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