*This model was contributed to Hugging Face Transformers on 2025-08-05.*
# GptOss
[GptOss](https://openai.com/index/introducing-gpt-oss/) 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.
```python
from transformers import pipeline
pipe = pipeline(
task="text-generation",
model="openai/gpt-oss-20b",
)
pipe("Plants create energy through a process known as")
```
```python
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
