README.md

# 🚀 OpenAI GPT OSS Models - Simple Generation Script

Generate synthetic datasets using OpenAI's GPT OSS models with transparent reasoning. Works on HuggingFace Jobs with L4 GPUs!

## ✅ Tested & Working

Successfully tested on HF Jobs with `l4x4` flavor (4x L4 GPUs = 96GB total memory).

## 🌟 Quick Start

```bash
# Run on HF Jobs (tested and working)
hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
    https://huggingface.co/datasets/davanstrien/openai-oss/raw/main/gpt_oss_minimal.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset username/gpt-oss-haiku \
    --prompt-column question \
    --max-samples 2 \
    --reasoning-effort high
```

## 📋 Script Options

| Option | Description | Default |
|--------|-------------|---------|
| `--input-dataset` | HuggingFace dataset to process | Required |
| `--output-dataset` | Output dataset name | Required |
| `--prompt-column` | Column containing prompts | `prompt` |
| `--model-id` | Model to use | `openai/gpt-oss-20b` |
| `--max-samples` | Limit samples to process | None (all) |
| `--max-new-tokens` | Max tokens to generate | `1024` |
| `--reasoning-effort` | Reasoning depth: low/medium/high | `medium` |

## 💡 What You Get

The output dataset contains:
- `prompt`: Original prompt from input dataset
- `raw_output`: Full model response with channel markers
- `model`: Model ID used
- `reasoning_effort`: The reasoning level used

### Understanding the Output

The raw output contains special channel markers:
- `<|channel|>analysis<|message|>` - Chain of thought reasoning
- `<|channel|>final<|message|>` - The actual response

Example raw output structure:
```
<|channel|>analysis<|message|>
[Reasoning about the task...]
<|channel|>final<|message|>
[Actual haiku or response]
```

## 🎯 Examples

### Test with Different Reasoning Levels

**High reasoning (most detailed):**
```bash
hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
    https://huggingface.co/datasets/davanstrien/openai-oss/raw/main/gpt_oss_minimal.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset username/haiku-high \
    --prompt-column question \
    --reasoning-effort high \
    --max-samples 5
```

**Low reasoning (fastest):**
```bash
hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
    https://huggingface.co/datasets/davanstrien/openai-oss/raw/main/gpt_oss_minimal.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset username/haiku-low \
    --prompt-column question \
    --reasoning-effort low \
    --max-samples 10
```

## 🖥️ GPU Requirements

| Model | Memory Required | Recommended Flavor |
|-------|----------------|-------------------|
| **openai/gpt-oss-20b** | ~40GB | `l4x4` (4x24GB = 96GB) |
| **openai/gpt-oss-120b** | ~240GB | `8xa100` (8x80GB) |

**Note**: The 20B model automatically dequantizes from MXFP4 to bf16 on non-Hopper GPUs, requiring more memory than the quantized size.

## 🔧 Technical Details

### Why L4x4?
- The 20B model needs ~40GB VRAM when dequantized
- Single A10G (24GB) is insufficient
- L4x4 provides 96GB total memory across 4 GPUs
- Cost-effective compared to A100 instances

### Reasoning Effort
The `reasoning_effort` parameter controls how much chain-of-thought reasoning the model generates:
- `low`: Quick responses with minimal reasoning
- `medium`: Balanced reasoning (default)
- `high`: Detailed step-by-step reasoning

## 📚 Resources

- [Model: openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
- [HF Jobs Documentation](https://huggingface.co/docs/hub/spaces-gpu-jobs)
- [Dataset: davanstrien/haiku_dpo](https://huggingface.co/datasets/davanstrien/haiku_dpo)

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*Last tested: 2025-01-06 on HF Jobs with l4x4 flavor*