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,    

SD Turbo and SDXL Turbo are two fast generative text-to-image models capable of generating viable images in as little as one step, a significant improvement over the 30+ steps often required with previous Stable Diffusion models. SD Turbo is a distilled version of Stable Diffusion 2.1, and SDXL Turbo is a distilled version of SDXL 1.0. We’ve previously shown how to accelerate Stable Diffusion inference with ONNX Runtime. Not only does ONNX Runtime provide performance benefits when used with SD […]

Addendum After consulting with the authors of the IPO paper, we discovered that the implementation of IPO in TRL was incorrect; in particular, the loss over the log-likelihoods of the completions needs to be averaged instead of summed. We have added a fix in this PR and re-run the experiments. The results are now consistent with the paper, with IPO on par with DPO and performing better than KTO in the paired preference setting. We have updated the post to […]

PatchTSMixer is a lightweight time-series modeling approach based on the MLP-Mixer architecture. It is proposed in TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting by IBM Research authors Vijay Ekambaram, Arindam Jati, Nam Nguyen, Phanwadee Sinthong and Jayant Kalagnanam. For effective mindshare and to promote open-sourcing – IBM Research joins hands with the HuggingFace team to release this model in the Transformers library. In the Hugging Face implementation, we provide PatchTSMixer’s capabilities to effortlessly facilitate lightweight mixing across patches, […]

New (01/2024): This blog post is strongly inspired by “Fine-tuning XLS-R on Multi-Lingual ASR” and “Fine-tuning MMS Adapter Models for Multi-Lingual ASR”. Introduction Last month, MetaAI released Wav2Vec2-BERT, as a    

Open-source LLMs have now reached a performance level that makes them suitable reasoning engines for powering agent workflows: Mixtral even surpasses GPT-3.5 on our benchmark, and its performance could easily be further enhanced with fine-tuning. We’ve released the simplest agentic library out there: smolagents! Go checkout the smolagents introduction blog here. Introduction Large Language Models (LLMs) trained for causal language modeling can tackle a wide range of    

11/13/2025 Update: we announced a new and deeper partnership with Google Cloud to enable companies to build their own AI with open