Bump transformers from 4.33.2 to 4.34.0

[dependabot]

Bumps transformers from 4.33.2 to 4.34.0.

Release notes

Sourced from transformers's releases.

v4.34: Mistral, Persimmon, Prompt templating, Flash Attention 2, Tokenizer refactor

New models

Mistral

Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices:

• Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
• GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
• Byte-fallback BPE tokenizer - ensures that characters are never mapped to out-of-vocabulary tokens.

• [Mistral] Mistral-7B-v0.1 support by @​Bam4d in #26447

Persimmon

The authors introduced Persimmon-8B, a decoder model based on the classic transformers architecture, with query and key normalization. Persimmon-8B is a fully permissively licensed model with approximately 8 billion parameters, released under the Apache license. Some of the key attributes of Persimmon-8B are long context size (16K), performance, and capabilities for multimodal extensions.

• [Persimmon] Add support for persimmon by @​ArthurZucker in #26042

BROS

BROS stands for BERT Relying On Spatiality. It is an encoder-only Transformer model that takes a sequence of tokens and their bounding boxes as inputs and outputs a sequence of hidden states. BROS encode relative spatial information instead of using absolute spatial information.

• Add BROS by @​jinhopark8345 in #23190

ViTMatte

ViTMatte leverages plain Vision Transformers for the task of image matting, which is the process of accurately estimating the foreground object in images and videos.

• Add ViTMatte by @​NielsRogge in #25843

Nougat

Nougat uses the same architecture as Donut, meaning an image Transformer encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.

• Add Nougat by @​NielsRogge and @​molbap in #25942

Prompt templating

We've added a new template feature for chat models. This allows the formatting that a chat model was trained with to be saved with the model, ensuring that users can exactly reproduce that formatting when they want to fine-tune the model or use it for inference. For more information, see our template documentation.

• Overhaul Conversation class and prompt templating by @​Rocketknight1 in #25323

🚨🚨 Tokenizer refactor

• [Tokenizer] attemp to fix add_token issues by @​ArthurZucker in #23909
• Nit-added-tokens by @​ArthurZucker in #26538 adds some fix to #23909 .

🚨Workflow Changes 🚨:

... (truncated)

Commits

• b71f20a Release: v4.34.0
• 2aef9a9 [PEFT] Final fixes (#26559)
• ae9a344 [Mistral] Add Flash Attention-2 support for mistral (#26464)
• 1a2e966 Nit-added-tokens (#26538)
• 245da7e [Doctest] Add configuration_encoder_decoder.py (#26519)
• 3632fb3 [AMD] Add initial version for run_tests_multi_gpu (#26346)
• 768aa3d [Wav2Vec2 and Co] Update init tests for PT 2.1 (#26494)
• b5ca8fc Add tokenizer kwargs to fill mask pipeline. (#26234)
• df6a855 [RFC, Logging] Change warning to info (#26545)
• cf345d5 Bump urllib3 from 1.26.9 to 1.26.17 in /examples/research_projects/decision_t...
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Superseded by #174.