SetFitABSA is an efficient technique to detect the sentiment towards specific aspects within the text. Aspect-Based Sentiment Analysis (ABSA) is the task of detecting the sentiment towards specific aspects within the text. For example, in the sentence, “This phone has a great screen, but its battery is too small”, the aspect terms are “screen” and “battery” and the sentiment polarities towards them are Positive and Negative, respectively. ABSA is widely used by organizations for extracting valuable insights by analyzing customer […]

There is a second iteration (Feb 2026) of the blog post where we cover how the transformers library has built around MoEs to make them “first class citizens” of the library and the Hub. Here is the link to the post: Mixture of Experts (MoEs) in Transformers With the release of Mixtral 8x7B (announcement, model card), a class of transformer has become the hottest topic in the open AI community: Mixture of Experts, or MoEs for short. In this blog […]

Mixtral 8x7b is an exciting large language model released by Mistral today, which sets a new state-of-the-art for open-access models and outperforms GPT-3.5 across many benchmarks. We’re excited to support the launch with a comprehensive integration of Mixtral in the Hugging Face ecosystem 🔥! Among the features and integrations being released today, we have: Table of Contents What is    

2023 has seen a surge of public interest in Large Language Models (LLMs), and now that most people have an idea of what they are and can do, the public debates around open versus closed source have reached a wide audience as well. At Hugging Face, we follow open models with great interest, as they allow research to be reproducible, empower the    

Open AI’s Whisper is a general purpose speech transcription model that achieves state-of-the-art results across a range of different benchmarks and audio conditions. The latest large-v3 model tops the OpenASR Leaderboard, ranking as the best open-source speech transcription model for English. The model also demonstrates strong multilingual performance, achieving less    

A community derived guide to some of the SOTA practices for SD-XL Dreambooth LoRA fine tuning TL;DR We combined the Pivotal Tuning technique used on Replicate’s SDXL Cog trainer with the Prodigy optimizer used in the Kohya trainer (plus a bunch of other optimizations) to achieve very good results on training Dreambooth LoRAs for SDXL. Check    

We’re excited to present an efficient non-diffusion text-to-image model named aMUSEd. It’s called so because it’s a open reproduction of Google’s MUSE. aMUSEd’s generation quality is not the best and we’re releasing a research preview with a permissive license. In contrast to the commonly used latent diffusion approach (Rombach et al. (2022)), aMUSEd employs a Masked Image Model (MIM) methodology. This not only requires fewer inference steps, as noted by Chang et al. (2023), but also enhances the model’s interpretability. […]

Pulling your hair out because LLM fine-tuning is taking forever? In this post, we introduce a lightweight tool developed by the community to make LLM fine-tuning go super fast! Before diving into Unsloth, it may be helpful to read our QLoRA blog post, or be familiar with LLM fine-tuning using the 🤗 PEFT library.

Hugging Face’s Open LLM Leaderboard (originally created by Ed Beeching and Lewis Tunstall, and maintained by Nathan Habib and Clémentine Fourrier) is well known for tracking the performance of open source LLMs, comparing their performance in a variety of tasks, such as TruthfulQA or HellaSwag. This has been of tremendous value to the open-source community, as it provides a way for practitioners to keep track of the best open-source models. In late 2023, at Vectara we introduced the Hughes Hallucination […]

Index: Intro In this tutorial I will present a step-by-step guide on how to convert a complex ComfyUI workflow to a simple Gradio application, and how to deploy this application on Hugging Face Spaces ZeroGPU serverless structure,