---
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
tags:
- function-calling
- tool-use
- llama
- bfcl
---
# watt-tool-8B

watt-tool-8B is a fine-tuned language model based on LLaMa-3.1-8B-Instruct, optimized for tool usage and multi-turn dialogue. It achieves state-of-the-art performance on the Berkeley Function-Calling Leaderboard (BFCL).

## Model Description

This model is specifically designed to excel at complex tool usage scenarios that require multi-turn interactions. By leveraging a carefully curated and optimized dataset, watt-tool-8B demonstrates superior capabilities in understanding user requests, selecting appropriate tools, and effectively utilizing them across multiple turns of conversation.

## Key Features

*   **Enhanced Tool Usage:** Fine-tuned for precise and efficient tool selection and execution.
*   **Multi-Turn Dialogue:** Optimized for maintaining context and effectively utilizing tools across multiple turns of conversation, enabling more complex task completion.
*   **State-of-the-Art Performance:** Achieves top performance on the BFCL, demonstrating its capabilities in function calling and tool usage.

## Training Methodology

watt-tool-8B is trained using supervised fine-tuning on a specialized dataset designed for tool usage and multi-turn dialogue. We use CoT techniques to synthesize high-quality multi-turn dialogue data.

The training process is inspired by the principles outlined in the paper: ["Direct Multi-Turn Preference Optimization for Language Agents"](https://arxiv.org/abs/2406.14868).
We use SFT and DMPO to further enhance the model's performance in multi-turn agent tasks.

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "watt-ai/watt-tool-8B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map="auto")

# Example usage (adapt as needed for your specific tool usage scenario)
system_prompt = "You are an AI assistant for function calling. 
For politically sensitive questions, security and privacy issues, you will refuse to answer.\n"
user_prompt = "Please find the weather in London and then book a table at a restaurant nearby."
tools = [
    {
        "name": "financial_ratios.interest_coverage", "description": "Calculate a company's interest coverage ratio given the company name and duration",
        "arguments": {
            "type": "dict",
            "properties": {
                "company_name": {
                    "type": "string",
                    "description": "The name of the company."
                }, 
                "years": {
                    "type": "integer",
                    "description": "Number of past years to calculate the ratio."
                }
            }, 
            "required": ["company_name", "years"]
        }
    },
    {
        "name": "sales_growth.calculate",
        "description": "Calculate a company's sales growth rate given the company name and duration",
        "arguments": {
            "type": "dict", 
            "properties": {
                "company": {
                    "type": "string",
                    "description": "The company that you want to get the sales growth rate for."
                }, 
                "years": {
                    "type": "integer",
                    "description": "Number of past years for which to calculate the sales growth rate."
                }
            }, 
            "required": ["company", "years"]
        }
    },
    {
        "name": "weather_forecast",
        "description": "Retrieve a weather forecast for a specific location and time frame.",
        "arguments": {
            "type": "dict",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city that you want to get the weather for."
                }, 
                "days": {
                    "type": "integer",
                    "description": "Number of days for the forecast."
                }
            },
            "required": ["location", "days"]
        }
    }
]

messages = [
    {'role': 'system', 'content': system_prompt.format(functions=tools)},
    {'role': 'user', 'content': query}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))