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Correct small grammar issues for better comprehension (#21)

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* Correct small grammar issues for better comprehension

* Update README.md

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Co-authored-by: Christopher Whitelam <cwhitelam@northeastprecast.com>
Co-authored-by: Dominik Kundel <dkundel@openai.com>
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2025-08-05 17:28:41 -04:00
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@@ -18,16 +18,16 @@ 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 H100 GPU (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][harmony] and should only be used with the harmony format as it will not work correctly otherwise.
Both models were trained using our [harmony response format][harmony] and should only be used with this format; otherwise, they will not work correctly.
### 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.
- **Full chain-of-thought:** Provides complete access to the model's reasoning process, facilitating easier debugging and greater trust in outputs. This information is 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](#browser), [Python code execution](#python), and Structured Outputs.
- **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single H100 GPU and the `gpt-oss-20b` model run within 16GB of memory.
- **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, allowing `gpt-oss-120b` to run on a single H100 GPU and `gpt-oss-20b` to run within 16GB of memory..
### Inference examples
@@ -402,7 +402,7 @@ To improve performance the tool caches requests so that the model can revisit a
### Python
The model was trained to use using a python tool to perform calculations and other actions as part of its chain-of-thought. During the training the model used a stateful tool which makes running tools between CoT loops easier. This reference implementation, however, uses a stateless mode. As a result the PythonTool defines its own tool description to override the definition in [`openai-harmony`][harmony].
The model was trained to use a python tool to perform calculations and other actions as part of its chain-of-thought. During the training the model used a stateful tool which makes running tools between CoT loops easier. This reference implementation, however, uses a stateless mode. As a result the PythonTool defines its own tool description to override the definition in [`openai-harmony`][harmony].
> [!WARNING]
> This implementation runs in a permissive Docker container which could be problematic in cases like prompt injections. It's serving as an example and you should consider implementing your own container restrictions in production.