Bump transformers from 4.31.0 to 4.32.1

[dependabot]

Bumps transformers from 4.31.0 to 4.32.1.

Release notes

Sourced from transformers's releases.

Patch release: v4.32.1

Patch release including several patches from v4.31.0, listed below:

• Put IDEFICS in the right section of the doc (#25650)
• removing unnecesssary extra parameter (#25643)
• [SPM] Patch spm Llama and T5 (#25656)
• Fix bloom add prefix space (#25652)
• Generate: add missing logits processors docs (#21440)
• [idefics] small fixes (#25764)

IDEFICS, GPTQ Quantization

IDEFICS

The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh

IDEFICS is the first open state-of-the-art visual language model at the 80B scale!

The model accepts arbitrary sequences of image and text and produces text, similarly to a multimodal ChatGPT.

Blogpost: hf.co/blog/idefics Playground: HuggingFaceM4/idefics_playground

• new model: IDEFICS via HuggingFaceM4 by @​stas00 in #24796

MPT

MPT has been added and is now officially supported within Transformers. The repositories from MosaicML have been updated to work best with the model integration within Transformers.

• [MPT] Add MosaicML's MPT model to transformers by @​ArthurZucker & @​younesbelkada in #24629

GPTQ Integration

GPTQ quantization is now supported in Transformers, through the optimum library. The backend relies on the auto_gptq library, from which we use the GPTQ and QuantLinear classes.

See below for an example of the API, quantizing a model using the new GPTQConfig configuration utility.

from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)

Most models under TheBloke namespace with the suffix GPTQ should be supported, for example, to load a GPTQ quantized model on TheBloke/Llama-2-13B-chat-GPTQ simply run (after installing latest optimum and auto-gptq libraries):

... (truncated)

Commits

• ccb92be Release: v4.32.1
• 657eb26 Skip broken tests
• 13aef13 [idefics] small fixes (#25764)
• 6836e9d Generate: add missing logits processors docs (#25653)
• e82040e Fix bloom add prefix space (#25652)
• 9d42e40 [SPM] Patch spm Llama and T5 (#25656)
• 27f9157 removing unnecesssary extra parameter (#25643)
• 6a029a8 Put IDEFICS in the right section of the doc (#25650)
• 41aef33 v4.32.0: Release
• 26e4c33 Fix test_modeling_mpt typo in model id (#25606)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #60.