llama-1b-tool-router-grpo

LLaMA 1B Tool Router - GRPO Trained

A 1B parameter model trained with pure PyTorch GRPO for fast, accurate tool selection in agentic AI systems.

Results

Why This Model?

100x cheaper. 10x faster. Runs anywhere.

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "debashis/llama-1b-tool-router-grpo",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("debashis/llama-1b-tool-router-grpo")

def call_tool(query: str, tools: list) -> str:
    tools_text = ""
    for i, tool in enumerate(tools, 1):
        tools_text += f"{i}. {tool['name']}\n"
        tools_text += f"   Description: {tool['description']}\n"
        tools_text += f"   Parameters: {', '.join(tool['parameters'])}\n"
    
    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a function calling assistant. Based on the user's query, call the appropriate function with correct arguments.

Available functions:
{tools_text}
Respond with a JSON object: {{"name": "function_name", "arguments": {{"param1": "value1"}}}}

Only output JSON.<|eot_id|><|start_header_id|>user<|end_header_id|}

{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.1, do_sample=False)
    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

Example

tools = [
    {"name": "search_flights", "description": "Search for flights", 
     "parameters": ["origin", "destination", "date"]},
    {"name": "book_hotel", "description": "Book a hotel room", 
     "parameters": ["city", "checkin", "checkout"]},
]

result = call_tool("Find flights from NYC to London on Dec 25th", tools)
# Output: {"name": "search_flights", "arguments": {"origin": "NYC", "destination": "London", "date": "Dec 25th"}}

Training Details

Training Curves

Limitations

• May struggle with 10+ similar tools
• Complex nested objects may lose detail
• Domain-specific tools benefit from additional fine-tuning

Links

• Training Code: GitHub
• Dataset: Salesforce/xlam-function-calling-60k

Citation

@misc{llama-tool-router-grpo-2025,
  author = {Debashis Ghosh},
  title = {LLaMA 1B Tool Router: GRPO-Trained for Agentic Systems},
  year = {2026},
  publisher = {Hugging Face}
}