README.md

🚀 OpenAI GPT OSS Models - Works on Regular GPUs!

Generate synthetic datasets with transparent reasoning using OpenAI's GPT OSS models. No H100s required - works on L4, A100, A10G, and even T4 GPUs!

🎉 Key Discovery

The models work on regular datacenter GPUs! Transformers automatically handles MXFP4 → bf16 conversion, making these models accessible on standard hardware.

🌟 Quick Start

Test Locally (Single Prompt)

uv run gpt_oss_transformers.py --prompt "Write a haiku about mountains"

Run on HuggingFace Jobs (No GPU Required!)

# Generate haiku with reasoning (~$1.50/hr on A10G)
hf jobs uv run --flavor a10g-small \
    https://huggingface.co/datasets/uv-scripts/openai-oss/raw/main/gpt_oss_transformers.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset username/haiku-reasoning \
    --prompt-column question \
    --max-samples 50

💡 What You Get

The models output structured reasoning in separate channels:

Raw Output:

analysisI need to write a haiku about mountains. Haiku: 5-7-5 syllable structure...
assistantfinalSilent peaks climb high,
Echoing winds trace stone's breath,
Dawn paints them gold bright.

Parsed Dataset:

{
  "prompt": "Write a haiku about mountains",
  "think": "[Analysis] I need to write a haiku about mountains. Haiku: 5-7-5 syllable structure...",
  "content": "Silent peaks climb high,\nEchoing winds trace stone's breath,\nDawn paints them gold bright.",
  "reasoning_level": "high",
  "model": "openai/gpt-oss-20b"
}

🖥️ GPU Requirements

✅ Confirmed Working GPUs

GPU	Memory	Status	Notes
L4	24GB	✅ Tested	Works perfectly!
A100	40/80GB	✅ Works	Great performance
A10G	24GB	✅ Recommended	Best value at $1.50/hr
T4	16GB	⚠️ Limited	May need 8-bit for 20B
RTX 4090	24GB	✅ Works	Consumer GPU support

Memory Requirements

• 20B model: ~40GB VRAM when dequantized (use A100-40GB or 2xL4)
• 120B model: ~240GB VRAM when dequantized (use 4xA100-80GB)

🎯 Examples

Creative Writing with Reasoning

# Process haiku dataset with high reasoning
uv run gpt_oss_transformers.py \
    --input-dataset davanstrien/haiku_dpo \
    --output-dataset my-haiku-reasoning \
    --prompt-column question \
    --reasoning-level high \
    --max-samples 100

Math Problems with Step-by-Step Solutions

# Generate math solutions with reasoning traces
uv run gpt_oss_transformers.py \
    --input-dataset gsm8k \
    --output-dataset math-with-reasoning \
    --prompt-column question \
    --reasoning-level high

Test Different Reasoning Levels

# Compare reasoning levels
for level in low medium high; do
    echo "Testing: $level"
    uv run gpt_oss_transformers.py \
        --prompt "Explain gravity to a 5-year-old" \
        --reasoning-level $level \
        --debug
done

📋 Script Options

Option	Description	Default
--input-dataset	HuggingFace dataset to process	-
--output-dataset	Output dataset name	-
--prompt-column	Column with prompts	prompt
--model-id	Model to use	openai/gpt-oss-20b
--reasoning-level	Reasoning depth: low/medium/high	high
--max-samples	Limit samples to process	None
--temperature	Sampling temperature	0.7
--max-tokens	Max tokens to generate	512
--prompt	Single prompt test (skip dataset)	-
--debug	Show raw model output	False

🔧 Technical Details

Why It Works Without H100s

1. Automatic MXFP4 Handling: When your GPU doesn't support MXFP4, you'll see:

   MXFP4 quantization requires triton >= 3.4.0 and triton_kernels installed, 
   we will default to dequantizing the model to bf16
   

2. No Flash Attention 3 Required: FA3 needs Hopper architecture, but models work fine without it

3. Simple Loading: Just use standard transformers:

   model = AutoModelForCausalLM.from_pretrained(
       "openai/gpt-oss-20b",
       torch_dtype=torch.bfloat16,
       device_map="auto"
   )
   

Channel Output Format

The models use a simplified channel format:

• analysis: Chain of thought reasoning
• commentary: Meta operations (optional)
• final: User-facing response

Reasoning Control

Control reasoning depth via system message:

messages = [
    {
        "role": "system", 
        "content": f"...Reasoning: {level}..."
    },
    {"role": "user", "content": prompt}
]

🚨 Best Practices

1. Token Limits: Use 1000+ tokens for detailed reasoning
2. Security: Never expose reasoning channels to end users
3. Batch Size: Keep at 1 for memory efficiency
4. Reasoning Levels:
   • low: Quick responses
   • medium: Balanced reasoning
   • high: Detailed chain-of-thought

🐛 Troubleshooting

Out of Memory

• Use larger GPU flavor: --flavor a100-large
• Reduce batch size to 1
• Try 8-bit quantization for smaller GPUs

No GPU Available

• Use HuggingFace Jobs (no local GPU needed!)
• Or use cloud instances with GPU support

Empty Reasoning

• Increase --max-tokens to 1500+
• Ensure prompts trigger reasoning

📚 References

• OpenAI Cookbook: GPT OSS
• Model: openai/gpt-oss-20b
• HF Jobs Documentation

🎉 The Bottom Line

You don't need H100s! These models work great on regular datacenter GPUs. Just run the script and start generating datasets with transparent reasoning.

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Last tested: 2025-08-05 on NVIDIA L4 GPUs - Working perfectly!