---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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# LoRA Distillation Evaluation Report

## 1. Executive Summary
This report outlines the final evaluation metrics of the reasoning distillation pipeline. By fine-tuning a 1.5B parameter base model on the Chain-of-Thought (CoT) outputs of a 7B parameter teacher model, we achieved a **+15.3%** absolute improvement in autonomous coding capabilities.

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## 2. Model Comparison

| Model | Role | Average Pass Rate |
| :--- | :--- | :---: |
| **Qwen2.5-Coder-7B (Teacher)** | Dataset Generator | **96.9%** |
| **Qwen2.5-Coder-1.5B (Base)** | Baseline Coder | **64.5%** |
| **Qwen2.5-Coder-1.5B (Distilled/LoRA)** | Distilled Agent | **79.8%** |

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## 3. Key Observations & Analysis

### The Base Model's Weakness
The un-trained 1.5B base model demonstrated a tendency to rush into code generation, resulting in brittle algorithms that failed edge cases. While it occasionally "cheated" using built-in Python functions (e.g., using `.sort()` for O(log n) requirements), its structural logic failed on complex Dynamic Programming and boundary checks.

### The LoRA Model's Strength (Distilled Reasoning)
By injecting `[REASONING]` tokens during Supervised Fine-Tuning (SFT), the LoRA adapter successfully forced the 1.5B model to adopt a "think-before-acting" paradigm. 
* It achieved near-perfect scores (95%+) on complex algorithmic edge cases.
* It demonstrated active problem deconstruction before writing Python code.
* **Overall Delta:** A massive +10 problems fully solved, bringing the baseline from 64.5% to 79.8%.

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