Files changed (2) hide show
  1. README.md +2 -16
  2. generation_config.json +1 -2
README.md CHANGED
@@ -13,7 +13,7 @@ tags:
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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://arxiv.org/abs/2508.10925"><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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  * **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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  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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-
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- # Citation
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-
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- ```bibtex
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- @misc{openai2025gptoss120bgptoss20bmodel,
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- title={gpt-oss-120b & gpt-oss-20b Model Card},
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- author={OpenAI},
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- year={2025},
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- eprint={2508.10925},
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- archivePrefix={arXiv},
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- primaryClass={cs.CL},
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- url={https://arxiv.org/abs/2508.10925},
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- }
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- ```
 
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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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  * **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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+ * **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, 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.
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  ---
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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.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
generation_config.json CHANGED
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  "do_sample": true,
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  "eos_token_id": [
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  200002,
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- 199999,
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- 200012
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  ],
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  "pad_token_id": 199999,
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  "transformers_version": "4.55.0.dev0"
 
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  "do_sample": true,
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  "eos_token_id": [
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  200002,
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+ 199999
 
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  ],
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  "pad_token_id": 199999,
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  "transformers_version": "4.55.0.dev0"