Beside large scale language modeling , it also achieve SOTA on many other tasks, with very little tweak.

It’s here! Meet Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights • 8B and 70B parameter openly available pre-trained and fine-tuned models. • Trained on more

The long-awaited LLAMA3!!

Welp folks, we have gpt-4 at home

Goodbye Hallucinations! Today, Cleanlab launches the Trustworthy Language Model (TLM 1.0), addressing the biggest problem in Generative AI: reliability. technologyreview.com/2024/04/25/109…

I’m excited to announce our latest paper, introducing a family of early-fusion token-in token-out (gpt4o….), models capable of interleaved text and image understanding and generation. arxiv.org/abs/2405.09818

Excited to release our work from last year showcasing a stable training recipe for fully token-based multi-modal early-fusion auto-regressive models! arxiv.org/abs/2405.09818 Huge shout out to Armen Aghajanyan Ramakanth Luke Zettlemoyer Gargi Ghosh and other co-authors. (1/n)

We found that the uncontrolled growth of output norms is an early indicator of future training divergence. More shout-outs: Victoria X Lin Chunting Zhou Lili Yu (4/n)

🚀 Excited to introduce Chameleon, our latest breakthrough in mixed-modal early-fusion foundation models! 🦎✨ Capable of understanding and generating text and images in any sequence. Check out our paper to learn more about its SOTA performance and versatile capabilities! 🌟

Interleaving text image generation with consistency is a unique feature bought by our early-fusing end to end training model.

We are better than all other multimodal baseline on interleaving generation.

The team is working very hard to make this happen. Armen Aghajanyan Srini Iyer Gargi Ghosh Luke Zettlemoyer

Such a fun coincident on picking the same name. Before scaling up, we called it CM3leon (pronounced as chameleon, with a twist to older cm3 paper) last year, in the paper "Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning" (arxiv.org/abs/2309.02591).

Extremely proud of the team who made it happen. Armen Aghajanyan Srini Iyer Luke Zettlemoyer Asli Celikyilmaz Victoria X Lin Scott Yih Lili Yu and many others!

Super excited to open-source Chameleon 7B and 34B model weights today. Finally 🎇

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model abs: arxiv.org/abs/2408.11039 New paper from Meta that introduces Transfusion, a recipe for training a model that can seamlessly generate discrete and continuous modalities. The authors pretrain a

Transfusion Predict the Next Token and Diffuse Images with One Multi-Modal Model discuss: huggingface.co/papers/2408.11… We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function

Meta presents Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model - Can generate images and text on a par with similar scale diffusion models and language models - Compresses each image to just 16 patches arxiv.org/abs/2408.11039

🚀 Excited to share our latest work: Transfusion! A new multi-modal generative training combining language modeling and image diffusion in a single transformer! Huge shout to Chunting Zhou Omer Levy Michi Yasunaga Arun Babu Kushal Tirumala and other collaborators.

New research paper from Meta FAIR – Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model. Chunting Zhou, Lili Yu and team introduce this recipe for training a multi-modal model over discrete and continuous data. Transfusion combines next token