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--- |
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base_model: meta-llama/Llama-3.2-1B-Instruct |
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datasets: argilla-warehouse/apigen-smollm-trl-FC |
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library_name: transformers |
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model_name: Llama-3.2-1B-Instruct-v2-FC |
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tags: |
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- generated_from_trainer |
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- trl |
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- sft |
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licence: license |
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--- |
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# Model Card for Llama-3.2-1B-Instruct-v2-FC |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [argilla-warehouse/apigen-smollm-trl-FC](https://huggingface.co/datasets/argilla-warehouse/apigen-smollm-trl-FC) dataset. |
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It has been trained using [TRL](https://github.com/huggingface/trl). |
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## Quick start |
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```python |
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import json |
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import re |
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from typing import Optional |
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from jinja2 import Template |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers.utils import get_json_schema |
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system_prompt = Template("""You are an expert in composing functions. You are given a question and a set of possible functions. |
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Based on the question, you will need to make one or more function/tool calls to achieve the purpose. |
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If none of the functions can be used, point it out and refuse to answer. |
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If the given question lacks the parameters required by the function, also point it out. |
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You have access to the following tools: |
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<tools>{{ tools }}</tools> |
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The output MUST strictly adhere to the following format, and NO other text MUST be included. |
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The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]'. |
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<tool_call>[ |
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{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}, |
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... (more tool calls as required) |
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]</tool_call>""") |
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def prepare_messages( |
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query: str, |
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tools: Optional[dict[str, any]] = None, |
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history: Optional[list[dict[str, str]]] = None |
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) -> list[dict[str, str]]: |
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"""Prepare the system and user messages for the given query and tools. |
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Args: |
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query: The query to be answered. |
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tools: The tools available to the user. Defaults to None, in which case if a |
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list without content will be passed to the model. |
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history: Exchange of messages, including the system_prompt from |
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the first query. Defaults to None, the first message in a conversation. |
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""" |
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if tools is None: |
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tools = [] |
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if history: |
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messages = history.copy() |
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messages.append({"role": "user", "content": query}) |
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else: |
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messages = [ |
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{"role": "system", "content": system_prompt.render(tools=json.dumps(tools))}, |
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{"role": "user", "content": query} |
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] |
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return messages |
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def parse_response(text: str) -> str | dict[str, any]: |
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"""Parses a response from the model, returning either the |
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parsed list with the tool calls parsed, or the |
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model thought or response if couldn't generate one. |
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Args: |
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text: Response from the model. |
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""" |
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pattern = r"<tool_call>(.*?)</tool_call>" |
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matches = re.findall(pattern, text, re.DOTALL) |
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if matches: |
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return json.loads(matches[0]) |
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return text |
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model_name_llama = "argilla-warehouse/Llama-3.2-1B-Instruct-v2-FC" |
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model = AutoModelForCausalLM.from_pretrained(model_name_llama, device_map="auto", torch_dtype="auto", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_name_llama) |
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from datetime import datetime |
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import random |
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def get_current_time() -> str: |
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"""Returns the current time in 24-hour format. |
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Returns: |
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str: Current time in HH:MM:SS format. |
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""" |
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return datetime.now().strftime("%H:%M:%S") |
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def get_random_number_between(min: int, max: int) -> int: |
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""" |
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Gets a random number between min and max. |
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Args: |
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min: The minimum number. |
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max: The maximum number. |
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Returns: |
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A random number between min and max. |
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""" |
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return random.randint(min, max) |
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tools = [get_json_schema(get_random_number_between), get_json_schema(get_current_time)] |
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toolbox = {"get_random_number_between": get_random_number_between, "get_current_time": get_current_time} |
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query = "Give me a number between 1 and 300" |
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query = "Can you give me the hour?" |
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messages = prepare_messages(query, tools=tools) |
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) |
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result = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) |
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tool_calls = parse_response(result) |
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# [{'name': 'get_random_number_between', 'arguments': {'min': 1, 'max': 300}} |
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# Get tool responses |
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tool_responses = [toolbox.get(tc["name"])(*tc["arguments"].values()) for tc in tool_calls] |
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# ['07:20:47'] |
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tool_response = get_random_number_between(*tool_calls[0].get("arguments").values()) |
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# 45 |
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``` |
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## Training procedure |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/plaguss/huggingface/runs/kac9pnd7) |
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This model was trained with SFT. |
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### Framework versions |
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- TRL: 0.12.0.dev0 |
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- Transformers: 4.46.0.dev0 |
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- Pytorch: 2.4.1 |
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- Datasets: 3.0.1 |
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- Tokenizers: 0.20.1 |
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## Citations |
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Cite TRL as: |
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```bibtex |
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@misc{vonwerra2022trl, |
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title = {{TRL: Transformer Reinforcement Learning}}, |
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, |
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year = 2020, |
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journal = {GitHub repository}, |
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publisher = {GitHub}, |
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howpublished = {\url{https://github.com/huggingface/trl}} |
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} |
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``` |
