# Local Model Setup - Solution Summary

## 🎯 Problem Resolved

**Issue**: Training failed with `OSError: [Errno 116] Stale file handle` when trying to download/use models from HuggingFace cache.

**Root Cause**: Corrupted NFS file handle in HuggingFace cache directory preventing model access.

**Solution**: Downloaded Mistral-7B-v0.1 model directly to workspace, bypassing the corrupted cache.

---

## 📦 Model Location

```
/workspace/ftt/base_models/Mistral-7B-v0.1
```

**Size**: 28 GB (includes both PyTorch and SafeTensors formats)

**Contents**:
- ✓ Model weights (model-00001-of-00002.safetensors, model-00002-of-00002.safetensors)
- ✓ Tokenizer (tokenizer.model, tokenizer.json)
- ✓ Configuration files (config.json, generation_config.json)

---

## 🔧 Changes Made

### 1. Downloaded Model Locally
Used `huggingface-cli` to download model directly to workspace:
```bash
huggingface-cli download mistralai/Mistral-7B-v0.1 \
  --local-dir /workspace/ftt/base_models/Mistral-7B-v0.1 \
  --local-dir-use-symlinks False
```

### 2. Updated Gradio Interface
**File**: `/workspace/ftt/semicon-finetuning-scripts/interface_app.py`

**Change**: Updated default base model path from HuggingFace ID to local path:
```python
# Before:
value="mistralai/Mistral-7B-v0.1"

# After:
value="/workspace/ftt/base_models/Mistral-7B-v0.1"
```

### 3. Restarted Interface
Killed old Gradio process and started fresh instance with updated configuration.

---

## 🚀 How to Use

### Starting Training

1. **Access Gradio Interface**:
   - The interface is running on port 7860
   - Access via the public link displayed in the terminal

2. **Fine-tuning Tab**:
   - Base Model field now defaults to: `/workspace/ftt/base_models/Mistral-7B-v0.1`
   - You can still use HuggingFace model IDs if needed
   - Upload your dataset or use HuggingFace datasets
   - Configure training parameters
   - Click "Start Fine-tuning"

3. **Monitor Training**:
   - Status updates in real-time
   - Progress bar shows epoch and loss
   - Logs are scrollable with copy functionality

### Using Other Models

If you want to use a different base model:

**Option 1: Download Another Model Locally**
```bash
cd /workspace/ftt
source /venv/main/bin/activate

# Download model
huggingface-cli download <model-id> \
  --local-dir /workspace/ftt/base_models/<model-name> \
  --local-dir-use-symlinks False

# Use the path in Gradio:
# /workspace/ftt/base_models/<model-name>
```

**Option 2: Use HuggingFace ID Directly**
- Simply enter the model ID in the Base Model field (e.g., `mistralai/Mistral-7B-Instruct-v0.2`)
- The script will download it if not cached (may hit cache issues if they persist)

---

## 🔍 Verification

### Check Model is Accessible
```bash
python3 << 'EOF'
from transformers import AutoTokenizer, AutoConfig

model_path = "/workspace/ftt/base_models/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
config = AutoConfig.from_pretrained(model_path, local_files_only=True)

print(f"✓ Tokenizer: {len(tokenizer)} tokens")
print(f"✓ Model: {config.model_type}")
EOF
```

### Check Gradio Status
```bash
# Check process
ps aux | grep interface_app.py

# Check port
lsof -i :7860

# View logs (if started with nohup)
tail -f /tmp/gradio_interface.log
```

---

## 📊 Interface Features

### Fine-tuning Section
- ✓ File upload support (JSON/JSONL)
- ✓ HuggingFace dataset integration
- ✓ Automatic train/validation/test split
- ✓ Max sequence length up to 6000
- ✓ GPU-based parameter recommendations
- ✓ Detailed tooltips for all parameters
- ✓ Real-time progress tracking
- ✓ Checkpoint/resume functionality

### API Hosting Section
- ✓ Host fine-tuned models from local paths
- ✓ Host models from HuggingFace repositories
- ✓ FastAPI with automatic documentation
- ✓ Health checks and status monitoring

### Test Inference Section
- ✓ Test local fine-tuned models
- ✓ Test HuggingFace models
- ✓ Adjustable max-length (up to 6000)
- ✓ Temperature control with tooltips
- ✓ Uses API if running, otherwise direct loading

### UI Controls
- ✓ Stop Training button
- ✓ Refresh Status button
- ✓ Scrollable logs with copy functionality
- ✓ Progress bars for training
- ✓ 🛑 Shutdown Gradio Server button (System Controls)

---

## 🐛 Troubleshooting

### Issue: Cache errors persist
**Solution**: Always use local model paths from `/workspace/ftt/base_models/`

### Issue: Training logs not updating
**Solution**: 
1. Click "Refresh Status" button
2. Check that training process is running: `ps aux | grep finetune_mistral`

### Issue: Interface not accessible
**Solution**:
```bash
# Check if running
lsof -i :7860

# Restart if needed
pkill -f interface_app.py
cd /workspace/ftt/semicon-finetuning-scripts
python3 interface_app.py
```

### Issue: Out of memory during training
**Solution**:
1. Reduce batch size
2. Reduce max sequence length
3. Enable gradient checkpointing (already enabled in script)
4. Use LoRA with lower rank (r=8 instead of r=16)

---

## 📝 Technical Details

### Training Script
**Location**: `/workspace/ftt/semicon-finetuning-scripts/models/msp/ft/finetune_mistral7b.py`

**Key Features**:
- LoRA fine-tuning for memory efficiency
- Gradient checkpointing enabled
- Automatic device detection (CUDA/MPS/CPU)
- Resume from checkpoint support
- JSON configuration export

### Fine-tuning Command (Generated by Interface)
```bash
python3 -u /workspace/ftt/semicon-finetuning-scripts/models/msp/ft/finetune_mistral7b.py \
  --base-model /workspace/ftt/base_models/Mistral-7B-v0.1 \
  --dataset /path/to/your/dataset.jsonl \
  --output-dir ./your-finetuned-model \
  --max-length 2048 \
  --num-epochs 3 \
  --batch-size 4 \
  --learning-rate 2e-4 \
  --lora-r 16 \
  --lora-alpha 32
```

---

## 🎉 Success Criteria

You'll know everything is working when:

1. ✅ Gradio interface loads without errors
2. ✅ Base model field shows local path
3. ✅ Training starts without cache errors
4. ✅ Progress updates appear in UI
5. ✅ Model weights are saved to output directory

---

## 📚 Related Files

- **Interface**: `/workspace/ftt/semicon-finetuning-scripts/interface_app.py`
- **Training Script**: `/workspace/ftt/semicon-finetuning-scripts/models/msp/ft/finetune_mistral7b.py`
- **Base Model**: `/workspace/ftt/base_models/Mistral-7B-v0.1/`
- **Startup Script**: `/workspace/ftt/semicon-finetuning-scripts/start_interface.sh`
- **Requirements**: `/workspace/ftt/semicon-finetuning-scripts/requirements_interface.txt`

---

## 🆘 Support

If you encounter any issues:

1. Check this document's troubleshooting section
2. Review the training logs in the UI
3. Check process status: `ps aux | grep -E "interface_app|finetune_mistral"`
4. Review cache directories are clear: `ls -lh /workspace/.hf_home/hub/`

---

*Last Updated: 2025-11-24*
*Solution: Local model download to bypass corrupted HuggingFace cache*