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@dependabot dependabot bot commented on behalf of github Aug 11, 2025

Bumps transformers from 4.41.2 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)

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Bumps [transformers](https://github.com/huggingface/transformers) from 4.41.2 to 4.55.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.41.2...v4.55.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 4.55.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Aug 11, 2025
@dependabot dependabot bot requested a review from a team August 11, 2025 08:35
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dependabot bot commented on behalf of github Aug 25, 2025

Superseded by #2256.

@dependabot dependabot bot closed this Aug 25, 2025
@dependabot dependabot bot deleted the dependabot/pip/transformers-4.55.0 branch August 25, 2025 09:51
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