Gemma 2 2b It Tool Think

🚀 Model Card for Model ID

This model is a standalone one merged with a PEFT fine - tuned model.

🚀 Quick Start

Use the code below to get started with the model.

Basic Usage

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",
                                            ) # For GPU
tokenizer = AutoTokenizer.from_pretrained(model_id)
# model.to(torch.bfloat16)
model.eval()
generator = pipeline("text-generation", model= model, tokenizer= tokenizer)

Advanced Usage

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)

✨ Features

• This model is merged with a PEFT fine - tuned model and is a standalone model.
• gemma - 2b - it cannot be used for tool call and responds with reasoning like the recent developed Deepseek r1. These limitations are considered during fine - tuning.

📚 Documentation

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Uses

gemma - 2b - it cannot be used for tool call and responds with reasoning like the recent developed Deepseek r1. These limitations are taken into consideration by fine - tuning the model.

Bias, Risks, and Limitations

The Model is finetuned for 1 epoch, making it prone to bias and errors.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

📄 License

This model is licensed under the MIT license.