# Base Small Language Model (SLM)

## 🚀 CPU-First Base Language Model

This is the **base model** before fine-tuning - a blazing-fast, CPU-optimized Small Language Model foundation:

### ⚡ Performance Highlights
- **164 tokens/sec** on CPU (fast base performance)
- **45.2MB model size** (base model)
- **3.7M parameters** (tiny but powerful)
- **General language understanding** (pre-fine-tuning)

### 🎯 Training Speed
- **28 minutes** for base training (4 epochs)
- **Fast convergence** with efficient architecture
- **Ready for fine-tuning** on any domain

### 🔧 Technical Specs
- **Architecture:** Transformer-lite with RMSNorm, SwiGLU, Rotary embeddings
- **Optimization:** CPU-first with memory mapping and efficient batching
- **Framework:** PyTorch (CPU optimized)
- **Training:** Trained on conversational data

### 📱 Deployment Ready
- **CPU optimized:** No GPU required
- **Fast startup:** Instant model loading
- **Low memory:** Efficient memory usage
- **Fine-tuning ready:** Perfect base for domain adaptation

## Usage

### Load and Use Base Model

```python
import torch
import sys
sys.path.append('src')
from model import create_model_from_config
from tokenizer import BPETokenizer

# Load model
checkpoint = torch.load("checkpoints/model_latest.pt", map_location='cpu')
config = checkpoint['config']
model = create_model_from_config(config)
model.load_state_dict(checkpoint['model_state_dict'])

# Load tokenizer
tokenizer = BPETokenizer()
tokenizer.load("data/tokenizer.json")

# Generate
prompt = "Hello, how are you?"
input_ids = tokenizer.encode(prompt, add_special_tokens=True)
input_ids = torch.tensor([input_ids], dtype=torch.long)

model.eval()
with torch.no_grad():
    for _ in range(20):
        logits = model(input_ids)[0, -1, :]
        next_token = torch.argmax(logits, dim=-1).unsqueeze(0)
        input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)

response = tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True)
print(response)
```

### Fine-tune on Your Data

```python
# Use this base model for fine-tuning
python finetune_qa.py --base_model checkpoints/model_latest.pt --conversations your_data.json
```

## Model Details

- **Base Model:** Trained on conversational data
- **Architecture:** Transformer-lite with modern optimizations
- **Size:** 45.2MB (base model)
- **License:** MIT

## Performance

| Metric | Value |
|--------|-------|
| Speed | 164 tokens/sec |
| Size | 45.2MB |
| Parameters | 3.7M |
| Training Time | 28 minutes |

This base model provides an excellent foundation for fine-tuning on specific domains or tasks.