to the Hub - <a href="https://huggingface.co/datasets/KonradSzafer/lm-eval-results-demo" rel="nofollow">example dataset on the HF Hub</a>. For instance:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval --model hf \ --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \ --tasks hellaswag \ --log_samples \ --output_path results \ --hf_hub_log_args hub_results_org=EleutherAI,hub_repo_name=lm-eval-results,push_results_to_hub=True,push_samples_to_hub=True,public_repo=False \"><pre>lm_eval --model hf \ --model_args pretrained=model-name-or-path,autogptq=model.safetensors,gptq_use_triton=True \ --tasks hellaswag \ --log_samples \ --output_path results \ --hf_hub_log_args hub_results_org=EleutherAI,hub_repo_name=lm-eval-results,push_results_to_hub=True,push_samples_to_hub=True,public_repo=False \</pre></div> <p dir="auto">This allows you to easily download the results and samples from the Hub, using:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from datasets import load_dataset load_dataset("EleutherAI/lm-eval-results-private", "hellaswag", "latest")"><pre><span class="pl-k">from</span> <span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-s1">load_dataset</span> <span class="pl-en">load_dataset</span>(<span class="pl-s">"EleutherAI/lm-eval-results-private"</span>, <span class="pl-s">"hellaswag"</span>, <span class="pl-s">"latest"</span>)</pre></div> <p dir="auto">For a full list of supported arguments, check out the <a href="https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md">interface</a> guide in our documentation!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Visualizing Results</h2><a id="user-content-visualizing-results" class="anchor" aria-label="Permalink: Visualizing Results" href="#visualizing-results"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can seamlessly visualize and analyze the results of your evaluation harness runs using both Weights & Biases (W&B) and Zeno.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Zeno</h3><a id="user-content-zeno" class="anchor" aria-label="Permalink: Zeno" href="#zeno"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can use <a href="https://zenoml.com" rel="nofollow">Zeno</a> to visualize the results of your eval harness runs.</p> <p dir="auto">First, head to <a href="https://hub.zenoml.com" rel="nofollow">hub.zenoml.com</a> to create an account and get an API key <a href="https://hub.zenoml.com/account" rel="nofollow">on your account page</a>. Add this key as an environment variable:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export ZENO_API_KEY=[your api key]"><pre><span class="pl-k">export</span> ZENO_API_KEY=[your api key]</pre></div> <p dir="auto">You'll also need to install the <code>lm_eval[zeno]</code> package extra.</p> <p dir="auto">To visualize the results, run the eval harness with the <code>log_samples</code> and <code>output_path</code> flags. We expect <code>output_path</code> to contain multiple folders that represent individual model names. You can thus run your evaluation on any number of tasks and models and upload all of the results as projects on Zeno.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval \ --model hf \ --model_args pretrained=EleutherAI/gpt-j-6B \ --tasks hellaswag \ --device cuda:0 \ --batch_size 8 \ --log_samples \ --output_path output/gpt-j-6B"><pre>lm_eval \ --model hf \ --model_args pretrained=EleutherAI/gpt-j-6B \ --tasks hellaswag \ --device cuda:0 \ --batch_size 8 \ --log_samples \ --output_path output/gpt-j-6B</pre></div> <p dir="auto">Then, you can upload the resulting data using the <code>zeno_visualize</code> script:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python scripts/zeno_visualize.py \ --data_path output \ --project_name "Eleuther Project""><pre>python scripts/zeno_visualize.py \ --data_path output \ --project_name <span class="pl-s"><span class="pl-pds">"</span>Eleuther Project<span class="pl-pds">"</span></span></pre></div> <p dir="auto">This will use all subfolders in <code>data_path</code> as different models and upload all tasks within these model folders to Zeno. If you run the eval harness on multiple tasks, the <code>project_name</code> will be used as a prefix and one project will be created per task.