This blog post introduces ModernBERT, a family of state-of-the-art encoder-only models representing improvements over older generation encoders across the board, with a 8192 sequence length, better downstream performance and much faster processing. ModernBERT is available as a slot-in replacement for any BERT-like models, with both a base (149M params) and large (395M params) model size. Click to see how to use these models with transformers ModernBERT will be included in v4.48.0 of transformers. Until then, it requires installing transformers from […]

The emergence of native Speech to Speech models offers exciting opportunities to increase voice agent capabilities and simplify speech-enabled workflows. However, it’s crucial to evaluate whether this simplification comes at the cost of model performance or introduces    

Generating text with language models often involves selecting the next token based on a distribution of probabilities. A straightforward approach like greedy search selects the most probable token, but this can result in generic or repetitive outputs.    

You must be familiar with this message 🤬: RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 7.93 GiB total capacity; 6.00 GiB already allocated; 14.88 MiB free; 6.00 GiB reserved in total by PyTorch) While it’s easy to see that GPU memory is full, understanding why and how to fix it can be more challenging. In    

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