| # 🚀 OpenAI GPT OSS Models - Open Source Language Models with Reasoning | |
| Generate responses with transparent chain-of-thought reasoning using OpenAI's new open source GPT models. Run on cloud GPUs with zero setup! | |
| ## 🏁 Quick Setup for HF Jobs (One-time) | |
| ```bash | |
| # Install huggingface-hub CLI using uv | |
| uv tool install huggingface-hub | |
| # Login to Hugging Face | |
| huggingface-cli login | |
| # Now you're ready to run jobs! | |
| ``` | |
| Need more help? Check the [HF Jobs documentation](https://huggingface.co/docs/huggingface_hub/guides/job). | |
| ## 🌟 Try It Now! Copy & Run This Command: | |
| ```bash | |
| # Generate 50 haiku with reasoning (~5 minutes on A10G) | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset davanstrien/haiku_dpo \ | |
| --output-dataset haiku-reasoning \ | |
| --prompt-column question \ | |
| --max-samples 50 | |
| ``` | |
| That's it! Your dataset will be generated and pushed to `your-username/haiku-reasoning`. 🎉 | |
| ## 💡 What You Get | |
| The models output structured reasoning in separate channels: | |
| ```json | |
| { | |
| "prompt": "Write a haiku about mountain serenity", | |
| "think": "I need to create a haiku with 5-7-5 syllable structure. Mountains suggest stillness, permanence. For serenity, I'll use calm imagery like 'silent peaks' (3 syllables)...", | |
| "content": "Silent peaks stand tall\nClouds drift through morning stillness\nPeace in stone and sky", | |
| "reasoning_level": "high", | |
| "model": "openai/gpt-oss-20b" | |
| } | |
| ``` | |
| ## 🎯 More Examples | |
| ### Use Your Own Dataset | |
| ```bash | |
| # Process your entire dataset | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset your-prompts \ | |
| --output-dataset my-responses | |
| # Use the larger 120B model | |
| huggingface-cli job run --gpu-flavor a100-large \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset your-prompts \ | |
| --output-dataset my-responses-120b \ | |
| --model-id openai/gpt-oss-120b | |
| ``` | |
| ### Process Different Dataset Types | |
| ```bash | |
| # Math problems with step-by-step reasoning | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset math-problems \ | |
| --output-dataset math-solutions \ | |
| --reasoning-level high | |
| # Code generation with explanation | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset code-prompts \ | |
| --output-dataset code-explained \ | |
| --max-tokens 1024 | |
| # Test with just 10 samples | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_vllm.py \ | |
| --input-dataset your-dataset \ | |
| --output-dataset quick-test \ | |
| --max-samples 10 | |
| ``` | |
| ## 📦 Two Script Options | |
| 1. **`gpt_oss_vllm.py`** - High-performance batch generation using vLLM (recommended) | |
| 2. **`gpt_oss_transformers.py`** - Standard transformers implementation (fallback) | |
| ### Transformers Fallback (if vLLM has issues) | |
| ```bash | |
| # Same command, different script! | |
| huggingface-cli job run --gpu-flavor a10g-small \ | |
| uv run https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_transformers.py \ | |
| --input-dataset davanstrien/haiku_dpo \ | |
| --output-dataset haiku-reasoning \ | |
| --prompt-column question \ | |
| --max-samples 50 | |
| ``` | |
| ## 💰 GPU Flavors and Costs | |
| | Model | GPU Flavor | Memory | Cost/Hour | Best For | | |
| |-------|------------|--------|-----------|----------| | |
| | `gpt-oss-20b` | `a10g-large` | 48GB | $2.50 | 20B model (needs ~40GB) | | |
| | `gpt-oss-20b` | `a100-large` | 80GB | $4.34 | 20B with headroom | | |
| | `gpt-oss-120b` | `4xa100` | 320GB | $17.36 | 120B model (needs ~240GB) | | |
| | `gpt-oss-120b` | `8xl40s` | 384GB | $23.50 | 120B maximum speed | | |
| **Note**: The MXFP4 quantization is dequantized to bf16 during loading, which doubles memory requirements. | |
| ## 🏃 Local Execution | |
| If you have a local GPU: | |
| ```bash | |
| # Using vLLM (recommended) | |
| uv run gpt_oss_vllm.py \ | |
| --input-dataset davanstrien/haiku_dpo \ | |
| --output-dataset haiku-reasoning \ | |
| --prompt-column question \ | |
| --max-samples 50 | |
| # Using Transformers | |
| uv run gpt_oss_transformers.py \ | |
| --input-dataset davanstrien/haiku_dpo \ | |
| --output-dataset haiku-reasoning \ | |
| --prompt-column question \ | |
| --max-samples 50 | |
| ``` | |
| ## 🛠️ Parameters | |
| | Parameter | Description | Default | | |
| |-----------|-------------|---------| | |
| | `--input-dataset` | Source dataset on HF Hub | Required | | |
| | `--output-dataset` | Output dataset name (auto-prefixed with your username) | Required | | |
| | `--prompt-column` | Column containing prompts | `prompt` | | |
| | `--model-id` | Model to use | `openai/gpt-oss-20b` | | |
| | `--reasoning-level` | Reasoning depth (high/medium/low) | `high` | | |
| | `--max-samples` | Limit number of examples | None (all) | | |
| | `--temperature` | Generation temperature | `0.7` | | |
| | `--max-tokens` | Max tokens to generate | `512` | | |
| ## 🎯 Key Features | |
| - **Open Source Models**: `openai/gpt-oss-20b` and `openai/gpt-oss-120b` | |
| - **Structured Output**: Separate channels for reasoning (`analysis`) and response (`final`) | |
| - **Zero Setup**: Run with a single command on HF Jobs | |
| - **Flexible Input**: Works with any prompt dataset | |
| - **Automatic Upload**: Results pushed directly to your Hub account | |
| ## 🎯 Use Cases | |
| 1. **Training Data**: Create datasets with built-in reasoning explanations | |
| 2. **Evaluation**: Generate test sets where each answer includes its rationale | |
| 3. **Research**: Study how large models approach different types of problems | |
| 4. **Applications**: Build systems that can explain their outputs | |
| ## 🤔 Which Script to Use? | |
| - **`gpt_oss_vllm.py`**: First choice for performance and scale | |
| - **`gpt_oss_transformers.py`**: Fallback if vLLM has compatibility issues | |
| ## 🔧 Requirements | |
| For HF Jobs: | |
| - Hugging Face account (free) | |
| - `huggingface-hub` CLI tool | |
| For local execution: | |
| - Python 3.10+ | |
| - GPU with CUDA support | |
| - Hugging Face token | |
| ## 🤝 Contributing | |
| This is part of the [uv-scripts](https://huggingface.co/uv-scripts) collection. Contributions and improvements welcome! | |
| ## 📜 License | |
| Apache 2.0 - Same as the OpenAI GPT OSS models | |
| --- | |
| **Ready to generate data with reasoning?** Copy the command at the top and run it! 🚀 |