Bump transformers from 4.40.1 to 4.41.2

[dependabot]

Bumps transformers from 4.40.1 to 4.41.2.

Release notes

Sourced from transformers's releases.

Release v4.41.2

Mostly fixing some stuff related to trust_remote_code=True and from_pretrained

The local_file_only was having a hard time when a .safetensors file did not exist. This is not expected and instead of trying to convert, we should just fallback to loading the .bin files.

• Do not trigger autoconversion if local_files_only #31004 from @​Wauplin fixes this!
• Paligemma: Fix devices and dtype assignments (#31008) by @​molbap
• Redirect transformers_agents doc to agents (#31054) @​aymeric-roucher
• Fix from_pretrained in offline mode when model is preloaded in cache (#31010) by @​oOraph
• Fix faulty rstrip in module loading (#31108) @​Rocketknight1

Release v4.41.1 Fix PaliGemma finetuning, and some small bugs

Release v4.41.1

Fix PaliGemma finetuning:

The causal mask and label creation was causing label leaks when training. Kudos to @​probicheaux for finding and reporting!

• huggingface/transformers@a755745 : PaliGemma - fix processor with no input text (huggingface/transformers#30916) @​hiyouga
• huggingface/transformers@a25f7d3 : Paligemma causal attention mask (huggingface/transformers#30967) @​molbap and @​probicheaux

Other fixes:

• huggingface/transformers@bb48e92: tokenizer_class = "AutoTokenizer" Llava Family (huggingface/transformers#30912)
• huggingface/transformers@1d568df : legacy to init the slow tokenizer when converting from slow was wrong (huggingface/transformers#30972)
• huggingface/transformers@b1065aa : Generation: get special tokens from model config (huggingface/transformers#30899) @​zucchini-nlp

Reverted huggingface/transformers@4ab7a28

v4.41.0: Phi3, JetMoE, PaliGemma, VideoLlava, Falcon2, FalconVLM & GGUF support

New models

Phi3

The Phi-3 model was proposed in Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone by Microsoft.

TLDR; Phi-3 introduces new ROPE scaling methods, which seems to scale fairly well! A 3b and a Phi-3-mini is available in two context-length variants—4K and 128K tokens. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality.

• Phi-3 by @​gugarosa in huggingface/transformers#30423

JetMoE

JetMoe-8B is an 8B Mixture-of-Experts (MoE) language model developed by Yikang Shen and MyShell. JetMoe project aims to provide a LLaMA2-level performance and efficient language model with a limited budget. To achieve this goal, JetMoe uses a sparsely activated architecture inspired by the ModuleFormer. Each JetMoe block consists of two MoE layers: Mixture of Attention Heads and Mixture of MLP Experts. Given the input tokens, it activates a subset of its experts to process them. This sparse activation schema enables JetMoe to achieve much better training throughput than similar size dense models. The training throughput of JetMoe-8B is around 100B tokens per day on a cluster of 96 H100 GPUs with a straightforward 3-way pipeline parallelism strategy.

... (truncated)

Commits

• ab0f050 Release: v4.41.2
• 57f5553 Fix faulty rstrip in module loading (#31108)
• 73b180c fix from_pretrained in offline mode when model is preloaded in cache (#31010)
• a6325a7 Redirect transformers_agents doc to agents (#31054)
• 9ccdc84 Paligemma- fix devices and dtype assignments (#31008)
• 12aa316 Do not trigger autoconversion if local_files_only (#31004)
• 75f15f3 Release: v4.41.1
• 8282db5 Paligemma causal attention mask (#30967)
• e5b788a Revert "feat: Upgrade Weights & Biases callback (#30135)"
• 9d05459 Generation: get special tokens from model config (#30899)
• Additional commits viewable in compare view

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[dependabot]

Looks like transformers is up-to-date now, so this is no longer needed.