README.md
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1 ---
2 license: apache-2.0
3 pipeline_tag: text-generation
4 library_name: transformers
5 tags:
6 - vllm
7 ---
8
9 <p align="center">
10 <img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg">
11 </p>
12
13 <p align="center">
14 <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
15 <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
16 <a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
17 <a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
18 </p>
19
20 <br>
21
22 Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
23
24 We’re releasing two flavors of these open models:
25 - `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
26 - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
27
28 Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
29
30
31 > [!NOTE]
32 > This model card is dedicated to the larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller model.
33
34 # Highlights
35
36 * **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
37 * **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
38 * **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
39 * **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
40 * **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
41 * **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
42
43 ---
44
45 # Inference examples
46
47 ## Transformers
48
49 You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
50
51 To get started, install the necessary dependencies to setup your environment:
52
53 ```
54 pip install -U transformers kernels torch
55 ```
56
57 Once, setup you can proceed to run the model by running the snippet below:
58
59 ```py
60 from transformers import pipeline
61 import torch
62
63 model_id = "openai/gpt-oss-120b"
64
65 pipe = pipeline(
66 "text-generation",
67 model=model_id,
68 torch_dtype="auto",
69 device_map="auto",
70 )
71
72 messages = [
73 {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
74 ]
75
76 outputs = pipe(
77 messages,
78 max_new_tokens=256,
79 )
80 print(outputs[0]["generated_text"][-1])
81 ```
82
83 Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
84
85 ```
86 transformers serve
87 transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-120b
88 ```
89
90 [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
91
92 ## vLLM
93
94 vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
95
96 ```bash
97 uv pip install --pre vllm==0.10.1+gptoss \
98 --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
99 --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
100 --index-strategy unsafe-best-match
101
102 vllm serve openai/gpt-oss-120b
103 ```
104
105 [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
106
107 ## PyTorch / Triton
108
109 To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
110
111 ## Ollama
112
113 If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
114
115 ```bash
116 # gpt-oss-120b
117 ollama pull gpt-oss:120b
118 ollama run gpt-oss:120b
119 ```
120
121 [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
122
123 #### LM Studio
124
125 If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
126
127 ```bash
128 # gpt-oss-120b
129 lms get openai/gpt-oss-120b
130 ```
131
132 Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
133
134 ---
135
136 # Download the model
137
138 You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
139
140 ```shell
141 # gpt-oss-120b
142 huggingface-cli download openai/gpt-oss-120b --include "original/*" --local-dir gpt-oss-120b/
143 pip install gpt-oss
144 python -m gpt_oss.chat model/
145 ```
146
147 # Reasoning levels
148
149 You can adjust the reasoning level that suits your task across three levels:
150
151 * **Low:** Fast responses for general dialogue.
152 * **Medium:** Balanced speed and detail.
153 * **High:** Deep and detailed analysis.
154
155 The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
156
157 # Tool use
158
159 The gpt-oss models are excellent for:
160 * Web browsing (using built-in browsing tools)
161 * Function calling with defined schemas
162 * Agentic operations like browser tasks
163
164 # Fine-tuning
165
166 Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
167
168 This larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
169
170 # Citation
171
172 ```bibtex
173 @misc{openai2025gptoss120bgptoss20bmodel,
174 title={gpt-oss-120b & gpt-oss-20b Model Card},
175 author={OpenAI},
176 year={2025},
177 eprint={2508.10925},
178 archivePrefix={arXiv},
179 primaryClass={cs.CL},
180 url={https://arxiv.org/abs/2508.10925},
181 }
182 ```