Bump transformers from 4.53.2 to 4.55.0

[dependabot]

Bumps transformers from 4.53.2 to 4.55.0.

Release notes

Sourced from transformers's releases.

v4.55.0: New openai GPT OSS model!

Welcome GPT OSS, the new open-source model family from OpenAI!

For more detailed information about this model, we recommend reading the following blogpost: https://huggingface.co/blog/welcome-openai-gpt-oss

GPT OSS is a hugely anticipated open-weights release by OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases. It comprises two models: a big one with 117B parameters (gpt-oss-120b), and a smaller one with 21B parameters (gpt-oss-20b). Both are mixture-of-experts (MoEs) and use a 4-bit quantization scheme (MXFP4), enabling fast inference (thanks to fewer active parameters, see details below) while keeping resource usage low. The large model fits on a single H100 GPU, while the small one runs within 16GB of memory and is perfect for consumer hardware and on-device applications.

Overview of Capabilities and Architecture

• 21B and 117B total parameters, with 3.6B and 5.1B active parameters, respectively.
• 4-bit quantization scheme using mxfp4 format. Only applied on the MoE weights. As stated, the 120B fits in a single 80 GB GPU and the 20B fits in a single 16GB GPU.
• Reasoning, text-only models; with chain-of-thought and adjustable reasoning effort levels.
• Instruction following and tool use support.
• Inference implementations using transformers, vLLM, llama.cpp, and ollama.
• Responses API is recommended for inference.
• License: Apache 2.0, with a small complementary use policy.

Architecture

• Token-choice MoE with SwiGLU activations.
• When calculating the MoE weights, a softmax is taken over selected experts (softmax-after-topk).
• Each attention layer uses RoPE with 128K context.
• Alternate attention layers: full-context, and sliding 128-token window.
• Attention layers use a learned attention sink per-head, where the denominator of the softmax has an additional additive value.
• It uses the same tokenizer as GPT-4o and other OpenAI API models.
• Some new tokens have been incorporated to enable compatibility with the Responses API.

The following snippet shows simple inference with the 20B model. It runs on 16 GB GPUs when using mxfp4, or ~48 GB in bfloat16.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "openai/gpt-oss-20b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto",
)
messages = [
{"role": "user", "content": "How many rs are in the word 'strawberry'?"},
]
inputs = tokenizer.apply_chat_template(
messages,
</tr></table>

... (truncated)

Commits

• 06f8004 Release: v4.55.0
• c54203a gpt_oss last chat template changes (#39925)
• 7c38d8f Add GPT OSS model from OpenAI (#39923)
• 738c1a3 🌐 [i18n-KO] Translated cache_explanation.md to Korean (#39535)
• d2ae766 Export SmolvLM (#39614)
• c430047 [docs] update object detection guide (#39909)
• dedcbd6 run model debugging with forward arg (#39905)
• 20ce210 Revert "remove dtensors, not explicit (#39840)" (#39912)
• 2589a52 Fix aria tests (#39879)
• 6e4a9a5 Fix eval thread fork bomb (#39717)
• Additional commits viewable in compare view

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[coveralls]

coverage: 52.919% (+0.04%) from 52.88%
when pulling 41d6c27 on dependabot/pip/transformers-4.55.0
into 3158296 on dev.

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