Llama 2 Release, Hugging Face Updates, OpenAI Availability and Deprecation, and “Superalignment” Vision

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relative w-full h-full object-cover" src="https://media.licdn.com/dms/image/v2/D4E12AQHkZ1TTDTfe7Q/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1690279496867?e=2147483647&v=beta&t=CZdJuEJz2kHRVUlYiVpwaMeicaE7iLCL_HxaPquiWgw" fetchpriority="high" data-embed-id="cover-image" alt="Llama 2 Release, Hugging Face Updates, OpenAI Availability and Deprecation, and “Superalignment” Vision"> </div> </div> <figcaption class="cover-img__caption text-sm text-color-text-secondary pt-2">Provectus AI review #4</figcaption> </figure> <header> <section class="core-section-container relative my-3"> <h1 class="pulse-title text-display-md text-color-text lg:text-display-lg font-bold w-11/12">Llama 2 Release, Hugging Face Updates, OpenAI Availability and Deprecation, and “Superalignment” Vision</h1> <div class="ellipsis-menu absolute right-0 top-0"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger 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</div> </div> <div class="core-section-container__content break-words"> <div class="base-card relative w-full hover:no-underline focus:no-underline base-card--link base-main-card flex flex-wrap py-2 pr-2 babybear:pr-0 base-main-card--link publisher-author-card"> <a class="base-card__full-link absolute top-0 right-0 bottom-0 left-0 p-0 z-[2]" href="https://www.linkedin.com/company/provectus-it-inc" data-tracking-control-name="article-ssr-frontend-pulse_publisher-author-card" data-tracking-will-navigate> <span class="sr-only"> Provectus </span> </a> <img class="inline-block relative w-6 h-6 " data-delayed-url="https://media.licdn.com/dms/image/v2/D4D0BAQFnf7MoIbtEqQ/company-logo_100_100/company-logo_100_100/0/1693390195179/provectus_it_inc_logo?e=2147483647&v=beta&t=s9McVt3w2eaUsBGzMCIDrDlFLSzhRBG8X4vcNwtzC0Y" data-ghost-classes="bg-color-entity-ghost-background" data-ghost-url="https://static.licdn.com/aero-v1/sc/h/cs8pjfgyw96g44ln9r7tct85f" alt="Provectus"> <div class="base-main-card__info self-center ml-1 flex-1 relative break-words papabear:min-w-0 mamabear:min-w-0 babybear:w-full ">  <h3 class="base-main-card__title font-sans text-[18px] font-bold text-color-text overflow-hidden "> Provectus </h3> <h4 class="base-main-card__subtitle body-text text-color-text overflow-hidden "> We help businesses leverage cloud, data, and AI to reimagine the way they operate, compete, and deliver customer value. </h4>  <div class="body-text text-color-text-low-emphasis base-main-card__metadata"> Published Jul 25, 2023 </div>  </div> <div class="base-main-card__ctas z-[3] self-center ml-3 babybear:ml-1 babybear:self-start"> <a class="base-main-card__cta btn-sm btn-secondary" href="https://www.linkedin.com/signup/cold-join?session_redirect=%2Fpulse%2Fllama-2-release-hugging-face-updates-openai-availability%3Ftrk%3Dpublic_post&trk=article-ssr-frontend-pulse_publisher-author-card" data-tracking-control-name="article-ssr-frontend-pulse_publisher-author-card" data-tracking-will-navigate> + Follow </a> </div> </div> </div> </section> </header> <div data-test-id="article-content-blocks">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Welcome to the latest edition of the “Provectus AI Review” series! </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Over the last couple of weeks, the rapid growth in the AI/ML niche has been nothing short of remarkable. Even more notable is the astounding progress of Generative AI and Large Language Models (LLMs) – a wave of innovation that is fundamentally reshaping our understanding of what's possible.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>This issue highlights a few topics that are already shaping the future of AI, including the debut of Llama 2, the latest updates from Hugging Face, an in-depth analysis of OpenAI's deprecation policies, and the concept of Superalignment.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Meta AI’s Llama 2 Is Amazing</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Meta has just unveiled </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fai%2Emeta%2Ecom%2Fllama%2F&urlhash=gfko&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Llama 2 </a></span><span class>, the latest open-source Large Language Model (LLM) that raises the bar for state-of-the-art AI. Building upon its predecessor, this second iteration of Llama comes with a commercial-friendly license, offering substantial benefits for enterprise use.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQEcPXXc-H26KA/article-inline_image-shrink_400_744/article-inline_image-shrink_400_744/0/1690276971280?e=2147483647&v=beta&t=W81h8ke2bzDOUZjtm9wMLwJhZRRj_CKQHmB7zJ9PA-4">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Llama 2 is available in three distinct sizes — 7 billion (7B), 13 billion (13B), and 70 billion (70B) parameters. The 7B and 13B variants maintain the same architectural design as the original Llama and serve as direct substitutes for commercial applications.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>There are a few enhancements to Llama 2 that set it apart from the original version. Notably, it has been trained on an impressive 2 trillion tokens. This version also permits commercial usage, a pivotal improvement that broadens its applicability. Special attention has been given to enhancing its capabilities for dialogue use cases with dedicated chat models. