库名称：transformers
许可证：mit
基础模型：

• google/gemma-2b-it
  流水线标签：text-generation

模型ID的模型卡

此模型融合了peft微调模型，是一个独立模型。

模型详情

模型描述

这是🤗 transformers模型的模型卡，已推送至Hub。此模型卡为自动生成。

• 开发者：[Liching]
• 资助方：[hobby]
• 模型类型：[text-generation]
• 语言（NLP）：[En]
• 许可证：[MIT]
• 微调基础模型：[gemma-2b-it]

用途

gemma-2b-it无法用于工具调用，其响应方式类似于近期开发的Deepseek r1，这些限制在微调模型时已考虑在内。

偏见、风险与限制

由于模型仅微调了1个周期，因此容易产生偏见和错误。

如何开始使用模型

使用以下代码开始使用模型。

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig  
from transformers import pipeline  
import torch  
model_id = "langdai/gemma-2-2b-it-tool-think"  
model = AutoModelForCausalLM.from_pretrained(model_id,  
                                             device_map="cuda:0",  
                                            ) # 适用于GPU  
tokenizer = AutoTokenizer.from_pretrained(model_id)  
# model.to(torch.bfloat16)  
model.eval()  
generator = pipeline("text-generation", model= model, tokenizer= tokenizer)  

prompt="""<bos><start_of_turn>human  
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.Here are the available tools:<tools> [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}] </tools>Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:  
<tool_call>  
{tool_call}  
</tool_call>Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>  

Hi, I need to convert 500 INR to Euros. Can you help me with that?<end_of_turn><eos>  
<start_of_turn>model  
<think>"""  

output = generator([{"role": "user", "content": prompt}], max_new_tokens=512, return_full_text=False)[0]  

print(output)  

环境影响

碳排放量可使用机器学习影响计算器估算，详见Lacoste et al. (2019)。

• 硬件类型：[T4 24GPU]
• 使用时长：[4小时]