wmaousley
/

MiniCrit-7B

@@ -1,202 +1,200 @@
 ---
-base_model: Qwen/Qwen2-7B-Instruct
 library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.12.0

 ---
+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.