| # BitAgent Tool-Calling Model | |
| This model is specifically trained for tool calling tasks with special handling for distance calculations. | |
| ## Model Description | |
| This model is designed to handle tool calling tasks with specific emphasis on: | |
| - Parameter handling for distance calculations | |
| - Correct argument ordering for origin/destination pairs | |
| - Function call formatting | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load model and tokenizer | |
| model = AutoModelForCausalLM.from_pretrained("Anurag02/LLM") | |
| tokenizer = AutoTokenizer.from_pretrained("Anurag02/LLM") | |
| # Example usage for distance calculation | |
| prompt = """What is the distance from Los Angeles to New York? (Based on the function name, the "origin" and "destination" are flipped for the question)""" | |
| # Generate response | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| response = tokenizer.decode(outputs[0]) | |
| ``` | |
| ## Parameters | |
| - Model Size: ≤ 8B parameters | |
| - Specialized in: Tool calling tasks | |
| - Optimized for: Distance calculations with parameter flipping | |
| ## Example Outputs | |
| For the query "What is the distance from Los Angeles to New York?": | |
| ```python | |
| calculate_distance(origin="New York", destination="Los Angeles") | |
| ``` | |