| # Qwen2-1.5B-Finetuned | |
| ## Training details | |
| datasets: | |
| <pre> | |
| - alpaca-gpt4_cleaned-qwen2-train.jsonl | |
| - alpaca-gpt4_cleaned-qwen2-val.jsonl | |
| - xlam-dataset-60k-qwen2-train.jsonl | |
| - xlam-dataset-60k-qwen2-val.jsonl | |
| * 9/1 train/eval ratio. | |
| </pre> | |
| ## Quickstart | |
| ### utils for user content. | |
| - replace [TRIPLE_BACKTICK] to ``` | |
| ```python | |
| xlam_system = ( | |
| "You are an AI assistant for function calling. " | |
| "For politically sensitive questions, security and privacy issues, " | |
| "and other non-computer science questions, you will refuse to answer" | |
| ) | |
| def to_xlam_tools(tools:list|dict): | |
| if not isinstance(tools, list): tools = [tools] | |
| xlam_tools = [] | |
| for tool in tools: | |
| assert isinstance(tool, dict) | |
| xlam_tools.append( { | |
| "name": tools["name"], | |
| "description": tools["description"], | |
| "parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()} | |
| }) | |
| return xlam_tools | |
| TASK_INSTRUCTION = '''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 functions can be used, point it out and refuse to answer. | |
| If the given question lacks the parameters required by the function, fill the parameters as None.''' | |
| FORMAT_INSTRUCTION = '''The output MUST strictly adhere to the following JSON format, and NO other text MUST be included. | |
| The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make tool_calls an empty list '[]'. | |
| [TRIPLE_BACKTICK] | |
| { "tool_calls": [ | |
| {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}, | |
| ... (more tool calls as required) | |
| ] } | |
| [TRIPLE_BACKTICK] | |
| ''' | |
| def get_prompt(xlam_tools:list|dict, query:str ): | |
| if not isinstance(xlam_tools, str): xlam_tools = json.dumps(xlam_tools) | |
| prompt = f"<instruction>\n{TASK_INSTRUCTION}\n</instruction>\n\n" | |
| prompt += f"<available tools>\n{xlam_tools}\n</available tools>\n\n" | |
| prompt += f"<tool format>\n{FORMAT_INSTRUCTION}\n</tool format>\n\n" | |
| prompt += f"<query>\n{query.strip()}\n<query>\n\n" | |
| return prompt | |
| ``` | |
| ### inference | |
| - replace [TRIPLE_BACKTICK] to ``` | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| user_msg = '''<instruction> | |
| 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 functions can be used, point it out and refuse to answer. | |
| If the given question lacks the parameters required by the function, fill the parameters as None. | |
| </instruction> | |
| <available tools> | |
| [{"name": "messages_from_telegram_channel", "description": "Fetches the last 10 messages or a specific message from a given public Telegram channel.", "parameters": {"channel": {"description": "The @username of the public Telegram channel.", "type": "str", "default": "telegram"}, "idmessage": {"description": "The ID of a specific message to retrieve. If not provided, the function will return the last 10 messages.", "type": "str, optional", "default": ""}}}, {"name": "shopify", "description": "Checks the availability of a given username on Shopify using the Toolbench RapidAPI.", "parameters": {"username": {"description": "The username to check for availability on Shopify.", "type": "str", "default": "username"}}}, {"name": "generate_a_face", "description": "Generates a face image using an AI service and returns the result as a JSON object or text. It utilizes the Toolbench RapidAPI service.", "parameters": {"ai": {"description": "The AI model identifier to be used for face generation.", "type": "str", "default": "1"}}}] | |
| </available tools> | |
| <tool format> | |
| The output MUST strictly adhere to the following JSON format, and NO other text MUST be included. | |
| The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make tool_calls an empty list '[]'. | |
| [TRIPLE_BACKTICK] | |
| { "tool_calls": [ | |
| {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}, | |
| ... (more tool calls as required) | |
| ] } | |
| [TRIPLE_BACKTICK] | |
| </tool format> | |
| <query> | |
| Check if the username 'ShopMaster123' is available on Shopify. | |
| </query>''' | |
| messages = [dict(role='user', content=user_msg)] | |
| label = { "tool_calls": [{"name": "shopify", "arguments": {"username": "ShopMaster123"}}] } | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "objects76/qwen2-xlam", trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "objects76/qwen2-xlam", trust_remote_code=True, | |
| torch_dtype="auto", | |
| device_map="cuda", | |
| ) | |
| model.config.use_cache = True | |
| model.eval() | |
| input_ids = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=True, | |
| add_generation_prompt=True, | |
| max_length=tokenizer.model_max_length, | |
| padding=False, truncation=True, | |
| return_tensors='pt', | |
| ).to(model.device) | |
| outputs = model.generate( | |
| input_ids = input_ids, # attention_mask=attention_mask, | |
| max_new_tokens=1024, | |
| eos_token_id=tokenizer.eos_token_id, | |
| pad_token_id=tokenizer.pad_token_id, | |
| # do_sample=True, temperature=0.01, top_p= 0.01, | |
| use_cache=True) | |
| response = tokenizer.decode(outputs[0, input_ids.shape[-1]:], skip_special_tokens=True) | |
| print('response=', response) | |
| print('label=', label) | |
| ``` | |
| ### general instruction-following(few-shot) | |
| ```python | |
| system = ('Your task is to extract specific information from the given text.' | |
| ' Please provide the requested information in the format shown in the examples below.' | |
| ) | |
| fewshot_example = '''\ | |
| Example 1: | |
| Text: John Smith is a 35-year-old software engineer from New York. He has been working at TechCorp for 5 years. | |
| Name: John Smith | |
| Age: 35 | |
| Occupation: Software Engineer | |
| Location: New York | |
| Company: TechCorp | |
| Years of Experience: 5 | |
| Example 2: | |
| Text: Sarah Johnson, a 28-year-old marketing specialist, recently moved to San Francisco to join StartupX as their new Head of Marketing. | |
| Name: Sarah Johnson | |
| Age: 28 | |
| Occupation: Marketing Specialist | |
| Location: San Francisco | |
| Company: StartupX | |
| Position: Head of Marketing | |
| Now, extract the information from the following text: | |
| Text: Michael Brown, 42, is a senior data scientist at DataInc in Chicago. He has been in the field for over a decade and specializes in machine learning algorithms. | |
| ''' | |
| answer_from_gpt = '''\ | |
| Name: Michael Brown | |
| Age: 42 | |
| Occupation: Senior Data Scientist | |
| Location: Chicago | |
| Company: DataInc | |
| Years of Experience: Over a decade | |
| Specialization: Machine Learning Algorithms | |
| ''' | |
| messages = [ | |
| {"role": "system", "content": system.strip()}, | |
| {"role": "user", "content": fewshot_example.strip()}, | |
| ] | |
| ``` | |