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README.md

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-my model
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- vllm
+---
+
+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}, 
+}
+```