🤖 MCP-Agent-1.7B

The first open-source small language model fine-tuned to natively speak the Model Context Protocol (MCP).

What Is This?

MCP-Agent-1.7B is Qwen/Qwen3-1.7B fine-tuned with LoRA to:

🔧 Call tools using MCP protocol (JSON-RPC format)
🔗 Plan multi-step tool chains with DAG dependencies
❓ Ask clarifying questions when info is missing (instead of hallucinating)
🛡️ Refuse dangerous requests (e.g., "delete all files")

Training

Detail	Value
Base model	Qwen/Qwen3-1.7B (2B params)
Method	LoRA SFT (rank=16, all-linear)
Dataset	muhammadtlha944/mcp-agent-training-data (16.5K examples)
Epochs	3
Learning rate	2e-4 (cosine schedule)
Effective batch size	16
Precision	fp16
Cost	$0 (trained on free Kaggle/Colab GPU)

🚀 Train It Yourself (FREE)

Option 1: Kaggle (Recommended — 30 free GPU hrs/week)

Go to kaggle.com/code → New Notebook
Settings → Accelerator → GPU T4×2 → Internet → ON
Copy-paste the code from MCP_Agent_1_7B_Kaggle.ipynb
Run all cells → wait ~2 hours → model auto-pushes to Hub

Option 2: Google Colab (Backup — free T4)

Open colab.research.google.com
Runtime → Change runtime type → T4 GPU
Copy-paste from MCP_Agent_1_7B_Training.ipynb
Run all cells → wait ~2 hours

Quick Start

from transformers import pipeline

pipe = pipeline("text-generation", model="muhammadtlha944/MCP-Agent-1.7B")

messages = [
    {"role": "system", "content": "You are an MCP agent with tools: github_search, read_file, shell_exec."},
    {"role": "user", "content": "Find all Python files that import pandas"}
]

response = pipe(messages, max_new_tokens=512)
print(response[0]['generated_text'][-1]['content'])

Research Background

This project is informed by:

STAR Framework (arxiv 2602.03022) — proved Qwen3-1.7B beats Llama-3.1-8B at function calling
TinyAgent (arxiv 2409.00608) — proved 1.1B models can match GPT-4 at focused tool calling
LoRA Without Regret — all-linear LoRA matches full fine-tuning quality

License

Apache 2.0 (same as base model)

Author

Built by Muhammad Talha — learning ML by building real projects.