Add an example for basic usage

README.md

@@ -28,6 +28,110 @@ More details can be found in our paper on arxiv:  [*ToolACE: Winning the Points
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66bf01f45bdd611f9a602087/WmyWOYtg_dbTgwQmvlqcz.jpeg)
 
 
+### Usage
+Here we provide a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate function calling with given functions.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "/home/huangxu/work/OpenLLMs/ToolACE-8B-zh-v2.2_1ep"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype='auto',
+    device_map='auto'
+)
+
+
+# You can modify the prompt for your task
+system_prompt = """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."
+
+# Availabel tools in JSON format (OpenAI-format)
+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))
+```
+
+Then you should be able to see the following output functional calls:
+```
+[sales_growth.calculate(company="XYZ", years=3), financial_ratios.interest_coverage(company_name="XYZ", years=3)]
+```
+
+
+
 ### Citation
 
 If you think ToolACE is useful in your work, please cite our paper: