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.
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"
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)
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Bumps transformers from 4.31.0 to 4.32.0.
Release notes
Sourced from transformers's releases.
... (truncated)
Commits
41aef33
v4.32.0: Release26e4c33
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
Hotfix26a38a9
reattach hooks when usingresize_token_embeddings
(#25596)3665af0
new model: IDEFICS via HuggingFaceM4 (#24796)3c99f24
🌐 [i18n-KO] Translatedperf_train_tpu_tf.md
to Korean (#25433)2efa0d4
Make TTS automodels importable (#25595)ef15342
[TokenizerFast
] Fix setting prefix space in init (#25563)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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