Try gpt-oss · Guides · Model card · OpenAI blog

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. We’re releasing two flavors of these open models: - \`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) - \`gpt-oss-20b\` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) 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. > [!NOTE] > This model card is dedicated to the smaller \`gpt-oss-20b\` model. Check out [\`gpt-oss-120b\`](https://huggingface.co/openai/gpt-oss-120b) for the larger model. # Highlights * **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment. * **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs. * **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. * **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning. * **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. * **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. --- # Inference examples ## Transformers 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. To get started, install the necessary dependencies to setup your environment: \`\`\` pip install -U transformers kernels torch \`\`\` Once, setup you can proceed to run the model by running the snippet below: \`\`\`py from transformers import pipeline import torch model_id = "openai/gpt-oss-20b" pipe = pipeline( "text-generation", model=model_id, torch_dtype="auto", device_map="auto", ) messages = [ \{"role": "user", "content": "Explain quantum mechanics clearly and concisely."\}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) \`\`\` Alternatively, you can run the model via [\`Transformers Serve\`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver: \`\`\` transformers serve transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b \`\`\` [Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers) ## vLLM 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. \`\`\`bash uv pip install --pre vllm==0.10.1+gptoss \ --extra-index-url https://wheels.vllm.ai/gpt-oss/ \ --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \ --index-strategy unsafe-best-match vllm serve openai/gpt-oss-20b \`\`\` [Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm) ## PyTorch / Triton 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). ## Ollama 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). \`\`\`bash # gpt-oss-20b ollama pull gpt-oss:20b ollama run gpt-oss:20b \`\`\` [Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama) #### LM Studio If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download. \`\`\`bash # gpt-oss-20b lms get openai/gpt-oss-20b \`\`\` 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. --- # Download the model You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI: \`\`\`shell # gpt-oss-20b huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/ pip install gpt-oss python -m gpt_oss.chat model/ \`\`\` # Reasoning levels You can adjust the reasoning level that suits your task across three levels: * **Low:** Fast responses for general dialogue. * **Medium:** Balanced speed and detail. * **High:** Deep and detailed analysis. The reasoning level can be set in the system prompts, e.g., "Reasoning: high". # Tool use The gpt-oss models are excellent for: * Web browsing (using built-in browsing tools) * Function calling with defined schemas * Agentic operations like browser tasks # Fine-tuning Both gpt-oss models can be fine-tuned for a variety of specialized use cases. This smaller model \`gpt-oss-20b\` can be fine-tuned on consumer hardware, whereas the larger [\`gpt-oss-120b\`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node. # Citation \`\`\`bibtex @misc\{openai2025gptoss120bgptoss20bmodel, title=\{gpt-oss-120b & gpt-oss-20b Model Card\}, author=\{OpenAI\}, year=\{2025\}, eprint=\{2508.10925\}, archivePrefix=\{arXiv\}, primaryClass=\{cs.CL\}, url=\{https://arxiv.org/abs/2508.10925\}, \} \`\`\`