Discover how to build a Chatbot for a tool of your choice (Argilla 2.0 in this case) that can understand technical documentation and chat with users about it. In this article, we’ll show you how to leverage distilabel and fine-tune a domain-specific embedding model to create a conversational model that’s both accurate and engaging. This article outlines the process of creating a Chatbot for Argilla 2.0. We will: create a synthetic dataset from the technical documentation to fine-tune a domain-specific […]

This blog post introduces SmolLM, a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset. It covers data curation, model evaluation, and usage. Introduction There is increasing interest in small language models that can operate on local devices. This trend involves techniques such as distillation or quantization to compress large models, as well as training small models from scratch on large datasets. These approaches enable novel applications while dramatically    

Are you tired of the complexity and expense of managing multiple AI models? What if you could deploy once and serve 30 models? In today’s ML world, organizations looking to leverage the value of their data will likely end up in a fine-tuned world, building a multitude of models, each one highly specialized for a specific task. But how can you keep up with the hassle and cost of deploying a model for each use case? The answer is Multi-LoRA […]

With this blog we are releasing Docmatix – a huge dataset for Document Visual Question Answering (DocVQA) that is 100s of times larger than previously available. Ablations using this dataset for fine-tuning Florence-2 show a 20% increase in    

WWDC’ 24 is the moment Apple officially unveiled Apple Intelligence and reiterated their commitment to efficient, private, and on-device AI. During the keynote and the sessions that followed, they demonstrated Apple Intelligence, which powers a huge array of AI-enhanced features that show practical uses for everyday tasks. These are not *AI-for-the-sake-of-AI* shiny demos. These are time-saving, appropriate (and fun!) helpers that are deeply integrated with apps and the OS, that also offer developers a number of ways to include these […]

Llama 3.1 is out! Today we welcome the next iteration of the Llama family to Hugging Face. We are excited to collaborate with Meta to ensure the best integration in the Hugging Face ecosystem. Eight open-weight models (3 base models and 5 fine-tuned ones) are available on the Hub. Llama 3.1 comes in three sizes: 8B for efficient deployment and development on consumer-size GPU, 70B for large-scale AI native applications, and 405B for synthetic data, LLM as a Judge or […]

While developing Docmatix, we noticed that fine-tuning Florence-2 on it yielded great performance on DocVQA, but resulted in low scores on the benchmark. To enhance performance, we had to fine-tune the model further on DocVQA to learn the syntax    

Update: This service is deprecated and no longer available as of April 10th, 2025. For an alternative, you should consider Inference Providers Today, we are thrilled to announce the launch of Hugging Face NVIDIA NIM API    

Over the past few months, we have seen an emergence in the use of Transformer-based diffusion backbones for high-resolution text-to-image (T2I) generation. These models use the transformer architecture as the building block for the diffusion process, instead of the    

One month after the release of Gemma 2, Google has expanded their set of Gemma models to include the following new additions: Gemma 2 2B – The 2.6B parameter version of Gemma 2, making it a great candidate for on-device use. ShieldGemma – A series of safety classifiers, trained on top of Gemma 2, for developers to filter inputs and outputs of their applications. Gemma Scope – A comprehensive, open suite of sparse autoencoders for Gemma 2 2B and 9B. […]