A generalist reasoning LLM trained on synthetic chain-of-thought traces over STEM data. Led as a research project during Sep 2023 — before reasoning-focused models became mainstream.
## Overview
Perry is a fine-tuned LLaMA 2 7B model designed to improve reasoning capabilities through synthetic CoT supervision. The core idea: generate structured reasoning traces on STEM problems and use them to teach the model to think step-by-step, resulting in stronger generalization across reasoning benchmarks.
Models were trained at 7B and 13B scales using compute-efficient methods.
## Results
Improvements over LLaMA 2 7B (as of Sep 2023):
| Benchmark | Perry-7B | LLaMA 2 7B | Delta |
|-----------|----------|------------|-------|
| MMLU (5-shot) | 46.18 | 43.80 | +2.38 |
| TruthfulQA (0-shot) | 40.08 | 38.98 | +1.10 |
| GSM8K (5-shot) | 10.31 | 5.38 | +4.93 |
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("dotvignesh/perry-7b")
