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Aug 10, 2025
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Fix typos and improve grammar in README #61

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Fix typos and improve grammar in README
hasanerdemak Aug 6, 2025
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Merge branch 'main' into main
hasanerdemak Aug 8, 2025
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Merge branch 'main' into main
romainhuet Aug 10, 2025
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16 changes: 8 additions & 8 deletions README.md
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Expand Up @@ -63,7 +63,7 @@ print(outputs[0]["generated_text"][-1])

#### 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.
vLLM recommends using [`uv`](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible web server. The following command will automatically download the model and start the server.

```bash
uv pip install --pre vllm==0.10.1+gptoss \
Expand Down Expand Up @@ -130,7 +130,7 @@ This repository provides a collection of reference implementations:

### Requirements

- python 3.12
- Python 3.12
- On macOS: Install the Xcode CLI tools --> `xcode-select --install`
- On Linux: These reference implementations require CUDA
- On Windows: These reference implementations have not been tested on Windows. Try using solutions like Ollama if you are trying to run the model locally.
Expand Down Expand Up @@ -171,7 +171,7 @@ hf download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/

We include an inefficient reference PyTorch implementation in [gpt_oss/torch/model.py](gpt_oss/torch/model.py). This code uses basic PyTorch operators to show the exact model architecture, with a small addition of supporting tensor parallelism in MoE so that the larger model can run with this code (e.g., on 4xH100 or 2xH200). In this implementation, we upcast all weights to BF16 and run the model in BF16.

To run the reference implementation, install these dependencies:
To run the reference implementation, install the dependencies:

```shell
pip install -e ".[torch]"
Expand Down Expand Up @@ -227,7 +227,7 @@ To perform inference you'll need to first convert the SafeTensor weights from Hu
python gpt_oss/metal/scripts/create-local-model.py -s <model_dir> -d <output_file>
```

Or downloaded the pre-converted weight:
Or download the pre-converted weight:

```shell
hf download openai/gpt-oss-120b --include "metal/*" --local-dir gpt-oss-120b/metal/
Expand All @@ -250,7 +250,7 @@ We also include two system tools for the model: browsing and python container. C

### Terminal Chat

The terminal chat application is a basic example on how to use the harmony format together with the PyTorch, Triton, and vLLM implementations. It also exposes both the python and browser tool as optional tools that can be used.
The terminal chat application is a basic example of how to use the harmony format together with the PyTorch, Triton, and vLLM implementations. It also exposes both the python and browser tool as optional tools that can be used.

```bash
usage: python -m gpt_oss.chat [-h] [-r REASONING_EFFORT] [-a] [-b] [--show-browser-results] [-p] [--developer-message DEVELOPER_MESSAGE] [-c CONTEXT] [--raw] [--backend {triton,torch,vllm}] FILE
Expand Down Expand Up @@ -289,7 +289,7 @@ You can start this server with the following inference backends:

- `triton` — uses the triton implementation
- `metal` — uses the metal implementation on Apple Silicon only
- `ollama` — uses the Ollama /api/generate API as a inference solution
- `ollama` — uses the Ollama /api/generate API as an inference solution
- `vllm` — uses your installed vllm version to perform inference
- `transformers` — uses your installed transformers version to perform local inference

Expand Down Expand Up @@ -468,10 +468,10 @@ if last_message.recipient == "python":

We released the models with native quantization support. Specifically, we use [MXFP4](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) for the linear projection weights in the MoE layer. We store the MoE tensor in two parts:

- `tensor.blocks` stores the actual fp4 values. We pack every two value in one `uint8` value.
- `tensor.blocks` stores the actual fp4 values. We pack every two values in one `uint8` value.
- `tensor.scales` stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.

All other tensors will be in BF16. We also recommend use BF16 as the activation precision for the model.
All other tensors will be in BF16. We also recommend using BF16 as the activation precision for the model.

### Recommended Sampling Parameters

Expand Down