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yujonglee / eval

Evaluate your LLM apps, RAG pipeline, any generated text, and more!
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Bump transformers from 4.33.2 to 4.35.0 #179

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps transformers from 4.33.2 to 4.35.0.

Release notes

Sourced from transformers's releases.

Safetensors serialization by default, DistilWhisper, Fuyu, Kosmos-2, SeamlessM4T, Owl-v2

New models

Distil-Whisper

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. It was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

Distil-Whisper copies the entire encoder from Whisper, meaning it retains Whisper's robustness to different audio conditions. It only copies 2 decoder layers, which significantly reduces the time taken to auto-regressively generate text tokens:

Distil-Whisper is MIT licensed and directly available in the Transformers library with chunked long-form inference, Flash Attention 2 support, and Speculative Decoding. For details on using the model, refer to the following instructions.

Joint work from @​sanchit-gandhi, @​patrickvonplaten and @​srush.

Fuyu

image

The Fuyu model was created by ADEPT, and authored by Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar.

The authors introduced Fuyu-8B, a decoder-only multimodal model based on the classic transformers architecture, with query and key normalization. A linear encoder is added to create multimodal embeddings from image inputs.

By treating image tokens like text tokens and using a special image-newline character, the model knows when an image line ends. Image positional embeddings are removed. This avoids the need for different training phases for various image resolutions. 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.

Joint work from @​molbap, @​pcuenca, @​amyeroberts, @​ArthurZucker

SeamlessM4T

image

The SeamlessM4T model was proposed in SeamlessM4T — Massively Multilingual & Multimodal Machine Translation by the Seamless Communication team from Meta AI.

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.

SeamlessM4T enables multiple tasks without relying on separate models:

  • Speech-to-speech translation (S2ST)
  • Speech-to-text translation (S2TT)
  • Text-to-speech translation (T2ST)
  • Text-to-text translation (T2TT)
  • Automatic speech recognition (ASR)

... (truncated)

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dependabot[bot] commented 1 year ago

Superseded by #182.