An update to the Gemma models was released two months after this post, see the latest versions in this collection. Gemma, a new family of state-of-the-art open LLMs, was released today by Google! It’s great to see Google reinforcing its commitment to open-source AI, and we’re excited to fully support the launch with comprehensive integration in Hugging Face. Gemma comes in two sizes: 7B parameters, for efficient deployment and development on consumer-size GPU and TPU and 2B versions for CPU […]

In this blogpost, we will introduce you to the concept of Matryoshka Embeddings and explain why they are useful. We will discuss how these models are theoretically trained and how you can train them using Sentence Transformers. Additionally, we will provide practical guidance on how to use Matryoshka Embedding models and share a comparison between a Matryoshka embedding model and a regular embedding model. Finally, we invite you to check out our interactive demo that showcases the power of these […]

Content warning: since this blog post is about a red-teaming leaderboard (testing elicitation of harmful behavior in LLMs), some users might find the content of the related datasets or examples unsettling. LLM research is moving fast. Indeed, some might say too fast. While researchers in the field continue to rapidly expand and improve LLM performance, there is growing concern over whether these models are capable of realizing increasingly more undesired and unsafe behaviors. In recent months, there has been no […]

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 […]