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PyroNet

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----
-license: apache-2.0
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- vllm
----
-<p align="center">
-  <img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
-</p>
-<p align="center">
-  <a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
-  <a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
-  <a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> ·
-  <a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
-</p>
-<br>
-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.