transformers/docs/source/en/model_doc/gpt_oss.md

This model was contributed to Hugging Face Transformers on 2025-08-05.

GptOss

GptOss is a sparse mixture-of-experts (MoE) language model from OpenAI that routes each token to 4 of 128 experts. It uses attention sinks — learnable auxiliary tokens appended to each attention head — and YaRN rotary embeddings for sequences up to 131k tokens.

The example below demonstrates how to generate text with [Pipeline] or the [AutoModelForCausalLM] class.

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="openai/gpt-oss-20b",
)
pipe("Plants create energy through a process known as")

from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
model = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-20b",
    device_map="auto",
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

• SDPA is not supported because attention sinks require direct access to the full attention logits before softmax. Use Flash Attention or Flex Attention instead.
• When using Flex Attention, attention sinks require special handling. The score_mod function operates on individual score elements rather than the full attention matrix, so sink renormalization is applied after computation using the log-sum-exp (LSE) values returned by Flex Attention.

GptOssConfig

autodoc GptOssConfig

GptOssModel

autodoc GptOssModel - forward

GptOssForCausalLM

autodoc GptOssForCausalLM - forward

GptOssForSequenceClassification

autodoc GptOssForSequenceClassification - forward

GptOssForTokenClassification

autodoc GptOssForTokenClassification - forward