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.
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: [email protected]
🔗 GitHub: https://github.com/wmaousley