Today we are launching smolagents, a very simple library that unlocks agentic capabilities for language models. Here’s a glimpse: from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) agent.run(“How many seconds would it take for a leopard at full speed to run through Pont des Arts?”) Table of Contents

Since June 2024, we have evaluated more than 3,000 models on the Open LLM Leaderboard, a worldwide ranking of open language models performance. Even though we’re trying to run evaluations without wasting resources (we use the spare cycles of our cluster, in other words the GPUs which are active but waiting between jobs), this still represents quite a big amount of energy spent for model inference! In the last year, people have become more and more aware that using large […]

TL;DR: We present vdr-2b-multi-v1, the best multilingual embedding model for visual document retrieval. We also release its English-only twin vdr-2b-v1 and open-source the new vdr-multilingual-train dataset. With 500k high-quality samples, it’s the largest open-source multilingual    

Introduction The sudden, rapid advancement of LLM capabilities – such as writing fluent sentences and achieving increasingly high scores on benchmarks – has led AI developers and businesses alike to look towards what comes next: What game-changing technology is just on the horizon? One technology very recently taking off is “AI agents”, systems that can take actions in the digital world aligned with a deployer’s goals. Most of today’s AI agents    

This blog post introduces a method to train static embedding models that run 100x to 400x faster on CPU than state-of-the-art embedding models, while retaining most of the quality. This unlocks a lot of exciting use cases, including on-device and in-browser execution, edge computing, low power and embedded applications. We apply this recipe to train two extremely efficient embedding models: sentence-transformers/static-retrieval-mrl-en-v1    

Since its initial release in 2022, Text-Generation-Inference (TGI) has provided Hugging Face and the AI Community with a performance-focused solution to easily deploy large-language models (LLMs). TGI initially offered an almost no-code solution to load models from the Hugging    

Get lightning-fast inference, quick quantization, torch.compile boosts, and effortless fine-tuning for any timm model—all within the friendly 🤗 transformers ecosystem. Enter TimmWrapper—a simple, yet powerful tool that unlocks this potential. In this post, we’ll cover: How the timm integration works and why it’s a game-changer. How to integrate timm models with 🤗 transformers. Practical examples: pipelines, quantization, fine-tuning, and more. To follow along with this blog post, install the latest version of transformers and timm by running: pip install -Uq […]

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TL;DR: KVPress packs the latest KV cache compression techniques, enabling memory-efficient long-context LLMs. 🚀 One of the key features of Large Language Models (LLMs) is their context window—the maximum number of tokens they can process    

You hypocrite, first take the log out of your own eye, and then you will see clearly to take the speck out of your brother’s eye. Matthew 7, 3-5