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Llama 2 boasts a default context window of 4096 tokens (which can be increased per user requirements). The 70B parameter version of Llama 2 has also incorporated grouped-query attention (GQA) to further augment its functionality.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In terms of accessibility, the chat models of Llama 2 can effectively utilize various tools and plugins, offering users greater versatility. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Note:</span><span class> Llama 2-CHAT performs on par with OpenAI's ChatGPT, illustrating the competitive potential of this new model. Both Llama 2 and its chat variant are readily accessible on platforms such as Hugging Face and Meta.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQEwhNt5OYxP6w/article-inline_image-shrink_400_744/article-inline_image-shrink_400_744/0/1690277026508?e=2147483647&v=beta&t=sUAYEeBLjCCjuszvVP1Wi5NJ1fvkW-ut7Zlhog5pERo">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">More tokens with RoPE scaling</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Large Language Models like Llama and GPTNeoX place restrictions on the number of tokens that can be used in one go. This boundary has been pushed with </span><span class><a href="https://www.linkedin.com/posts/gante_scaling-llama-and-gptneox-to-8k-input-context-activity-7085545793050320896-8OKi/?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>the introduction of RoPE scaling </a></span><span class>.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>With RoPE scaling, these models can handle inputs of arbitrary length, extending beyond the previous cap of 4,000 tokens. The implementation is simple yet effective - just pass `rope_scaling` at the time of model loading and your model is immediately empowered to manage extended inputs. This extended capability comes without the need for additional fine-tuning.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>However, there is a minor caveat. While RoPE scaling does allow for expanded inputs, there can be an increase in perplexity with lengthening of the input. This can be addressed effectively with targeted fine-tuning. As such, RoPE scaling presents an exciting new development for users to manage longer sequences with Llama and GPTNeoX models.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">4-bit quantization</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>4-bit quantization is a promising approach, offering two distinct alternatives for using the HuggingFace platform with Llama 2 — dynamic and static quantization.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2Fdocs%2Ftransformers%2Fen%2Fmain_classes%2Fquantization%23load-a-large-model-in-4bit&urlhash=V0SS&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Dynamic quantization </a></span><span class> provides real-time quantization by loading original weights. This process is relatively straightforward: passing `device_map="auto", load_in_4bit=True` into the `from_pretrained()` function activates this feature, setting you up for an immediate start.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/posts/gante_llms-textgeneration-pytorch-activity-7080598132236951553-bMij/?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Static quantization </a></span><span class> should be your choice if you are looking to boost the speed of your local Llama 2 model. TheBloke </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2FTheBloke&urlhash=Em3H&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>offers quantized checkpoints on the hub </a></span><span class>, explaining how this can be done. In short, checkpoints can be used with </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2FPanQiWei%2FAutoGPTQ&urlhash=63dR&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>AutoGPTQ </a></span><span class> and the HuggingFace ecosystem, simplifying their use. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Check out </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fpastebin%2Ecom%2FZB5Wtg55&urlhash=67hS&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>an exemplary script </a></span><span class> that can be adapted specifically for Llama 2.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Deployment and fine-tuning of Llama 2</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The deployment and fine-tuning of Llama 2 bring a unique blend of challenges and opportunities. Here, we explore three potential scenarios that demonstrate how these processes can be effectively executed.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ol> <li><span class="font-[700]">Llama recipes:</span><span class> Meta's official '</span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Ffacebookresearch%2Fllama-recipes%2Ftree%2Fmain&urlhash=dZHm&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>llama-recipes </a></span><span class>' repository, designed as a companion to the Llama 2 model, offers comprehensive examples for efficient fine-tuning and inference of these fine-tuned models. It provides in-depth guidance and code examples for various scenarios, including single GPU fine-tuning, multi-GPU fine-tuning, LLM fine-tuning, adding custom datasets, and conducting inference.