Small Language Model (SLM) Agent Fine-tuning with MCP Integration

This project provides a complete pipeline for fine-tuning small language models for agentic use cases with Model Context Protocol (MCP) server integration and external tool usage.

🎯 Project Overview

Fine-tune small language models to create an intelligent agent capable of:

• Tool Selection: Automatically choose appropriate tools for tasks
• Parameter Extraction: Extract correct parameters for tool calls
• Multi-step Reasoning: Chain multiple tool calls to complete complex tasks
• MCP Integration: Seamlessly work with external MCP servers
• Error Handling: Gracefully handle tool failures and edge cases

🏗️ Architecture

User Request → SLM Agent → Tool Selection → MCP Client → External Tools
                     ↑                                              ↓
              Final Response ← Response Generation ← Tool Results ←

🚀 Quick Start

1. Environment Setup

# Clone and navigate to project
git clone <repository>
cd model-agent-finetuning

# Create virtual environment
python -m venv model-env
source model-env/bin/activate  # Windows: model-env\Scripts\activate

# Run setup script
python scripts/setup.py

2. Configuration

Review and customize config/training_config.yaml:

• Adjust batch sizes based on your GPU memory
• Modify LoRA parameters for your use case
• Set training epochs and learning rate

3. Training

# Train with default settings
python scripts/train_model.py --config config/training_config.yaml

# Custom training
python scripts/train_model.py \
  --config config/training_config.yaml \
  --data-samples 10000 \
  --wandb-project my-model-agent

4. Evaluation

# Comprehensive evaluation
python scripts/evaluate_model.py \
  --model-path ./models/model-agent-final \
  --run-benchmarks

# Generate evaluation dataset
python scripts/evaluate_model.py \
  --model-path ./models/model-agent-final \
  --generate-eval-data \
  --eval-samples 500

5. Inference Demo

# Interactive demo
python scripts/inference_demo.py \
  --model-path ./models/model-agent-final \
  --mode interactive

# Run benchmarks
python scripts/inference_demo.py \
  --model-path ./models/model-agent-final \
  --mode benchmark

📊 Success Metrics

The model is evaluated on multiple dimensions:

Core Metrics

• Tool Selection Accuracy: >85% (correct tool choice)
• Parameter Extraction: >90% (accurate parameter parsing)
• Task Completion Rate: >80% (successful end-to-end execution)
• Hallucination Rate: <10% (factual accuracy)
• Response Time: <5s average (performance)

Evaluation Categories

• Single Tool Usage: Simple, direct tool calls
• Multi-step Tasks: Complex workflows requiring multiple tools
• Error Handling: Graceful failure recovery
• Context Maintenance: Coherence across conversation turns

🔧 Customization

Adding New Tools

1. Update MCP Client (src/inference/mcp_client.py):

self.available_tools["new_tool"] = {
    "server": "tool_server",
    "endpoint": "/new_endpoint",
    "description": "Tool description",
    "parameters": {
        "param1": {"type": "string", "required": True}
    }
}

1. Update Dataset Builder (src/data/dataset_builder.py):

# Add tool scenarios
tool_scenarios["new_tool"] = ["scenario1", "scenario2"]

1. Regenerate Training Data:

python scripts/train_model.py --config config/training_config.yaml

Fine-tuning Hyperparameters

Key parameters in config/training_config.yaml:

lora:
  r: 16 # LoRA rank (8-64)
  lora_alpha: 32 # LoRA scaling (16-64)
  lora_dropout: 0.1 # Dropout rate (0.05-0.2)

training:
  learning_rate: 2.0e-4 # Learning rate (1e-4 to 5e-4)
  num_train_epochs: 3 # Training epochs (2-5)
  per_device_train_batch_size: 4 # Batch size (2-8)

📁 Project Structure

slm-agent/
├── requirements.txt              # Dependencies
├── README.md                     # This file
├── config/
│   └── training_config.yaml      # Training configuration
├── src/
│   ├── data/                     # Data processing modules
│   │   ├── dataset_builder.py    # Training data generation
│   │   └── data_formatter.py     # Data formatting utilities
│   ├── training/                 # Training modules
│   │   ├── trainer.py            # Main training logic
│   │   └── evaluation.py         # Model evaluation
│   └── inference/                # Inference modules
│       ├── model_handler.py      # Model inference handler
│       └── mcp_client.py         # MCP client implementation
├── scripts/
│   ├── setup.py                  # Environment setup
│   ├── train_model.py           # Training script
│   ├── evaluate_model.py        # Evaluation script
│   └── inference_demo.py        # Demo script
└── data/                        # Data directories
    ├── raw/                     # Raw data files
    ├── processed/               # Processed datasets
    └── evaluation/              # Evaluation datasets

🛠️ Development Workflow

1. Data Preparation Phase

# Generate custom training data
python -c "
from src.data.dataset_builder import AgenticDatasetBuilder
builder = AgenticDatasetBuilder()
dataset = builder.generate_dataset(5000)
builder.save_dataset(dataset, 'custom_dataset.json')
"

2. Experimental Training

# Quick training run for testing
python scripts/train_model.py \
  --config config/training_config.yaml \
  --data-samples 1000 \
  --wandb-project model-experiment

3. Model Evaluation

# Detailed evaluation with custom metrics
python scripts/evaluate_model.py \
  --model-path ./results/checkpoint-1000 \
  --eval-dataset ./data/custom/eval.json \
  --run-benchmarks

4. Production Deployment

# Export optimized model
python -c "
from src.training.trainer import AgentTrainer
trainer = AgentTrainer('config/training_config.yaml')
trainer.save_model_for_inference('./models/production')
"

🔍 Troubleshooting

Common Issues

GPU Memory Errors:

# Reduce batch sizes in config/training_config.yaml
per_device_train_batch_size: 2
gradient_accumulation_steps: 8

Slow Training:

# Enable mixed precision
bf16: true
fp16: false # Use bf16 instead of fp16 for better stability

Tool Call Parsing Issues:

• Check tool usage format in training data
• Validate JSON parameter formatting
• Ensure consistent tool naming

Model Not Learning:

• Increase learning rate to 3e-4
• Add more diverse training examples
• Check data quality and formatting

Performance Optimization

Memory Usage:

• Use gradient checkpointing: gradient_checkpointing: true
• Enable 4-bit quantization: load_in_4bit: true
• Reduce sequence length: max_seq_length: 1024

Training Speed:

• Increase batch size if memory allows
• Use multiple GPUs with --multi_gpu
• Enable compilation: torch_compile: true (PyTorch 2.0+)

📈 Monitoring

Weights & Biases Integration

The project includes comprehensive W&B logging:

• Training/validation loss curves
• Tool usage accuracy metrics
• Parameter extraction success rates
• Response quality scores
• Hardware utilization

Access your runs at: https://wandb.ai/<username>/<project>

Local Monitoring

Check training progress:

# View training logs
tail -f training.log

# Monitor GPU usage
nvidia-smi -l 1

# Check disk space
df -h

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/new-capability
3. Add tests: Ensure new features have appropriate test coverage
4. Submit PR: Include detailed description and test results

Adding New Evaluation Metrics

# In src/training/evaluation.py
def _evaluate_custom_metric(self, sample, result):
    """Add your custom evaluation logic."""
    return score

# Update evaluate_full_model to include new metric

📚 References

• LoRA: Low-Rank Adaptation
• Model Context Protocol
• Weights & Biases Documentation