v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2, AMD ROCm, F.sdpa wide-spread support
New model additions
Mixtral
Mixtral is the new open-source model from Mistral AI announced by the blogpost Mixtral of Experts. The model has been proven to have comparable capabilities to Chat-GPT according to the benchmark results shared on the release blogpost.
The architecture is a sparse Mixture of Experts with Top-2 routing strategy, similar as NllbMoe architecture in transformers. You can use it through AutoModelForCausalLM interface:
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B", torch_dtype=torch.float16, device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")
The model is compatible with existing optimisation tools such Flash Attention 2, bitsandbytes and PEFT library. The checkpoints are release under mistralai organisation on the Hugging Face Hub.
Llava / BakLlava
Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions.
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Bumps transformers from 4.30.0 to 4.36.0.
Release notes
Sourced from transformers's releases.
... (truncated)
Commits
1466677
Release: v4.36.0accccdd
[Add Mixtral
] Adds support for the Mixtral MoE (#27942)0676d99
[from_pretrained
] Make from_pretrained fast again (#27709)9f18cc6
Fix SDPA dispatch & make SDPA CI compatible with torch<2.1.1 (#27940)7ea21f1
[LLaVa] Some improvements (#27895)5e620a9
FixSeamlessM4Tv2ModelIntegrationTest
(#27911)e96c1de
SkipUnivNetModelTest::test_multi_gpu_data_parallel_forward
(#27912)8d8970e
[BEiT] Fix test (#27934)235be08
[DETA] fix backbone freeze/unfreeze function (#27843)df5c5c6
Fix typo (#27918)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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