# 🚀 CodeLlama-7B Migration Progress Tracker

**Started:** November 25, 2025, 05:40 UTC  
**Status:** 🟡 In Progress  
**Target:** Complete migration with all critical + recommended updates

---

## 📁 Folder Structure

```
codellama-migration/
├── models/
│   └── base-models/              # Base models directory
├── datasets/
│   ├── raw/                      # Original datasets (reference)
│   └── processed/                # CodeLlama-formatted datasets
├── training-outputs/             # Fine-tuned models will be saved here
├── scripts/                      # Updated scripts (symlinks/copies)
│   ├── training/
│   ├── inference/
│   └── api/
└── MIGRATION_PROGRESS.md         # This file
```

---

## ✅ Progress Checklist

### 🔴 Critical Tasks

- [x] **Step 1:** Download CodeLlama-7B-Instruct model
  - Status: ✅ COMPLETED
  - Target: `codellama-migration/models/base-models/CodeLlama-7B-Instruct/`
  - Size: 26GB (actual size)
  - Started: 2025-11-25 05:55 UTC
  - Completed: 2025-11-25 06:03 UTC
  - Notes: ✅ Download completed successfully!
  - Files: 52 files (config.json, tokenizers, model weights)
  - Formats: Both .safetensors and .bin formats available

- [x] **Step 2:** Create CodeLlama-formatted dataset
  - Status: ✅ Completed (UPDATED)
  - Source: `elinnos_fifo_mistral_100samples_converted.jsonl`
  - Target: `codellama-migration/datasets/processed/elinnos_fifo_codellama_v1.jsonl`
  - Format: System prompt + task → ```verilog code``` (NO labels)
  - Started: 2025-11-25 05:54 UTC
  - Completed: 2025-11-25 06:00 UTC (UPDATED)
  - Notes: ✅ 94 samples reformatted, 125.6 KB file size
  - **UPDATE:** System prompt PRESERVED for domain specificity (removes generic responses)
  - **KEY:** Removed "System:" and "User:" labels to prevent conversational output

### 🟡 Recommended Tasks

- [x] **Step 3:** Update inference script with code extraction
  - Status: ✅ Completed
  - File: `codellama-migration/scripts/inference/inference_codellama.py`
  - Changes: 
    - ✅ Added `extract_code_from_response()` function
    - ✅ Changed default temperature: 0.7 → 0.3
    - ✅ Added code extraction to both streaming and non-streaming paths
  - Started: 2025-11-25 05:54 UTC
  - Completed: 2025-11-25 05:55 UTC
  - Notes: ✅ Code extraction handles ```verilog and generic ``` markers

- [x] **Step 4:** Document training parameters
  - Status: ✅ Documented
  - Parameters: 
    - Epochs: 3 → **5**
    - Learning Rate: 5e-5 → **2e-5**
    - LoRA Rank: 32 → **64**
    - LoRA Alpha: 64 → **128**
    - Temperature: 0.7 → **0.3**
  - Started: 2025-11-25 05:40 UTC
  - Completed: 2025-11-25 05:40 UTC
  - Notes: Parameters documented in migration plan

### ⚪ Optional Tasks

- [ ] **Step 5:** Update Gradio interface
  - Status: ⏳ Pending
  - File: `semicon-finetuning-scripts/interface_app.py`
  - Started: -
  - Completed: -
  - Notes: -

---

## 📊 Configuration Changes

### Model Paths
- **Old Base Model:** `/workspace/ftt/base_models/Mistral-7B-v0.1`
- **New Base Model:** `/workspace/ftt/codellama-migration/models/base-models/CodeLlama-7B-Instruct`
- **HuggingFace ID:** `codellama/CodeLlama-7b-Instruct-hf`

### Dataset Paths
- **Old Dataset:** `elinnos_fifo_mistral_100samples_CLEAN_v2.jsonl`
- **New Dataset:** `codellama-migration/datasets/processed/elinnos_fifo_codellama_v1.jsonl`

### Training Parameters
- **Epochs:** 3 → **5**
- **Learning Rate:** 5e-5 → **2e-5**
- **LoRA Rank:** 32 → **64**
- **LoRA Alpha:** 64 → **128**
- **Temperature:** 0.7 → **0.3**

---

## 📝 Change Log

### 2025-11-25 05:40 UTC - Initial Setup
- ✅ Created folder structure
- ✅ Created this progress tracking document
- ⏳ Starting Step 1: Download CodeLlama model

### 2025-11-25 05:54 UTC - Dataset & Scripts Updated
- ✅ **Step 2 COMPLETE:** Created CodeLlama-formatted dataset
  - Source: `elinnos_fifo_mistral_100samples_converted.jsonl`
  - Output: `codellama-migration/datasets/processed/elinnos_fifo_codellama_v1.jsonl`
  - Format: Removed system prompt, added ```verilog markers
  - Samples: 94 reformatted successfully (100.5 KB)
- ✅ **Step 3 COMPLETE:** Updated inference script
  - Added `extract_code_from_response()` function (lines 24-58)
  - Changed default temperature: 0.7 → 0.3 (line 142)
  - Added code extraction to streaming path (line 193)
  - Added code extraction to non-streaming path (line 219)
  - File: `codellama-migration/scripts/inference/inference_codellama.py`
- ✅ Created symlinks for training scripts (no changes needed)
- ⏳ Step 1 in progress: CodeLlama download (PID: 29047)

