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Bump transformers from 4.25.1 to 4.36.2 #455

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@dependabot dependabot bot commented on behalf of github Dec 18, 2023

Bumps transformers from 4.25.1 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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Bumps [transformers](https://github.com/huggingface/transformers) from 4.25.1 to 4.36.2.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.25.1...v4.36.2)

---
updated-dependencies:
- dependency-name: transformers
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Dec 18, 2023
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