Update README.md

README.md

@@ -87,8 +87,139 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+import json
+import torch
+
+class ResponseHandler:
+    def __init__(self):
+        self.function_list = {
+            'get_weather': self.get_weather
+        }
+        self.tool_list = {
+            'get_weather': self.get_weather
+        }
+
+    def handle_function_call(self, response):
+        function_calls = self.extract_function_calls(response)
+        results = []
+        for func_call in function_calls:
+            function_name = func_call.get('name')
+            arguments = func_call.get('arguments')
+            result = self.execute_function(function_name, arguments)
+            print(f"Function call result: {result}")
+            results.append(result)
+        return results
+
+    def extract_function_calls(self, response):
+        return response.get('tool_calls', [])
+
+    def handle_content_filter_error(self, response):
+        error_message = "Content filtered due to policy restrictions. Please modify your request."
+        print(error_message)
+        return error_message
+
+    def handle_length_error(self, response):
+        truncated_conversation = self.truncate_conversation(response)
+        print("Truncated conversation to fit within the context window.")
+        return truncated_conversation
+
+    def truncate_conversation(self, response):
+        # Implement actual truncation logic here
+        return response
+
+    def execute_function(self, function_name, arguments):
+        func = self.function_list.get(function_name)
+        if func:
+            return func(**arguments)
+        else:
+            error_message = f"Function '{function_name}' not found."
+            print(error_message)
+            return error_message
+
+    def execute_tool(self, tool_name, arguments):
+        tool = self.tool_list.get(tool_name)
+        if tool:
+            return tool(**arguments)
+        else:
+            error_message = f"Tool '{tool_name}' not found."
+            print(error_message)
+            return error_message
+
+    def extract_tool_calls(self, response):
+        return response.get('tool_calls', [])
+
+    def handle_tool_call(self, response):
+        tool_calls = self.extract_tool_calls(response)
+        results = []
+        for tool_call in tool_calls:
+            tool_name = tool_call.get('name')
+            arguments = tool_call.get('arguments')
+            result = self.execute_tool(tool_name, arguments)
+            print(f"Tool call result: {result}")
+            results.append(result)
+        return results
+
+    def get_weather(self, location, unit):
+        # Dummy implementation for testing
+        return f"Weather in {location} is 75 degrees {unit}."
+
+def convert_to_xlam_tool(tools):
+    if isinstance(tools, dict):
+        return {
+            "name": tools["name"],
+            "description": tools["description"],
+            "parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
+        }
+    elif isinstance(tools, list):
+        return [convert_to_xlam_tool(tool) for tool in tools]
+    else:
+        return tools
+
+def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
+    prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
+    prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(tools)}\n[END OF AVAILABLE TOOLS]\n\n"
+    prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
+    prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
+    return prompt
+
+# Example usage
+openai_format_tools = [
+    {
+        "name": "get_weather",
+        "description": "Get the current weather for a location.",
+        "parameters": {
+            "properties": {
+                "location": {"type": "string"},
+                "unit": {"type": "string"}
+            }
+        }
+    }
+]
+
+task_instruction = "Provide the current weather."
+format_instruction = "Return the weather information in a readable format."
+query = "What is the weather in New York in fahrenheit?"
+
+xlam_format_tools = convert_to_xlam_tool(openai_format_tools)
+content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query)
+
+messages = [
+    {'role': 'user', 'content': content}
+]
+
+# Assuming a temporary model name and tokenizer
+model_name = "temp_model"
+tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', model_name)
+model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', model_name)
+
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+# tokenizer.eos_token_id is the id of <|EOT|> token
+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))
+
+
+##end
 
 #### Training Hyperparameters