</p> <p dir="auto">You can find an example of this workflow in <a href="/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-zeno.ipynb">examples/visualize-zeno.ipynb</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Weights and Biases</h3><a id="user-content-weights-and-biases" class="anchor" aria-label="Permalink: Weights and Biases" href="#weights-and-biases"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">With the <a href="https://wandb.ai/site" rel="nofollow">Weights and Biases</a> integration, you can now spend more time extracting deeper insights into your evaluation results. The integration is designed to streamline the process of logging and visualizing experiment results using the Weights & Biases (W&B) platform.</p> <p dir="auto">The integration provide functionalities</p> <ul dir="auto"> <li>to automatically log the evaluation results,</li> <li>log the samples as W&B Tables for easy visualization,</li> <li>log the <code>results.json</code> file as an artifact for version control,</li> <li>log the <code><task_name>_eval_samples.json</code> file if the samples are logged,</li> <li>generate a comprehensive report for analysis and visualization with all the important metric,</li> <li>log task and cli specific configs,</li> <li>and more out of the box like the command used to run the evaluation, GPU/CPU counts, timestamp, etc.</li> </ul> <p dir="auto">First you'll need to install the lm_eval[wandb] package extra. Do <code>pip install lm_eval[wandb]</code>.</p> <p dir="auto">Authenticate your machine with an your unique W&B token. Visit <a href="https://wandb.ai/authorize" rel="nofollow">https://wandb.ai/authorize</a> to get one. Do <code>wandb login</code> in your command line terminal.</p> <p dir="auto">Run eval harness as usual with a <code>wandb_args</code> flag. Use this flag to provide arguments for initializing a wandb run (<a href="https://docs.wandb.ai/ref/python/init" rel="nofollow">wandb.init</a>) as comma separated string arguments.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="lm_eval \ --model hf \ --model_args pretrained=microsoft/phi-2,trust_remote_code=True \ --tasks hellaswag,mmlu_abstract_algebra \ --device cuda:0 \ --batch_size 8 \ --output_path output/phi-2 \ --limit 10 \ --wandb_args project=lm-eval-harness-integration \ --log_samples"><pre>lm_eval \ --model hf \ --model_args pretrained=microsoft/phi-2,trust_remote_code=True \ --tasks hellaswag,mmlu_abstract_algebra \ --device cuda:0 \ --batch_size 8 \ --output_path output/phi-2 \ --limit 10 \ --wandb_args project=lm-eval-harness-integration \ --log_samples</pre></div> <p dir="auto">In the stdout, you will find the link to the W&B run page as well as link to the generated report. You can find an example of this workflow in <a href="/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-wandb.ipynb">examples/visualize-wandb.ipynb</a>, and an example of how to integrate it beyond the CLI.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to Contribute or Learn More?</h2><a id="user-content-how-to-contribute-or-learn-more" class="anchor" aria-label="Permalink: How to Contribute or Learn More?" href="#how-to-contribute-or-learn-more"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For more information on the library and how everything fits together, check out all of our <a href="https://github.com/EleutherAI/lm-evaluation-harness/tree/main/docs">documentation pages</a>! We plan to post a larger roadmap of desired + planned library improvements soon, with more information on how contributors can help.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Implementing new tasks</h3><a id="user-content-implementing-new-tasks" class="anchor" aria-label="Permalink: Implementing new tasks" href="#implementing-new-tasks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To implement a new task in the eval harness, see <a href="/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md">this guide</a>.</p> <p dir="auto">In general, we follow this priority list for addressing concerns about prompting and other eval details:</p> <ol dir="auto"> <li>If there is widespread agreement among people who train LLMs, use the agreed upon procedure.</li> <li>If there is a clear and unambiguous official implementation, use that procedure.</li> <li>If there is widespread agreement among people who evaluate LLMs, use the agreed upon procedure.</li> <li>If there are multiple common implementations but not universal or widespread agreement, use our preferred option among the common implementations. As before, prioritize choosing from among the implementations found in LLM training papers.