This model has been fine-tuned to understand and execute calculator tool calls. It can perform mathematical calculations by generating appropriate tool call syntax.

Base Model: Qwen/Qwen2-0.5B
Training Method: Supervised Fine-Tuning (SFT)
Training Library: Tiny Tool Use
Tool: Calculator (mathematical expressions)
Parameters: 500M
Architecture: 24 layers, 896 hidden dimensions

Training Details

Training Data: 4 custom calculator examples
Training Steps: 3 steps, 1 epoch
Training Time: ~8.5 seconds
Hardware: CPU training (RTX 4060 available but CUDA compatibility issues)
Precision: float32

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/TinyToolUse-Qwen2-0.5B-Calculator")
model = AutoModelForCausalLM.from_pretrained("your-username/TinyToolUse-Qwen2-0.5B-Calculator")

# Example usage
prompt = "Human: What is 15 + 27?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Expected Output Format

The model should generate responses in the format:

tool_code: print(calculator(expression='15 + 27'))

Tool Definition

The model was trained with this calculator tool definition:

json

{
  "name": "calculator",
  "description": "Perform mathematical calculations",
  "type": "function",
  "parameters": {
    "type": "object",
    "properties": {
      "expression": {
        "type": "string",
        "description": "Mathematical expression to evaluate"
      }
    },
    "required": ["expression"]
  }
}

Training Examples

The model was trained on these examples:

"What is 2 + 2?" → tool_code: print(calculator(expression='2 + 2'))
"Calculate 10 * 5" → tool_code: print(calculator(expression='10 * 5'))
"What is 100 / 25?" → tool_code: print(calculator(expression='100 / 25'))
"Find the value of 3 to the power of 4." → tool_code: print(calculator(expression='3 ** 4'))

Limitations

Small training dataset (4 examples)
CPU-only training due to CUDA compatibility issues
Limited to calculator tool only
May require additional fine-tuning for production use

Framework

This model was trained using the Tiny Tool Use library, which provides:

Multiple training methods (SFT, DPO, Teacher Mode)
Flexible data generation strategies
Integration with Berkeley Function-Calling Leaderboard
Support for various LLM architectures

Citation

bibtex

@misc{tinytooluse2024,
  title={Tiny Tool Use: Training Open-Source LLMs for Tool Usage},
  author={Bagel Labs},
  year={2024},
  url={https://github.com/bagel-org/bagel-RL}
}

License

MIT License - see the LICENSE file for details.

Alogotron/TinyToolUse-Qwen2-0.5B-Calculator

TinyToolUse-Qwen2-0.5B-Calculator

Model Description