We recently announced that Gemma, the open weights language model from Google Deepmind, is available for the broader open-source community via Hugging Face. It’s available in 2 billion and 7 billion parameter sizes with pretrained and instruction-tuned flavors. It’s available on Hugging Face, supported in TGI, and easily accessible for deployment and fine-tuning in the Vertex Model Garden and Google Kubernetes Engine. The Gemma family of models also happens to be well suited for prototyping and experimentation using the free […]

In recent months, we’ve seen multiple news stories involving ‘deepfakes’, or AI-generated content: from images of Taylor Swift to videos of Tom Hanks and recordings of US President Joe Biden. Whether they are selling products, manipulating images of people without their consent, supporting phishing for private information, or creating misinformation materials intended to mislead voters, deepfakes are increasingly being shared on social media platforms. This enables them to be quickly propagated and have a wider reach and therefore, the potential […]

Automated measurement of the quality of text-to-speech (TTS) models is very difficult. Assessing the naturalness and inflection of a voice is a trivial task for humans, but it is much more difficult for AI. This is why today, we’re thrilled to announce the TTS Arena. Inspired by LMSys‘s Chatbot Arena for LLMs, we developed a tool that allows anyone to easily compare TTS models side-by-side. Just submit some text, listen to two different models speak it out, and vote on […]

BigCode is releasing StarCoder2, the next generation of transparently trained open code LLMs. All StarCoder2 variants were trained on The Stack v2, a new large and high-quality code dataset. We release all models, datasets, and the processing as well as the training code. Check out the paper for details. What is StarCoder2? StarCoder2 is a family of open LLMs for code and comes in 3 different sizes with    

Recently, Argilla and Hugging Face launched Data is Better Together, an experiment to collectively build a preference dataset of prompt rankings. In a few days, we had: 350 community contributors labeling data Over 11,000 prompt    

Models are becoming quite good at understanding text on its own, but what about text in images, which gives important contextual information? For example, navigating a map, or understanding a meme? The ability to reason about the interactions between the text and visual context in images can power many real-world applications, such as AI assistants, or tools to assist the visually impaired. We refer to these tasks as “context-sensitive text-rich visual reasoning tasks”. At the moment, most evaluations of instruction-tuned […]

In the world of web development, turning designs into functional websites usually involves a lot of coding and careful testing. What if we could simplify this process, making it possible to convert web designs into working websites more easily and quickly? WebSight is a new dataset that aims at building AI systems capable of transforming screenshots to HTML code. The challenge Turning a website design or screenshot into HTML code usually needs an experienced developer. But what if    

Embedding models are useful for many applications such as retrieval, reranking, clustering, and classification. The research community has witnessed significant advancements in recent years in embedding models, leading to substantial enhancements in all applications building on semantic representation. Models such as BGE, GTE, and E5 are placed at the top of the MTEB benchmark and in some cases outperform proprietary embedding services. There are a variety of model sizes found in Hugging Face’s Model hub, from lightweight (100-350M parameters) to […]

Quantization is a technique to reduce the computational and memory costs of evaluating Deep Learning Models by representing their weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32). Reducing the number of bits means the resulting model requires less memory storage, which is crucial for deploying Large Language Models on consumer devices. It also enables specific optimizations for lower bitwidth datatypes, such as int8 or float8 matrix multiplications on CUDA […]