# 🚀 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! 🚀