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Bump transformers from 4.39.3 to 4.40.1
Bumps transformers from 4.39.3 to 4.40.1.
Release notes
Sourced from transformers's releases.
v4.40.1: fix
EosTokenCriteriaforLlama3onmpsKudos to
@pcuencafor the prompt fix in:
- Make EosTokenCriteria compatible with mps #30376
To support
EosTokenCriteriaon MPS whilepytorchadds this functionality.v4.40.0: Llama 3, Idefics 2, Recurrent Gemma, Jamba, DBRX, OLMo, Qwen2MoE, Grounding Dino
New model additions
Llama 3
Llama 3 is supported in this release through the Llama 2 architecture and some fixes in the
tokenizerslibrary.Idefics2
The Idefics2 model was created by the Hugging Face M4 team and authored by Léo Tronchon, Hugo Laurencon, Victor Sanh. The accompanying blog post can be found here.
Idefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon IDEFICS-1, notably on document understanding, OCR, or visual reasoning. Idefics2 is lightweight (8 billion parameters) and treats images in their native aspect ratio and resolution, which allows for varying inference efficiency.
- Add Idefics2 by
@amyerobertsin #30253Recurrent Gemma
Recurrent Gemma architecture. Taken from the original paper.
The Recurrent Gemma model was proposed in RecurrentGemma: Moving Past Transformers for Efficient Open Language Models by the Griffin, RLHF and Gemma Teams of Google.
The abstract from the paper is the following:
We introduce RecurrentGemma, an open language model which uses Google’s novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.
- Add recurrent gemma by
@ArthurZuckerin #30143Jamba
Jamba is a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and an overall of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU.
As depicted in the diagram below, Jamba’s architecture features a blocks-and-layers approach that allows Jamba to successfully integrate Transformer and Mamba architectures altogether. Each Jamba block contains either an attention or a Mamba layer, followed by a multi-layer perceptron (MLP), producing an overall ratio of one Transformer layer out of every eight total layers.
Jamba introduces the first
HybridCacheobject that allows it to natively support assisted generation, contrastive search, speculative decoding, beam search and all of the awesome features from thegenerateAPI!
... (truncated)
Commits
9fe3f58v4.40.1f8fec6bMake EosTokenCriteria compatible with mps (#30376)745bbfeRelease: v4.40.05728b5aFIX: Fixes unexpected behaviour for Llava / LLama & AWQ Fused modules + rever...005b957Add DBRX Model (#29921)63c5e27Do not drop mask with SDPA for more cases (#30311)acab997Revert "Re-enable SDPA's FA2 path (#30070)" (#30314)7509a0aFix RecurrentGemma device_map (#30273)9459efbAdd atol for sliding window test (#30303)3f20877Add jamba (#29943)- Additional commits viewable in compare view
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Dependency Review
✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.OpenSSF Scorecard
| Package | Version | Score | Details | ||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pip/transformers | 4.40.1 | :green_circle: 5.8 | Details
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| pip/transformers | 4.39.3 | :green_circle: 5.8 | Details
|
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