</span></li> <li><span class="font-[700]">Hugging Face Transformers for Llama 2:</span><span class> </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2Fblog%2Fllama2&urlhash=v4yd&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>The Hugging Face Transformers library </a></span><span class> provides an in-depth guide for deploying and using the Llama 2 model via text-generation-inference endpoints. It outlines an efficient methodology for fine-tuning Llama 2 on simple hardware, demonstrating how to adjust the 7 billion parameter version on a single NVIDIA T4 GPU, available on Google Colab. To assist in this process, a specialized script is offered, aiding users in customizing the Llama 2 model for optimized performance.</span></li> <li><span class="font-[700]">QLoRA and Hugging Face Transformers on Amazon SageMaker:</span><span class> Using </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fhuggingface%2Fpeft%2Freleases%2Ftag%2Fv0%2E4%2E0&urlhash=F_nk&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>the Hugging Face Transformers library and QLoRA on Amazon SageMaker </a></span><span class>, you can efficiently fine-tune Llama version 2 through a process known as Partially Elastic Fine-Tuning (PEFT). This starts with setting up your development environment, followed by loading and preparing your dataset to suit your specific use case. The key phase involves fine-tuning the 13 billion parameter Llama model on Amazon SageMaker with the help of QLoRA. The process culminates in deploying the fine-tuned Large Language Model on Amazon SageMaker, ready to deliver powerful AI solutions in real-world applications.</span></li> </ol> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Hugging Face’s New PEFT Library Is Released</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Hugging Face has </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fhuggingface%2Fpeft%2Freleases%2Ftag%2Fv0%2E4%2E0&urlhash=F_nk&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>released an update of its PEFT library </a></span><span class>, boasting several significant enhancements. One of the key updates is the official integration of </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2Fblog%2F4bit-transformers-bitsandbytes&urlhash=KI6H&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>the QLoRA method </a></span><span class>, enabling the training of adapters on top of a 4-bit base model, which we partially covered above.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQGgIsXK1V5xrQ/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277089380?e=2147483647&v=beta&t=ixQRkuWJNzRXNctjBC2gXqNOawQINOE9k61bZDkQ0kk">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The new release introduces an additional PEFT task, feature extraction, and adds </span><span class="italic">PeftModelForQuestionAnswering</span><span class> and </span><span class="italic">PeftModelForFeatureExtraction</span><span class> classes. These inclusions facilitate support for question answering and feature extraction tasks respectively, allowing PEFT to be used for diverse tasks like </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2Fdocs%2Fpeft%2Ftask_guides%2Fsemantic-similarity-lora&urlhash=fM-2&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>semantic similarity </a></span><span class>.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">“Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning” paper</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2205%2E05638&urlhash=juQX&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>A new adapter method </a></span><span class>, IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations), is introduced from the T-Few Paper. IA3 optimizes fine-tuning efficiency by rescaling inner activations with learned vectors, which are then infused into the attention and feedforward modules in a typical transformer-based architecture. These vectors are the only parameters that are trainable during fine-tuning, leaving the original weights untouched. This approach ensures a reduced count of trainable parameters.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The release also features 'auto mapping,' a new paradigm allowing users to load PEFT models with a single line of code through the AutoPeftModelForxxx. Furthermore, users can now apply LoRA to any torch model and upload the adapter weights to the Hub, thanks to PEFT's enhanced flexibility.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The PEFT library now supports Stable Diffusion, being highly extensible and user-friendly for performing tasks like DreamBooth. The community has contributed conversion scripts for PEFT models to be used interchangeably in the Civitai/webui format. With these advancements, the PEFT library further solidifies its standing as a robust tool for AI model fine-tuning.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>General Availability of GPT-4 API Is Announced</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>OpenAI has </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fopenai%2Ecom%2Fblog%2Fgpt-4-api-general-availability&urlhash=V7ZT&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>made its GPT-4 API generally available </a></span><span class>, while also deprecating older models in the Completions API. They also introduced </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fcommunity%2Eopenai%2Ecom%2Ft%2Fopenai-deprecation-summary%2F289539&urlhash=j1LE&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>a new deprecation policy </a></span><span class> that may impact those who are employing OpenAI's models in production.