# 📊 CodeLlama Evaluation Summary

**Date:** November 25, 2025  
**Model:** `codellama-fifo-v1`

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## 🎯 Quick Summary

| Metric | Value |
|--------|-------|
| **Training Samples Avg Similarity** | 13.30% |
| **Test Samples Avg Similarity** | 0.93% |
| **Overall Similarity** | 7.11% |
| **Code Generation Rate** | 50% (training only) |

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## ✅ What Worked

1. **Model Loading:** Successfully loads with LoRA adapters
2. **Training Samples:** Partial code generation (module declarations)
3. **Training Sample 2:** 20.70% similarity (best result)

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## ❌ Critical Issues

1. **Incomplete Code:** Training samples generate only module declarations
2. **Text Instead of Code:** Test samples generate repetitive text notes
3. **Repetition:** Severe repetition in test sample outputs
4. **Early Stopping:** Code generation stops before completion

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## 🔧 Immediate Actions Needed

1. **Fix Prompt Format**
   - Match training data format exactly
   - Test without system prompt prefix
   - Add explicit code generation instruction

2. **Adjust Inference Parameters**
   - Try lower temperature (0.1-0.2)
   - Increase max_new_tokens
   - Test different stopping criteria

3. **Check Training Data**
   - Verify all samples have complete code
   - Ensure consistent formatting
   - Remove any text-only samples

4. **Re-test with Adjusted Prompts**
   - Use exact training format
   - Test simpler prompts
   - Verify generation doesn't stop early

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## 📈 Detailed Results

See `EVALUATION_REPORT.md` for complete analysis.

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**Status:** ⚠️ **NEEDS IMPROVEMENT**  
**Next Steps:** Adjust prompts and inference parameters, then re-test.