SeamlessM4T enables multiple tasks without relying on separate models: SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. The SeamlessM4T model was proposed in SeamlessM4T - Massively Multilingual & Multimodal Machine Translation by the Seamless Communication team from Meta AI. Fuyu: improve image processing by in #27007.Joint work from Add fuyu model by in #26911

With 8 billion parameters and licensed under CC-BY-NC, Fuyu-8B is notable for its ability to handle both text and images, its impressive context size of 16K, and its overall performance. This avoids the need for different training phases for various image resolutions. A linear encoder is added to create multimodal embeddings from image inputs.īy treating image tokens like text tokens and using a special image-newline character, the model knows when an image line ends. The authors introduced Fuyu-8B, a decoder-only multimodal model based on the classic transformers architecture, with query and key normalization. The Fuyu model was created by ADEPT, and authored by Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar.

Add WhisperForCausalLM for speculative decoding by in #27195.

Joint work from and Improve Encoder Decoder by in #26701 For details on using the model, refer to the following instructions. It only copies 2 decoder layers, which significantly reduces the time taken to auto-regressively generate text tokens:ĭistil-Whisper is MIT licensed and directly available in the Transformers library with chunked long-form inference, Flash Attention 2 support, and Speculative Decoding.

It was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling.ĭistil-Whisper copies the entire encoder from Whisper, meaning it retains Whisper's robustness to different audio conditions. Distil-Whisper is a distilled version of Whisper that is 6 times faster, 49% smaller, and performs within 1% word error rate (WER) on out-of-distribution data.

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