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQHI9h2ygiYzQA/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277182180?e=2147483647&v=beta&t=25CTDH5i7Q9sY-OaC6QolvWulUYnhwtNUlETR7R6fr8">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Users will now be required to test the new models, retrain any fine-tuned versions, and regenerate any embeddings they are currently using. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>This shift emphasizes a considerable advantage of open-source models: the flexibility and control they offer. With open-source models, users have the freedom to continue using them indefinitely and can decide when an upgrade suits their needs best.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Claude 2 from Anthropic Is Here to Rival ChatGPT</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Eanthropic%2Ecom%2F&urlhash=rwLr&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Anthropic </a></span><span class> has introduced its second version of its LLM-powered tool, </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Eanthropic%2Ecom%2Findex%2Fclaude-2&urlhash=KrnB&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Claude 2 </a></span><span class>. The model was trained on data up to early 2023, ensuring it is updated with the latest contextual nuances and semantic developments.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQEUpOOh3GzNLg/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277342387?e=2147483647&v=beta&t=vkC4-qbOawPsI5KguAuVPQHBVrozXi0i27YkuUsRb9o">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Claude 2 </span><span class="font-[700]">enhances the context length capacity up to an impressive 200,000 tokens</span><span class>, although initially, 100,000 tokens will be available at launch. This extended capacity allows for more nuanced, detailed, and contextually aware responses, greatly enhancing its utility across various applications.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Another noteworthy feature of Claude 2 is </span><span class="font-[700]">its ability to upload documents</span><span class>. This feature opens up a myriad of possibilities for users to feed structured and unstructured data directly into the model, enabling it to provide insights, summaries, or other actionable outputs based on the document contents.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In terms of API structure, Claude 2 has been designed to follow better practices. This leads to </span><span class="font-[700]">a more streamlined, user-friendly experience</span><span class> when integrating the model into various platforms or applications, increasing its usability and interoperability.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Claude 2 boasts</span><span class="font-[700]"> advanced coding abilities</span><span class>. Its model can understand, generate, and manipulate code more efficiently, making it a potent tool for software development tasks, code review, and even tutoring in various programming languages.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>To top it all, Anthropic is offering a free beta version of Claude 2 for public use. This offers a fantastic opportunity for developers, researchers, and AI enthusiasts to test the model's capabilities and see firsthand the advancements that have been made in this iteration.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>New Keras for TensorFlow, JAX, and PyTorch</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>François Chollet, a leading artificial intelligence researcher, has introduced a pivotal update, </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fkeras%2Eio%2Fkeras_core%2Fannouncement%2F&urlhash=5fZB&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Keras 3.0 </a></span><span class>, now referred to as Keras "Core." This update positions Keras as a frontend for TensorFlow, JAX, and even PyTorch, marking the first time all three frameworks have been unified under a single frontend.</span> </p> </div> <div class="inline-articles bg-color-surface-new border-hidden rounded-sm p-1 mx-auto my-1.5 mamabear:w-2/3 inline-articles__after"> <h2 class="text-xl font-medium ml-1.5 my-1 text-color-text mamabear:text-center">Recommended by LinkedIn</h2> <div class="flex flex-wrap mx-0.5 babybear:mx-1.5"> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/ht-wired-wisdom-ai-battles-digital-ownership-cherry-picking-t1s2c"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D5612AQFkYcH0anP-wg/article-cover_image-shrink_600_2000/article-cover_image-shrink_600_2000/0/1703132999755?e=2147483647&v=beta&t=JJrishdkt6uQNWEB2B_yiERQ1L-WQF429A4y8DCkYUQ"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">HT Wired Wisdom: AI battles, digital ownership and…</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Hindustan Times </span> <span class="before:middot pt-0.5 inline-article-published-date"> 11 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/google-openais-announcements-mean-end-beginning-ai-wars-jgqfe"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQFN9UFgoa2PrA/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1715962608086?e=2147483647&v=beta&t=I8IvXVjIJdaOsGJUh8QfUCTPTwMnU-LJ5lFxnb0roGM"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">Google and OpenAI's announcements mean "the end of the…</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Fast Company </span> <span class="before:middot pt-0.5 inline-article-published-date"> 6 