</li> </ol> <p dir="auto">These are guidelines and not rules, and can be overruled in special circumstances.</p> <p dir="auto">We try to prioritize agreement with the procedures used by other groups to decrease the harm when people inevitably compare runs across different papers despite our discouragement of the practice. Historically, we also prioritized the implementation from <a href="https://arxiv.org/abs/2005.14165" rel="nofollow">Language Models are Few Shot Learners</a> as our original goal was specifically to compare results with that paper.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Support</h3><a id="user-content-support" class="anchor" aria-label="Permalink: Support" href="#support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The best way to get support is to open an issue on this repo or join the <a href="https://discord.gg/eleutherai" rel="nofollow">EleutherAI Discord server</a>. The <code>#lm-thunderdome</code> channel is dedicated to developing this project and the <code>#release-discussion</code> channel is for receiving support for our releases. If you've used the library and have had a positive (or negative) experience, we'd love to hear from you!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Optional Extras</h2><a id="user-content-optional-extras" class="anchor" aria-label="Permalink: Optional Extras" href="#optional-extras"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Extras dependencies can be installed via <code>pip install -e ".[NAME]"</code></p> <markdown-accessiblity-table><table> <thead> <tr> <th>Name</th> <th>Use</th> </tr> </thead> <tbody> <tr> <td>api</td> <td>For using api models (Anthropic, OpenAI API)</td> </tr> <tr> <td>deepsparse</td> <td>For running NM's DeepSparse models</td> </tr> <tr> <td>dev</td> <td>For linting PRs and contributions</td> </tr> <tr> <td>gptq</td> <td>For loading models with AutoGPTQ</td> </tr> <tr> <td>gptqmodel</td> <td>For loading models with GPTQModel</td> </tr> <tr> <td>hf_transfer</td> <td>For speeding up HF Hub file downloads</td> </tr> <tr> <td>ifeval</td> <td>For running the IFEval task</td> </tr> <tr> <td>ibm_watsonx_ai</td> <td>For using IBM watsonx.ai model apis</td> </tr> <tr> <td>ipex</td> <td>For running on optimum-intel ipex backend</td> </tr> <tr> <td>neuronx</td> <td>For running on AWS inf2 instances</td> </tr> <tr> <td>mamba</td> <td>For loading Mamba SSM models</td> </tr> <tr> <td>math</td> <td>For running math task answer checking</td> </tr> <tr> <td>multilingual</td> <td>For multilingual tokenizers</td> </tr> <tr> <td>optimum</td> <td>For running Intel OpenVINO models</td> </tr> <tr> <td>promptsource</td> <td>For using PromptSource prompts</td> </tr> <tr> <td>sentencepiece</td> <td>For using the sentencepiece tokenizer</td> </tr> <tr> <td>sparseml</td> <td>For using NM's SparseML models</td> </tr> <tr> <td>testing</td> <td>For running library test suite</td> </tr> <tr> <td>vllm</td> <td>For loading models with vLLM</td> </tr> <tr> <td>zeno</td> <td>For visualizing results with Zeno</td> </tr> <tr> <td>---------------</td> <td>---------------------------------------</td> </tr> <tr> <td>all</td> <td>Loads all extras (not recommended)</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Cite as</h2><a id="user-content-cite-as" class="anchor" aria-label="Permalink: Cite as" href="#cite-as"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{eval-harness, author = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy}, title = {A framework for few-shot language model evaluation}, month = 07, year = 2024, publisher = {Zenodo}, version = {v0.4.3}, doi = {10.5281/zenodo.12608602}, url = {https://zenodo.org/records/12608602} }"><pre class="notranslate"><code>@misc{eval-harness, author = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy}, title = {A framework for few-shot language model evaluation}, month = 07, year = 2024, publisher = {Zenodo}, version = {v0.4.3}, doi = {10.5281/zenodo.12608602}, url = {https://zenodo.org/records/12608602} } </code></pre></div> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="q7j1qe0YtxMKKjYGSZPZVgn15yeuGGDXe5Lz1LJo3jluXnSslcyApTUXn+kh2eOr5yrJN5rX5zdL1Gc0/6x3Hw==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A framework for few-shot evaluation of language models. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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