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TRAINING_COMPLETE.md

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+# ✅ Training Complete! CodeLlama Fine-Tuned with Chat Format
+
+## 🎉 Training Summary
+
+**Status:** ✅ **COMPLETE**  
+**Model Location:** `training-outputs/codellama-fifo-v2-chat`  
+**Training Time:** ~4.5 minutes (270 seconds)
+
+---
+
+## 📊 Training Metrics
+
+### Loss Progression:
+- **Initial Loss (Epoch 1):** 1.1125
+- **Final Loss (Epoch 5):** 0.626
+- **Validation Loss:** 0.609
+- **Average Training Loss:** 0.855
+
+### Training Progress:
+- ✅ Completed all 5 epochs
+- ✅ 25 training steps total (4 steps per epoch)
+- ✅ 2 validation steps
+- ✅ Loss steadily decreased from 1.11 → 0.63
+
+---
+
+## 📈 Training Details
+
+### Configuration:
+- **Base Model:** CodeLlama-7B-Instruct
+- **Dataset Format:** Chat template format (`<s>[INST]...[/INST]`)
+- **Training Samples:** 70
+- **Validation Samples:** 9
+- **Total Steps:** 25 (with gradient accumulation)
+- **Batch Size:** 4
+- **Gradient Accumulation:** 4 (effective batch size: 16)
+- **Learning Rate:** 2e-5
+- **Max Length:** 1536 tokens
+- **LoRA Rank:** 48
+- **LoRA Alpha:** 96
+- **LoRA Dropout:** 0.15
+
+### Model Statistics:
+- **Trainable Parameters:** 119,930,880 (3.31% of total)
+- **Total Parameters:** 3,620,474,880
+- **Device:** CUDA (NVIDIA A100-SXM4-40GB)
+
+---
+
+## 🚀 Next Steps
+
+### 1. Test the New Model
+
+```bash
+cd /workspace/ftt/codellama-migration
+source /venv/main/bin/activate
+
+# Test with a training sample
+python3 scripts/inference/inference_codellama.py \
+    --mode local \
+    --model-path training-outputs/codellama-fifo-v2-chat \
+    --base-model-path models/base-models/CodeLlama-7B-Instruct \
+    --prompt "You are Elinnos RTL Code Generator v1.0, a specialized Verilog/SystemVerilog code generation agent. Your role: Generate clean, synthesizable RTL code for hardware design tasks. Output ONLY functional RTL code with no \$display, assertions, comments, or debug statements.
+
+Generate a synchronous FIFO with 8-bit data width, depth 4, write_enable, read_enable, full flag, empty flag." \
+    --max-new-tokens 1000 \
+    --temperature 0.1
+```
+
+### 2. Run Evaluation
+
+Test the model on training and test samples to verify it generates Verilog code correctly:
+
+```bash
+python3 test_samples.py
+```
+
+---
+
+## ✅ Key Improvements
+
+1. **✅ Correct Format:** Model trained with CodeLlama chat template format
+2. **✅ Proper Learning:** Loss decreased consistently over 5 epochs
+3. **✅ Validation:** Model validated on separate validation set
+4. **✅ Checkpointing:** Model checkpoints saved for resume capability
+
+---
+
+## 📝 Files Generated
+
+- ✅ **Model:** `training-outputs/codellama-fifo-v2-chat/`
+- ✅ **Config:** `training-outputs/codellama-fifo-v2-chat/training_config.json`
+- ✅ **Checkpoints:** Saved during training (if enabled)
+
+---
+
+## 🎯 Expected Results
+
+With the new chat format model, you should now see:
+- ✅ **Verilog code generation** (not unrelated text)
+- ✅ **Proper code structure** (module...endmodule)
+- ✅ **Accurate FIFO implementations**
+- ✅ **Matches training data format**
+
+---
+
+**Training completed successfully! Model is ready for testing.**
+