---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
library_name: peft
tags:
- lora
- bitcoin
- quantitative-analysis
- trading
- financial-analysis
- text-generation
license: mit
language:
- en
pipeline_tag: text-generation
---

# Milo Bitcoin GPT-OSS-20B LoRA v1

**A professional Bitcoin quantitative analysis model fine-tuned on GPT-OSS-20B**

This is a LoRA (Low-Rank Adaptation) adapter for [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit), specifically fine-tuned for professional Bitcoin market analysis and trading signal generation.

## Model Description

Milo Bitcoin is an AI-powered quantitative analyst that provides:
- **Professional Trading Analysis**: Multi-factor technical analysis with precise signals
- **Structured Decision Output**: JSON-formatted BUY/SELL/HOLD recommendations
- **Quantitative Intelligence**: Technical indicators, trend analysis, momentum signals
- **Risk Assessment**: Stop-loss, take-profit, and confidence scores

### Key Features
- 🎯 Specialized in Bitcoin market analysis
- 📊 Structured JSON output format
- 🔢 Multi-task: price forecasting + classification + risk assessment
- 📈 Professional trading-ready signals
- ⚡ Consistent methodology trained on 7,341 samples

## Training Details

### Training Data
- **Training Samples**: 6,239 professional Bitcoin analysis examples
- **Validation Samples**: 734 samples
- **Test Samples**: 368 samples
- **Total Samples**: 7,341 samples
- **Data Quality**: 99%+ validated professional samples
- **Data Mix**: 90% Bitcoin analysis + 7% math reasoning + 3% logic reasoning

### Training Configuration
- **Base Model**: GPT-OSS-20B (21B parameters, 3.6B active)
- **Method**: LoRA (Low-Rank Adaptation)
- **LoRA Rank**: 64
- **LoRA Alpha**: 128
- **Target Modules**: q_proj, k_proj, v_proj, o_proj
- **Training Epochs**: 3
- **Batch Size**: 4 (effective batch size: 32 with gradient accumulation)
- **Learning Rate**: 2e-4
- **Training Time**: 1.65 hours on RTX 5090 (32GB VRAM)

### Training Results
- **Final Training Loss**: 1.2539
- **Final Validation Loss**: 1.2931
- **Training Speed**: 3.145 samples/second
- **Model Size**: 122MB LoRA weights (vs base model ~20GB, 99.4% compression)
- **Convergence**: Stable loss reduction with no overfitting

## Usage

### Installation

```bash
pip install transformers peft torch unsloth
```

### Load Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/gpt-oss-20b-unsloth-bnb-4bit",
    device_map="auto",
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "HugMilo/milo-bitcoin-gpt-oss-20b-lora-v1"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    "HugMilo/milo-bitcoin-gpt-oss-20b-lora-v1"
)
```

### Generate Analysis

```python
# Prepare prompt
prompt = """Analyze the current Bitcoin market conditions with the following data:
- Current Price: $109,453
- 24h Change: -5.35%
- RSI(14): 31.2
- Volume: $22.63B

Provide professional trading analysis with structured output."""

# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Expected Output Format

```json
{
  "action": "HOLD",
  "confidence": 72,
  "current_price": 109453.00,
  "stop_loss": 105200.00,
  "take_profit": 116800.00,
  "forecast_10d": [109800, 111200, 112500, 114100, 115600, 116200, 115800, 116800, 118200, 117900],
  "analysis": "BTC consolidating around $109k level after -5.35% weekly decline. RSI oversold at 31, testing key support. Market cap dominance 56.5% suggests institutional confidence remains.",
  "risk_score": 0.31,
  "technical_indicators": {
    "rsi_14": 31.2,
    "sma_20": 112500,
    "volume_24h": "22.63B USD"
  }
}
```

## Intended Use

### Primary Users
- Quantitative traders seeking AI-powered analysis signals
- Crypto fund managers requiring structured analysis frameworks
- Professional investors for data-driven portfolio management
- FinTech developers building Bitcoin analysis APIs

### Use Cases
- Systematic trading signal generation
- Risk management and position sizing
- Research and backtesting
- API integration for trading systems

## Limitations and Disclaimers

⚠️ **For Professional Traders Only**

- Model predictions are based on historical data patterns (training data from Bitcoin market history)
- Past performance does not guarantee future results
- This is a tool for professional analysis, not financial advice
- Users are responsible for their own trading decisions and risk management
- Always combine with your own analysis and risk management framework
- Regulatory compliance is the user's responsibility

## Performance Metrics

- **Training Efficiency**: 7x faster than expected (1.65h vs 12-15h projected)
- **JSON Format Consistency**: 100% structured output during training
- **Inference Speed**: <2 seconds per analysis on RTX 5090
- **Memory Requirements**: ~8-12GB VRAM for inference (4-bit quantization)

## Technical Specifications

- **Framework**: Transformers 4.56.2, PEFT 0.17.1, TRL 0.23.0
- **PyTorch**: 2.8.0+cu128
- **Hardware Used**: NVIDIA RTX 5090 (32GB VRAM)
- **Quantization**: 4-bit via bitsandbytes
- **Gradient Checkpointing**: Unsloth optimized

## Model Card Authors

**Norton Gu** | University of Rochester '25
- GitHub: [@futurespyhi](https://github.com/futurespyhi)
- LinkedIn: [Norton Gu](https://www.linkedin.com/in/norton-gu-322737278/)
- Project: [Milo_Bitcoin](https://github.com/futurespyhi/MiloBitcoin)

## Citation

If you use this model in your research or applications, please cite:

```bibtex
@misc{gu2025milobitcoin,
  author = {Norton Gu},
  title = {Milo Bitcoin: AI-Powered Bitcoin Quantitative Analysis Assistant},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/HugMilo/milo-bitcoin-gpt-oss-20b-lora-v1}},
}
```

## License

MIT License - Free for educational and commercial use

## Acknowledgments

- Base model: [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit)
- Training framework: [Unsloth](https://github.com/unslothai/unsloth)
- Fine-tuning: [TRL](https://github.com/huggingface/trl) and [PEFT](https://github.com/huggingface/peft)

---

*Building the future of AI-powered crypto analysis, one meow at a time* 🐾₿