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v4.31.0: Llama v2, MusicGen, Bark, MMS, EnCodec, InstructBLIP, Umt5, MRa, vIvIt
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@LysandreJik LysandreJik released this 18 Jul 20:16
· 3895 commits to main since this release
v4.31.0
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sgugger Sylvain Gugger
GPG key ID: 4198F83D867EFC2E
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New models

Llama v2

Llama 2 was proposed in LLaMA: Open Foundation and Fine-Tuned Chat Models by Hugo Touvron et al. It builds upon the Llama architecture adding Grouped Query Attention for efficient inference.

Musicgen

The MusicGen model was proposed in the paper Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.

MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes, conditioned on these hidden-states. These audio tokens are then decoded using an audio compression model, such as EnCodec, to recover the audio waveform.

Through an efficient token interleaving pattern, MusicGen does not require a self-supervised semantic representation of the text/audio prompts, thus eliminating the need to cascade multiple models to predict a set of codebooks (e.g. hierarchically or upsampling). Instead, it is able to generate all the codebooks in a single forward pass.

Bark

Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark.

MMS

The MMS model was proposed in Scaling Speech Technology to 1,000+ Languages by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli

EnCodec

The EnCodec neural codec model was proposed in High Fidelity Neural Audio Compression by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.

InstructBLIP

The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. InstructBLIP leverages the BLIP-2 architecture for visual instruction tuning.

Umt5

The UMT5 model was proposed in UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant.

MRA

The MRA model was proposed in Multi Resolution Analysis (MRA) for Approximate Self-Attention by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh.

ViViT

The Vivit model was proposed in ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid. The paper proposes one of the first successful pure-transformer based set of models for video understanding.

Python 3.7

The last version to support Python 3.7 was 4.30.x, as it reached end-of-life on June 27, 2023 and is no longer supported by the Python Software Foundation.

PyTorch 1.9

The last version to support PyTorch 1.9 was 4.30.x. As it has been more than 2 years, and we're looking forward to using features available in PyTorch 1.10 and up, we do not support PyTorch 1.9 for v4.31 and up.

RoPE scaling

This PR adds RoPE scaling to the LLaMa and GPTNeoX families of models. It allows us to extrapolate and go beyond the original maximum sequence length (e.g. 2048 tokens on LLaMA), without fine-tuning. It offers two strategies:

  • Linear scaling
  • Dynamic NTK scaling

Agents

Tools now return a type that is specific to agents. This type can return a serialized version of itself (a string), that either points to a file on-disk or to the object's content. This should make interaction with text-based systems much simpler.

Tied weights load

Models with potentially tied weights dropped off some keys from the state dict even when the weights were not tied. This has now been fixed and more generally, the whole experience of loading a model with state dict that don't match exactly should be improved in this release.

Whisper word-level timestamps

This PR adds a method of predicting timestamps at the word (or even token) level, by analyzing the cross-attentions and applying dynamic time warping.

Auto model addition

A new auto model is added, AutoModelForTextEncoding. It is to be used when you want to extract the text encoder from an encoder-decoder architecture.

Model deprecation

Transformers is growing a lot and to ease a bit the burden of maintenance on our side, we have taken the decision to deprecate models that are not used a lot. Those models will never actually disappear from the library, but we will stop testing them or accepting PRs modifying them.
(enfin ça
The criteria to identify models to deprecate was less than 1,000 unique downloads in the last 30 days for models that are at least one year old. The list of deprecated models is:

  • BORT
  • M-CTC-T
  • MMBT
  • RetriBERT
  • TAPEX
  • Trajectory Transformer
  • VAN

Breaking changes

Fixes an issue with stripped spaces for the T5 family tokenizers. If this impacts negatively inference/training with your models, please let us know by opening an issue.

Bugfixes and improvements

Significant community contributions

The following contributors have made significant changes to the library over the last release:

  • @hollance
    • [WIP] add EnCodec model (#23655)
    • add word-level timestamps to Whisper (#23205)
    • add missing alignment_heads to Whisper integration test (#24487)
  • @sim-so
    • 🌐 [i18n-KO] Fixed tutorial/preprocessing.mdx (#24156)
    • 🌐 [i18n-KO] Translated custom_tools.mdx to Korean (#24580)
  • @novice03
    • Add Multi Resolution Analysis (MRA) (New PR) (#24513)
  • @jegork

Contributors

kashif, lig, and 105 other contributors
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