README.md

---
license: apache-2.0
language:
- en
library_name: peft
base_model: Qwen/Qwen2-7B-Instruct
tags:
- finance
- trading
- ai-safety
- adversarial-testing
- critique
- lora
- qwen2
datasets:
- custom
pipeline_tag: text-generation
---

# MiniCrit-7B: Adversarial AI Critique Model

<p align="center">
  <img src="https://img.shields.io/badge/Model-MiniCrit--7B-blue" alt="Model">
  <img src="https://img.shields.io/badge/Base-Qwen2--7B--Instruct-green" alt="Base Model">
  <img src="https://img.shields.io/badge/Method-LoRA-orange" alt="Method">
  <img src="https://img.shields.io/badge/License-Apache%202.0-red" alt="License">
</p>

## Model Description

**MiniCrit-7B** is a specialized adversarial AI model trained to identify flawed reasoning in autonomous AI systems before they cause catastrophic failures. Developed by [Antagon Inc.](https://antagon.ai), MiniCrit acts as an AI "devil's advocate" that critiques trading rationales, detecting issues like:

- Overconfident predictions
- Overfitting to historical patterns
- Spurious correlations
- Survivorship bias
- Confirmation bias
- Missing risk factors

## Model Details

| Attribute | Value |
|-----------|-------|
| **Developer** | Antagon Inc. (CAGE: 17E75, UEI: KBSGT7CZ4AH3) |
| **Base Model** | Qwen/Qwen2-7B-Instruct |
| **Method** | LoRA (Low-Rank Adaptation) |
| **Trainable Parameters** | 40.4M (0.53% of 7.6B total) |
| **Training Data** | 11.7M critique examples |
| **Training Hardware** | NVIDIA H100 PCIe (80GB) via [Lambda Labs](https://lambdalabs.com) GPU Grant |
| **License** | Apache 2.0 |

## Training Details

### Dataset
- **Size**: 11,674,598 training examples
- **Format**: Rationale → Critique pairs
- **Domain**: Financial trading signals (stocks, options, crypto)

### Training Configuration
```yaml
learning_rate: 2e-4
lr_scheduler: cosine
warmup_steps: 500
batch_size: 32 (effective)
max_sequence_length: 512
epochs: 1
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
```

### Training Progress
- **Steps Completed**: 35,650 / 364,831 (9.8%)
- **Initial Loss**: 1.8573
- **Final Loss**: 0.7869
- **Loss Reduction**: 57.6%

## Usage

### Installation
```bash
pip install transformers peft torch
```

### Loading the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Antagon/MiniCrit-7B")
```

### Inference
```python
def critique_rationale(rationale: str) -> str:
    prompt = f"### Rationale:\n{rationale}\n\n### Critique:\n"
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Critique:\n")[-1]

# Example
rationale = "AAPL long: MACD bullish crossover with supporting momentum."
critique = critique_rationale(rationale)
print(critique)
```

### Example Output
```
Input: "META long: Bollinger Band expansion with supporting momentum."

Output: "While Bollinger Band expansion can signal volatility, META's recent 
expansion isn't necessarily predictive; it could be a reaction to news, not 
a precursor to sustained movement. Furthermore, relying solely on momentum 
without considering overbought/oversold levels may lead to premature entry, 
especially if the expansion is already near its peak."
```

## Performance

### Production Metrics (MiniCrit-1.5B)
- **False Signal Reduction**: 35%
- **Sharpe Ratio Improvement**: +0.28
- **Live Trades Processed**: 38,000+

### Training Metrics
| Metric | Value |
|--------|-------|
| Initial Loss | 1.8573 |
| Final Loss | 0.7869 |
| Loss Reduction | 57.6% |
| Gradient Norm (avg) | 0.45 |

## Intended Use

### Primary Use Cases
- Validating AI trading signals before execution
- Identifying reasoning flaws in autonomous systems
- Risk assessment for algorithmic trading
- Quality assurance for AI-generated analysis

### Out-of-Scope Uses
- This model is NOT intended for:
  - Generating trading signals
  - Financial advice
  - Autonomous trading decisions

## Limitations

- Trained primarily on trading/finance domain
- May not generalize well to other critique domains without fine-tuning
- Checkpoint represents partial training (9.8% of planned steps)
- Should be used as a supplement to human judgment, not a replacement

## Citation

```bibtex
@misc{minicrit7b2026,
  title={MiniCrit-7B: Adversarial AI Critique for Trading Signal Validation},
  author={Ousley, William Alexander and Ousley, Jacqueline Villamor},
  year={2026},
  publisher={Antagon Inc.},
  url={https://huggingface.co/Antagon/MiniCrit-7B}
}
```

## Contact

- **Company**: Antagon Inc.
- **Website**: [antagon.ai](https://antagon.ai)
- **CAGE Code**: 17E75
- **UEI**: KBSGT7CZ4AH3

## Acknowledgments

We gratefully acknowledge **[Lambda Labs](https://lambdalabs.com)** for providing GPU compute through their Research Grant program. MiniCrit-7B was trained on Lambda's H100 infrastructure, and their support has been instrumental in advancing our AI safety research.

## License

This model is released under the Apache 2.0 License.