### 2025-11-25 05:55 UTC - Download Started
- ✅ CodeLlama-7B-Instruct download initiated
- 📝 Download log: `codellama-migration/download_log.txt`
- ⏳ Estimated completion: 10-15 minutes

### 2025-11-25 06:00 UTC - Dataset Updated with System Prompt
- ✅ **CRITICAL UPDATE:** Dataset reformatted to KEEP system prompt
- **Why:** System prompt ensures domain-specific behavior and prevents generic responses
- **Change:** 
  - ✅ System prompt content PRESERVED: "You are Elinnos RTL Code Generator..."
  - ❌ "System:" and "User:" LABELS removed (these triggered conversational mode)
  - ✅ Format: Clean instructional text + task → code
- **Result:** Best of both worlds - domain specificity + no conversation triggers
- **File Size:** 125.6 KB (increased from 100.5 KB due to system prompt)
- **Sample Format:**
  ```
  Instruction: "You are Elinnos... [system prompt]\n\nGenerate a FIFO..."
  Response: "```verilog\nmodule...```"
  ```

### 2025-11-25 06:03 UTC - CodeLlama Model Download Complete ✅
- ✅ **Step 1 COMPLETE:** CodeLlama-7B-Instruct successfully downloaded
- **Location:** `codellama-migration/models/base-models/CodeLlama-7B-Instruct/`
- **Size:** 26GB (52 files)
- **Key Files:**
  - ✅ config.json
  - ✅ tokenizer.json, tokenizer_config.json, tokenizer.model
  - ✅ model-00001-of-00002.safetensors (9.3GB)
  - ✅ model-00002-of-00002.safetensors (3.3GB)
  - ✅ pytorch_model-*.bin files (also available)
- **Download Time:** ~8 minutes (05:55 - 06:03 UTC)
- **Status:** ✅ **READY FOR TRAINING**

---

## 🔧 Script Updates Status

### Inference Script (`inference_codellama.py`)
- [ ] Code extraction function added
- [ ] Temperature default changed to 0.3
- [ ] Code marker removal logic implemented
- [ ] Tested with sample inference

### Training Script
- ✅ No changes needed (model-agnostic)

### API Server
- ✅ No changes needed (model-agnostic)

---

## 📈 Expected Outcomes

| Metric | Current (Mistral) | Target (CodeLlama) |
|--------|------------------|-------------------|
| Code Generation Rate | 16.7% | 85-95% |
| Average Match Score | 31.7% | 75-85% |
| Conversational Output | Frequent | Rare/None |

---

## 🐛 Issues & Resolutions

_Issues will be logged here as they occur_

---

## 📚 References

- Migration Plan: `/workspace/ftt/CODELLAMA_MIGRATION_PLAN.md`
- Comparison Report: `/workspace/ftt/CLEAN_V2_TRAINING_COMPARISON_REPORT.md`

---

### 2025-11-25 06:14 UTC - Dataset Splitting & Validation Scripts Created
- ✅ **Created:** Dataset splitting script (`scripts/dataset_split.py`)
- ✅ **Created:** Dataset validation script (`scripts/validate_dataset.py`)
- ✅ **Created:** Comprehensive guide (`DATASET_SPLIT_VALIDATION_GUIDE.md`)
- **Details:**
  - Splitting happens BEFORE training (manual split recommended)
  - Script handles 75/10/15 split (train/val/test)
  - Validation checks: format, content, quality, duplicates
  - All CodeLlama-specific parameters documented

### 2025-11-25 06:15 UTC - Hyperparameter Analysis Complete
- ✅ **Created:** Complete hyperparameter analysis (`HYPERPARAMETER_ANALYSIS.md`)
- **Dataset Analysis:**
  - 94 samples, avg ~322 tokens per sample
  - All samples have code markers (100%)
  - Small dataset → needs regularization
- **Optimized Parameters:**
  - LoRA Rank: 48 (balance for code patterns + small dataset)
  - Learning Rate: 2e-5 (stability)
  - Epochs: 5 (more training needed)
  - Max Length: 1536 (efficiency, sufficient for dataset)
  - Dropout: 0.15 (more regularization)
- **Efficiency:**
  - Memory: ~6-7GB (fits easily in A100)
  - Training Time: ~8-10 minutes
  - Expected improvement: 75-85% match score

### 2025-11-25 06:41 UTC - Training Started with Optimized Hyperparameters
- ✅ **Created:** Enhanced training script (`scripts/training/finetune_codellama.py`)
  - Checkpoint resume support (automatic detection)
  - Incremental fine-tuning (continue from existing adapter)
  - Fresh training option
  - Uses pre-split train/val datasets
- ✅ **Created:** Training guide (`TRAINING_GUIDE.md`)
- ✅ **Dataset Split:** 75/10/15 (train/val/test) - 70/9/15 samples
- ✅ **Training Started:** CodeLlama fine-tuning with optimized hyperparameters
  - Base Model: CodeLlama-7B-Instruct
  - Output: `training-outputs/codellama-fifo-v1`
  - Hyperparameters: All optimized values from HYPERPARAMETER_ANALYSIS.md
  - Status: 🟢 **TRAINING IN PROGRESS**

**Last Updated:** 2025-11-25 06:41 UTC  
**Current Status:** 🟢 **TRAINING IN PROGRESS**