README.md

---
license: mit
language:
  - en
tags:
  - finance
  - financial-reasoning
  - adversarial
  - critique-model
  - lora
  - safety
  - trading
datasets:
  - wmaousley/minicrit-training-12k
  - wmaousley/finrebut-600
model-index:
  - name: MiniCrit-1.5B
    results:
      - task:
          type: text-classification
          name: Financial Reasoning Critique
        dataset:
          name: MiniCrit-Training-12k
          type: wmaousley/minicrit-training-12k
        metrics:
          - name: Weak Reasoning F1
            type: f1
            value: 0.82
          - name: Hallucination Detection F1
            type: f1
            value: 0.76
---

# 🧠 MiniCrit-1.5B  
**Adversarial Financial Critic LLM for Trading-Rationale Evaluation**

MiniCrit-1.5B is an adversarial financial-critic LLM trained to evaluate, stress-test, and rebut trading rationales produced by other LLMs.  
It serves as a **validator layer** for autonomous or semi-autonomous trading systems where hallucinated logic or weak reasoning may create financial risk.

The model **does not** generate trades.  
It **only** critiques reasoning quality.

---

## 📦 Model Description

**Base Model:** 1.5B-parameter transformer  
**Tuning Method:** ATAC-LoRA  
**Training Data:**  
- **MiniCrit-Training-12k** (12,132 rationale → critique pairs)  
- **FinRebut-600** curated evaluation set  

**Primary Abilities**
- Detect flawed or risky trading logic  
- Identify hallucinated financial statistics  
- Flag improper use of indicators  
- Provide adversarial rebuttals  
- Validate rationales before execution  

---

## 📚 Datasets

### **1. MiniCrit-Training-12k**  
Large-scale dataset of institutional rationale/critique pairs.  
➡ https://huggingface.co/datasets/wmaousley/minicrit-training-12k

### **2. FinRebut-600**  
Curated, high-quality adversarial rebuttal set.  
➡ https://huggingface.co/datasets/wmaousley/finrebut-600

Both datasets are available under **CC-BY-4.0**.

---

## 🚀 Intended Use

### ✔ Recommended:
- Validating LLM-generated trading rationales  
- Hallucination detection in financial explanations  
- Model-to-model critique pipelines  
- AI-safety analysis for financial agents  
- Research in adversarial financial reasoning  

### ❌ Not Recommended:
- Generating trades  
- Investment decision-making  
- Fully autonomous trading without human review  

This model is for **research** and **evaluation** only.

---

## 📈 Performance

### Forward-Test (Paper Trading)
| Metric | Value |
|--------|-------|
| Sharpe (baseline) | +0.20 |
| Sharpe (MiniCrit-validated) | **+0.80** |
| Hallucination reduction | **–48%** |
| Weak-reasoning detection F1 | **0.82** |
| Hallucination F1 | **0.76** |

### Qualitative Strengths
- Detects regime mismatch  
- Identifies liquidity illusions  
- Flags circular or self-justifying logic  
- Highlights data-mining  
- Generates strong evidence-demanding rebuttals  

---

## 🔧 Usage

> This example works after the full model is uploaded to this repository.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "wmaousley/MiniCrit-1.5B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """Rationale:
'NVDA is oversold so I will long because RSI is below 30.'

Provide a critique.
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    do_sample=False,
    temperature=0.0,
)

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

---

## 🛡️ Safety & Limitations

### Model Risks
- May produce overly forceful critiques  
- Sensitive to prompt phrasing  
- Limited deep macroeconomic understanding  
- Not a trading or financial-advice model  

### Mitigations
- Does not produce trade signals  
- Outputs critique only  
- Warns about high-risk reasoning patterns  
- Datasets avoid target-label leakage  

---

## 📄 Citation

If you use MiniCrit-1.5B, please cite:

```
Ousley, W. A. (2025). MiniCrit-1.5B: Adversarial Financial Critic Model.
Zenodo. https://doi.org/10.5281/zenodo.17594497
```

---

## 👤 Author

**William Alexander Ousley**  
AI/ML Researcher — Autonomous Trading Systems  
ORCID: https://orcid.org/0009-0009-2503-2010  

---

## 🤝 Contributions

Pull requests welcome.

Ideal contributions include:  
- Dataset expansions  
- Adversarial-evaluation benchmarks  
- Safety improvements  
- ATAC-LoRA optimization  
- Forward-test research  

---

## 📬 Contact

📧 Email: **founders@antagon.ai**  
🔗 GitHub: https://github.com/wmaousley