# 🤖 Textilindo AI Training API Documentation

## 🚀 Pure API-Based Training System

This is a complete API-based training system that uses your data, configs, and the free GPU tier on Hugging Face Spaces.

## 📡 API Endpoints

### 1. **Start Training**
```bash
POST /api/train/start
```

**Request Body:**
```json
{
  "model_name": "distilgpt2",
  "dataset_path": "data/lora_dataset_20250829_113330.jsonl",
  "config_path": "configs/training_config.yaml",
  "max_samples": 10,
  "epochs": 1,
  "batch_size": 1,
  "learning_rate": 5e-5
}
```

**Response:**
```json
{
  "success": true,
  "message": "Training started successfully",
  "training_id": "train_20241025_120000",
  "status": "started"
}
```

### 2. **Check Training Status**
```bash
GET /api/train/status
```

**Response:**
```json
{
  "is_training": true,
  "progress": 45,
  "status": "training",
  "current_step": 5,
  "total_steps": 10,
  "loss": 2.34,
  "start_time": "2024-10-25T12:00:00",
  "error": null
}
```

### 3. **Get Training Data Info**
```bash
GET /api/train/data
```

**Response:**
```json
{
  "files": [
    {
      "name": "lora_dataset_20250829_113330.jsonl",
      "size": 12345,
      "lines": 33
    }
  ],
  "count": 4
}
```

### 4. **Check GPU Availability**
```bash
GET /api/train/gpu
```

**Response:**
```json
{
  "available": true,
  "count": 1,
  "name": "Tesla T4",
  "memory_gb": 15.0
}
```

### 5. **Test Trained Model**
```bash
POST /api/train/test
```

**Response:**
```json
{
  "success": true,
  "test_prompt": "Question: dimana lokasi textilindo? Answer:",
  "response": "Question: dimana lokasi textilindo? Answer: Textilindo berkantor pusat di Jl. Raya Prancis No.39, Kosambi Tim., Kec. Kosambi, Kabupaten Tangerang, Banten 15213",
  "model_path": "./models/textilindo-trained"
}
```

## 🧪 Testing the API

### 1. **Check GPU Availability**
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/gpu"
```

### 2. **View Training Data**
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/data"
```

### 3. **Start Training**
```bash
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/start" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "distilgpt2",
    "dataset_path": "data/lora_dataset_20250829_113330.jsonl",
    "config_path": "configs/training_config.yaml",
    "max_samples": 10,
    "epochs": 1,
    "batch_size": 1,
    "learning_rate": 5e-5
  }'
```

### 4. **Monitor Training Progress**
```bash
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/status"
```

### 5. **Test Trained Model**
```bash
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/test"
```

## 🔧 Training Configuration

### Available Models:
- `distilgpt2` (82M) - Small, fast, good for free tier
- `gpt2` (124M) - Original GPT-2
- `microsoft/DialoGPT-small` (117M) - Conversational

### Training Parameters:
- **max_samples**: Limit training data (10 for free tier)
- **epochs**: Number of training epochs (1-3 recommended)
- **batch_size**: Batch size (1 for free tier)
- **learning_rate**: Learning rate (5e-5 recommended)

## 🎯 Training Process

1. **Start Training**: POST to `/api/train/start`
2. **Monitor Progress**: GET `/api/train/status`
3. **Check GPU Usage**: GET `/api/train/gpu`
4. **Test Model**: POST `/api/train/test`

## 📊 Training Status Values

- `idle` - No training
- `starting` - Training initialization
- `training` - Active training
- `completed` - Training finished
- `failed` - Training error
- `stopped` - Training stopped

## ⚡ GPU Usage

The API automatically detects and uses GPU if available:
- **GPU Available**: Uses GPU with fp16 precision
- **CPU Only**: Falls back to CPU training
- **Memory Optimization**: Adjusts batch size based on available memory

## 🔍 Error Handling

### Common Errors:
- `400` - Training already in progress
- `404` - Dataset or config file not found
- `500` - Training failed (check logs)

### Error Response:
```json
{
  "detail": "Training already in progress"
}
```

## 📈 Training Monitoring

### Real-time Status:
- **Progress**: 0-100%
- **Current Step**: Current training step
- **Total Steps**: Total training steps
- **Loss**: Current training loss
- **GPU Usage**: GPU memory and utilization

### Training Logs:
Check the space logs for detailed training information.

## 🚀 Quick Start Example

```bash
# 1. Check GPU
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/gpu"

# 2. Start training
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/start" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "distilgpt2",
    "dataset_path": "data/lora_dataset_20250829_113330.jsonl",
    "max_samples": 5,
    "epochs": 1
  }'

# 3. Monitor progress
curl "https://harismlnaslm-Textilindo-AI.hf.space/api/train/status"

# 4. Test when complete
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/api/train/test"
```

## 🎉 Success Indicators

- ✅ Training starts without errors
- ✅ GPU is detected and used
- ✅ Progress increases over time
- ✅ Model saves to `./models/textilindo-trained`
- ✅ Test endpoint returns valid responses
- ✅ Chat interface works with trained model

---

*Pure API training system - No HTML interfaces! 🚀*