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/multimodal-race-begins-analytics-india-magazine"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D5612AQEn7T99tpQJxw/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1695641293995?e=2147483647&v=beta&t=bgyRYM9MF-kz_onxlLpznyXqpwf_0z478I881tXX0Qs"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">Multimodal Race Begins</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> AIM </span> <span class="before:middot pt-0.5 inline-article-published-date"> 1 year ago </span> </div> </a>  </div> </div> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQELWpyFIKoFUw/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277413674?e=2147483647&v=beta&t=vB_v4JremjbyYihuraJae3fYH3x3JOd1reRMcZwtFv4">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>This unification implies that developers can utilize either the functional API or the sequential approach across all backends, opening up an unprecedented level of flexibility in the design and execution of machine learning models.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class>One of the highlights of Keras 3.0 is its portability. For example, you can now train a model in PyTorch and subsequently run it in JAX, all without making any modifications to your code. </span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Although there are a few considerations to note, this level of interoperability extends even to different hardware configurations. For instance, a model trained on a GPU using PyTorch should, in theory, be runnable on a TPU using JAX.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Keras 3.0 also excels in minimizing technical debt. Chollet pointed out that the codebase of this latest release is only 30% the size of Keras 2.0, as the project has been entirely rewritten from scratch. This re-engineering effort not only eliminates legacy issues but also ensures the codebase is more maintainable and future-proof, contributing to a more robust and efficient ecosystem for deep learning development.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Extending Context Length with RoPE Scaling</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>As previously mentioned, </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fhuggingface%2Eco%2Fdocs%2Ftransformers%2Fmain%2Fen%2Fmodel_doc%2Fllama%23transformers%2ELlamaConfig%2Erope_scaling&urlhash=L0rW&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Hugging Face Transformers library now includes support for Rotary Position Embeddings (RoPE) scaling </a></span><span class>, an approach that extends the context length of large language models such as Llama, GPT-NeoX, and Falcon. Users can enable this by adjusting a specific parameter in the corresponding model's configuration.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The RoPE scaling technique, initially </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fwww%2Ereddit%2Ecom%2Fr%2FLocalLLaMA%2Fcomments%2F14mrgpr%2Fdynamically_scaled_rope_further_increases%2F&urlhash=09NG&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>proposed by u/emozilla on Reddit </a></span><span class>, allows for the dynamic interpolation of rotary position embeddings to depict lengthier sequences without compromising performance. It enables scaling out the context length of models without the need for fine-tuning.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQEykkgbZI_jeg/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277474448?e=2147483647&v=beta&t=-nGsOPfBBe-pP3Pb0BfKpwl_kAI8asFaDmfAdaz9qgo">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Although the technique provides satisfactory results out of the box, it has been found that performance can be further enhanced by additional fine-tuning. With the integration of RoPE scaling, businesses now have the opportunity to effortlessly expand open-source Large Language Models to context lengths tailored for their specific use cases, offering a significant stride in enhancing the adaptability of such models.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Lost in the Middle: How Language Models Use Long Contexts</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>A team of Stanford researchers recently embarked on </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2307%2E03172&urlhash=N6eL&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>a study to explore how Large Language Models (LLMs) make use of context </a></span><span class>, with a special focus on longer contexts exceeding 32,000 tokens. The goal was to gain a better understanding of how these models handle information in large token windows.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In the course of their study, the researchers tested both open-source models, such as MPT-30B-Instruct and LongChat-13B(16K), and closed-source models, including OpenAI's GPT-3.5-Turbo and Anthropic's Claude 1.3. They used multi-document question-answering setups where the context encompassed several retrieved documents along with a single correct answer, the position of which was randomly shuffled.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The team also employed key-value pair retrieval methods to analyze if performance was affected by longer contexts. This approach provided insights into how LLMs operate with varying levels of context length, and the results could potentially inform future development and tuning of such models.