Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-70B-Instruct
+tags:
+- function-calling
+- tool-use
+- llama
+- bfcl
+---
+# watt-tool-8B
+
+watt-tool-70B is a fine-tuned language model based on LLaMa-3.1-70B-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.
+*   **Based on LLaMa-3.1-70B-Instruct:** Inherits the strong language understanding and generation capabilities of the base model.
+
+## Training Methodology
+
+watt-tool-70B 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-70B"
+
+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)
+"""You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
+If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out.
+You should only return the function call in tools call sections.
+
+If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
+You SHOULD NOT include any other text in the response.
+Here is a list of functions in JSON format that you can invoke.\n{functions}\n
+"""
+# User query
+query = "Find me the sales growth rate for company XYZ for the last 3 years and also the interest coverage ratio for the same duration."
+
+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))