🎯 Final Answer: Why Response Not Accurate & What To Do

❌ Root Cause: Format Mismatch

The model is generating unrelated Kotlin/Android code instead of Verilog because:

CodeLlama-Instruct expects chat template format: <s>[INST] <<SYS>>...<</SYS>> User [/INST] Response </s>
Training used simple format: instruction + EOS + response + EOS
Result: Model didn't learn the task correctly → generates random code

✅ Solution: Reformat Dataset & Retrain

✅ What I've Done:

✅ Reformatted dataset to use CodeLlama chat template format
- New file: datasets/processed/elinnos_fifo_codellama_chat_format.jsonl
✅ Split dataset into train/val/test
- Location: datasets/processed/split_chat_format/
- Train: 70 samples, Val: 9, Test: 15
✅ Updated training script to handle chat format correctly
✅ Created training script: start_training_chat_format.sh

🚀 Next Step: RETRAIN (Required)

You MUST retrain because the old model won't work with the correct format.

Quick Command:

cd /workspace/ftt/codellama-migration
source /venv/main/bin/activate
bash start_training_chat_format.sh

📊 Expected Results After Retraining:

✅ Model generates Verilog code (not unrelated text)
✅ Output matches training data format
✅ Proper code structure (module...endmodule)
✅ Accurate responses to FIFO generation requests

🔍 Why You Need to Retrain:

Old model: Trained with wrong format → confused
Can't fix with inference changes: Format mismatch is in training data
New format: Matches CodeLlama-Instruct expectations → will work correctly

📝 Files Ready:

✅ Reformatted dataset: datasets/processed/elinnos_fifo_codellama_chat_format.jsonl
✅ Split dataset: datasets/processed/split_chat_format/
✅ Training script: start_training_chat_format.sh
✅ Updated training code: scripts/training/finetune_codellama.py

Answer: Yes, you need to reformat the dataset and retrain. The format mismatch is why responses aren't accurate. Everything is ready - just run the training script!