Bump transformers from 4.31.0 to 4.32.0

[dependabot]

Bumps transformers from 4.31.0 to 4.32.0.

Release notes

Sourced from transformers's releases.

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):

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TheBloke/Llama-2-13B-chat-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

For more information about this feature, we recommend taking a look at the following announcement blogpost: https://huggingface.co/blog/gptq-integration

... (truncated)

Commits

• 41aef33 v4.32.0: Release
• 26e4c33 Fix test_modeling_mpt typo in model id (#25606)
• 5ddafec Run doctest for new files (#25588)
• 976bd73 Ignore all exceptions from signal in dynamic code (#25623)
• be94bc5 Hotfix
• 26a38a9 reattach hooks when using resize_token_embeddings (#25596)
• 3665af0 new model: IDEFICS via HuggingFaceM4 (#24796)
• 3c99f24 🌐 [i18n-KO] Translated perf_train_tpu_tf.md to Korean (#25433)
• 2efa0d4 Make TTS automodels importable (#25595)
• ef15342 [TokenizerFast] Fix setting prefix space in init (#25563)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #291.