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Major takeaways from the study:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ul> <li><span class>Best Performance occurs when relevant information is at the beginning</span></li> <li><span class>Performance decreases with increases in context length</span></li> <li><span class>Too many retrieved documents harm performance</span></li> <li><span class>Improving the retrieval and prompt creation step with Cross-Encoders (ranking) could potentially boost performance by up to 20%</span></li> <li><span class>Combining Retrieval with Ranking should yield the best performance in RAG for Question Answering</span></li> <li><span class>Extended-context models (GPT-3.5-Turbo vs. GPT-3.5-Turbo (16K)) are not better if the prompt fits the original context</span></li> </ul> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>OpenAI Introduces Superalignment</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>OpenAI has recently announced an ambitious new initiative called "Superalignment." The goal of this project is to foster scientific and technical breakthroughs that will enable smarter steering and control of AI systems, particularly those significantly more advanced. The initiative is set to run over the next four years, co-led by </span><span class> <a href="https://www.linkedin.com/in/ilya-sutskever?trk=article-ssr-frontend-pulse_little-mention" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-mention" data-tracking-will-navigate data-test-link> Ilya Sutskever </a>  </span><span class> and </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fjan%2Eleike%2Ename%2F&urlhash=H-5w&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Jan Leike </a></span><span class>.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQE7NL__TEeK3w/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690277673267?e=2147483647&v=beta&t=il9DkCMUVs85tgQXtFyD6ZeorgjD3-ARsNLbQ5yNX5Q">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>OpenAI is dedicating 20% of its current computational resources to their work on "Superalignment." The ultimate objective is the creation of an automated alignment researcher, operating at a human level of expertise. By harnessing vast computational power, OpenAI aims to iteratively enhance the alignment of superintelligent AI systems, scaling efforts in a way that not only amplifies AI capabilities, but also ensures they remain under effective human control.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>OpenLLM</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fbentoml%2FOpenLLM&urlhash=Zzt_&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>OpenLLM </a></span><span class> is a tool designed to empower the deployment and utilization of any open-source Large Language Models (LLMs). Whether in the cloud or on-premises, OpenLLM's objective is to enable the development of AI applications with ease.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQEuoRcPTIa1aQ/article-inline_image-shrink_400_744/article-inline_image-shrink_400_744/0/1690277928684?e=2147483647&v=beta&t=arW25u3ZjglOEB_-n4ibvsJg64EwdeAGvjfVnm56Jr8">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The platform offers extensive support for various open-source LLMs and model runtimes, such as StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder, and more. It streamlines the process of serving LLMs over RESTful API or gRPC, enabling queries via a user-friendly WebUI, CLI, Python/Javascript clients, or any HTTP client.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>OpenLLM's first-class support for LangChain, BentoML, and Hugging Face allows users to effortlessly create Generative AI applications. This is made possible by enabling the integration of LLMs with other models and services. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>With OpenLLM, users can also generate Docker Images for their LLM server, or deploy as a serverless endpoint via BentoCloud, thereby enhancing the portability and accessibility of their AI services.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>GPT Prompt Engineer</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fmshumer%2Fgpt-prompt-engineer&urlhash=2Fit&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>GPT Prompt Engineer </a></span><span class> is a tool designed to harness the capabilities of GPT-4 and GPT-3.5-Turbo to automatically identify the most effective prompts for a given task. This is accomplished by feeding a task description and test cases into the system. In response, it generates an extensive selection of prompts, each of which is then evaluated based on the provided test cases. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>To rank the prompts according to their effectiveness, the GPT Prompt Engineer utilizes an ELO rating system. Initially, each prompt is assigned a rating of 1200. As the prompts are tested, this rating is adjusted according to their relative performance. This approach offers users a straightforward method for identifying the most efficacious prompts for their specific task. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Further enhancing its capabilities, the GPT Prompt Engineer provides an optional feature allowing users to log such configurations as temperature and max tokens, system and user prompts, test cases, and the resultant ELO ratings. These logs can be accessed on the Weights & Biases platform for future reference and analysis.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>GPT Researcher</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fassafelovic%2Fgpt-researcher&urlhash=b0j3&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>GPT Researcher </a></span><span class> is an autonomous agent that can help to undertake extensive online research on various tasks. This robust tool is capable of generating intricate, objective, and fact-based research reports. This is achieved by utilizing an innovative design inspired by AutoGPT and the recent Plan-and-Solve paper. