chore: Bump transformers from 4.50.3 to 4.51.0

[dependabot]

Bumps transformers from 4.50.3 to 4.51.0.

Release notes

Sourced from transformers's releases.

v4.51.0: Llama 4, Phi4-Multimodal, DeepSeek-v3, Qwen3

New Model Additions

Llama 4

Llama 4, developed by Meta, introduces a new auto-regressive Mixture-of-Experts (MoE) architecture.This generation includes two models:

• The highly capable Llama 4 Maverick with 17B active parameters out of ~400B total, with 128 experts.
• The efficient Llama 4 Scout also has 17B active parameters out of ~109B total, using just 16 experts.

Both models leverage early fusion for native multimodality, enabling them to process text and image inputs. Maverick and Scout are both trained on up to 40 trillion tokens on data encompassing 200 languages (with specific fine-tuning support for 12 languages including Arabic, Spanish, German, and Hindi).

For deployment, Llama 4 Scout is designed for accessibility, fitting on a single server-grade GPU via on-the-fly 4-bit or 8-bit quantization, while Maverick is available in BF16 and FP8 formats. These models are released under the custom Llama 4 Community License Agreement, available on the model repositories

Getting started with Llama 4 using transformers is straightforward. Make sure you have transformers v4.51.0 or later installed:

pip install -U transformers[hf_xet]

Here's a quick example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed. You need to run this script on an instance with 8 GPUs, using a command like:

torchrun –nproc-per-instance=8 script.py

from transformers import AutoProcessor, Llama4ForConditionalGeneration
import torch
model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
model_id,
attn_implementation="flex_attention",
device_map="auto",
torch_dtype=torch.bfloat16,
)
url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": url1},
{"type": "image", "url": url2},
{"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"},
]
},
</tr></table>

... (truncated)

Commits

• 0720e20 Release: v4.51.0
• 25b7f27 Add llama4 (#37307)
• aa40fda Hf Xet extra (#37305)
• e945715 Fix deepspeed loading (part 2) (#37306)
• 84aa13d Fix deepspeed loading (#37281)
• 0ef339f Update OpenAI GPT model card (#37255)
• 46d7391 Updated T5 model card with standardized format (#37261)
• 579135a Updated model card for distilbert (#37157)
• 8cd57eb mobilebert model card update (#37256)
• ebe47ce Fix: Unexpected Keys, Improve run_compressed, Rename Test Folder (#37077)
• Additional commits viewable in compare view

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[Bobbyperson]

@dependabot rebase

[Bobbyperson]

This only works after running pip install --upgrade accelerate. @GeckoEidechse any advice?

[GeckoEidechse]

So acclerate is outdated but dependabot doesn't bump it? Weird that transformers doesn't require a newer version.

Maybe there's a way to bump it via poetry?

[dependabot]

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