README.md

---
viewer: false
tags:
  - uv-script
  - synthetic-data
  - openai-oss
---

# 🚀 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).

## 🚀 Getting Started with HF Jobs

### First-time Setup (2 minutes)

1. **Install HuggingFace CLI**:
```bash
pip install huggingface-hub
```

2. **Login to HuggingFace**:
```bash
huggingface-cli login
```
(Enter your HF token when prompted - get one at https://huggingface.co/settings/tokens)

3. **Run the script on HF Jobs**:
```bash
hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
    https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_minimal.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset YOUR_USERNAME/gpt-oss-test \
    --prompt-column question \
    --max-samples 2
```

That's it! Your job will run on HuggingFace's GPUs and the output dataset will appear in your HF account.

## 🌟 Quick Start

```bash
# Run on HF Jobs (tested and working)
hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
    https://huggingface.co/datasets/uv-scripts/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           | Auto-scales: 512/1024/2048 |
| `--reasoning-effort` | Reasoning depth: low/medium/high | `medium`                   |
| `--temperature`      | Sampling temperature             | `1.0`                      |
| `--top-p`            | Top-p sampling                   | `1.0`                      |

**Note**: `max-new-tokens` auto-scales based on `reasoning-effort` if not set:

- `low`: 512 tokens
- `medium`: 1024 tokens
- `high`: 2048 tokens (prevents truncation of detailed reasoning)

## 💡 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/uv-scripts/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/uv-scripts/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) |

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

### 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

### Sampling Parameters

OpenAI recommends `temperature=1.0` and `top_p=1.0` as defaults for GPT OSS models:

- These settings provide good diversity without compromising quality
- The model was trained to work well with these parameters
- Adjust only if you need specific behavior (e.g., lower temperature for more deterministic output)

## 📚 Resources

- [OpenAI GPT OSS Model Collection](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) - Both 20B and 120B models
- [Model: openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
- [HF Jobs Documentation](https://huggingface.co/docs/huggingface_hub/guides/jobs) - Complete guide to running jobs on HuggingFace
- [HF CLI Guide](https://huggingface.co/docs/huggingface_hub/guides/cli) - HuggingFace CLI installation and usage
- [Dataset: davanstrien/haiku_dpo](https://huggingface.co/datasets/davanstrien/haiku_dpo)

---

_Last tested: 2025-01-06 on HF Jobs with l4x4 flavor_