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>To better accommodate user needs, the GPT Researcher allows customization in directing focus towards pertinent resources, crafting outlines, and synthesizing lessons from the acquired data. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>GPT Researcher addresses key issues concerning speed and determinism that are often present in AI research tools. By employing parallelized operations, as opposed to synchronous methods, it offers a markedly stable performance, along with a substantial increase in speed. </span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQGSVM8TzTD0AQ/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1690278703502?e=2147483647&v=beta&t=PZOYpLy1KZM-OVyQ-yP8d3CiRRteatiiVgGTnSH1jgY">  </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The goal of the GPT Researcher is to equip individuals and organizations with precise, impartial, and factual information. This is achieved by effectively leveraging the power of artificial intelligence, ultimately contributing to more informed decision-making processes.</span> </p> </div>  <hr data-test-id="publishing-divider-block" class="my-[3.2rem] mx-auto h-[1px] border-0 bg-black-a16">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In this edition of the “Provectus AI Review,” we covered the latest releases, updates, instruments, and trends in AI. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>From our analysis of Llama 2 and exploration of Hugging Face, to understanding OpenAI's new policies and the fascinating concept of Superalignment, we hope we have provided a rich perspective of the AI landscape. </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The Provectus team will continue to provide thorough and up-to-date reviews of the latest in AI, keeping you informed about new transformative developments molding the world of AI.</span> </p> </div>  <hr data-test-id="publishing-divider-block" class="my-[3.2rem] mx-auto h-[1px] border-0 bg-black-a16">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Author:</span><span class> </span><span class><a href="https://www.linkedin.com/in/marlon-cajamarca-vega-235577b7?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAABjSk4kBO9g03CRalm4TflbLkeQQmf_swTA&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Marlon Cajamarca Vega </a></span><span class> — Machine Learning Engineer & AI Educator || Provectus || ML Ed Solutions</span> </p> </div>  <hr data-test-id="publishing-divider-block" class="my-[3.2rem] mx-auto h-[1px] border-0 bg-black-a16">  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>Moving Forward — Learn more about Provectus AI expertise</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ol> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fcomparison-large-language-models-biomedical-domain%2F&urlhash=oZyj&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>A Comparison of Large Language Models (LLMs) in Biomedical Domain </a></span></li> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fan-instruction-following-gpt-j-model-based-on-instructions-generated-by-bloom%2F&urlhash=3xIa&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>An Instruction-following GPT-J Model Based on Instructions Generated by Bloom </a></span></li> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fexploring-intelligent-search-solutions-a-comparative-analysis-of-amazon-kendra-integration-and-large-language-model-crawlers%2F&urlhash=12I4&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Exploring Intelligent Search Solutions: A Comparative Analysis of Amazon Kendra Integration and Large Language Model Crawlers </a></span></li> <li><span class><a href="https://www.linkedin.com/pulse/google-io-2023-journey-future-ai-technology-provectus-it-inc/?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Provectus AI review 1 — Google I/O 2023 Overview </a></span></li> <li><span class><a href="https://www.linkedin.com/pulse/false-promise-imitating-proprietary-llms-provectus-perspective?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Provectus AI review 2 — The False Promise of Imitating Proprietary LLMs </a></span></li> <li><span class><a href="https://www.linkedin.com/pulse/progress-gen-ai-open-source-llms-new-product-launches/?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Provectus AI review 3 — Progress in Gen AI and Open-Source LLMs, New Product Launches, and Educational Resources </a></span></li> </ol> </span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/feed/hashtag/artificialintelligence?trk=article-ssr-frontend-pulse_little-text-block" target="_self" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link data-test-id="little-text-block-hashtag">#artificialintelligence </a></span><span class> </span><span class><a href="https://www.linkedin.com/feed/hashtag/machinelearning?trk=article-ssr-frontend-pulse_little-text-block" target="_self" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link data-test-id="little-text-block-hashtag">#machinelearning </a></span><span class> </span><span class><a href="https://www.linkedin.com/feed/hashtag/generativeai?trk=article-ssr-frontend-pulse_little-text-block" target="_self" 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