build(deps): bump transformers from 4.35.0 to 4.36.2

[dependabot]

Bumps transformers from 4.35.0 to 4.36.2.

Release notes

Sourced from transformers's releases.

Patch release: v4.36.2

Patch release to resolve some critical issues relating to the recent cache refactor, flash attention refactor and training in the multi-gpu and multi-node settings:

• Resolve training bug with PEFT + GC #28031
• Resolve cache issue when going beyond context window for Mistral/Mixtral FA2 #28037
• Re-enable passing config to from_pretrained with FA #28043
• Fix resuming from checkpoint when using FDSP with FULL_STATE_DICT #27891
• Resolve bug when saving a checkpoint in the multi-node setting #28078

Patch release: v4.36.1

A patch release for critical torch issues mostly:

• Fix SDPA correctness following torch==2.1.2 regression #27973
• [Tokenizer Serialization] Fix the broken serialisation #27099
• Fix bug with rotating checkpoints #28009
• Hot-fix-mixstral-loss (#27948)

🔥

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")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]

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

... (truncated)

Commits

• a7cab3c Release: v4.36.2
• f6d6189 Fix bug for checkpoint saving on multi node training setting (#28078)
• 64bcf77 fix resuming from ckpt when using FSDP with FULL_STATE_DICT (#27891)
• 780376f [Modeling / Mixtral] Fix GC + PEFT issues with Mixtral (#28061)
• 6e4429f [FA-2] Fix fa-2 issue when passing config to from_pretrained (#28043)
• f33b061 Generate: Mistral/Mixtral FA2 cache fix when going beyond the context window ...
• d1dec79 [core / modeling] Fix training bug with PEFT + GC (#28031)
• c48787f fix seamless import
• bd65410 Release: v4.36.1
• 6342b9b Fix bug with rotating checkpoints (#28009)
• Additional commits viewable in compare view

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@dependabot merge

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[dependabot]

Superseded by #150.