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alifarooq77/
aqi-model

Name: aqi-model
Availability: InStock

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license: gemma

Gemma 2B LoRA - AQI Fine-tuned

This model is a LoRA adapter fine-tuned on the google/gemma-2b base model for air quality index (AQI) related question answering.

🧠 Base Model

google/gemma-2b

Note: This repository contains only the LoRA adapter weights. To use the model, you must load it on top of google/gemma-2b.

License

Gemma is provided under and subject to the Gemma Terms of Use.
See the included NOTICE file.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load the base Gemma 2B model
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")

# Load tokenizer and LoRA adapter weights from this repo
tokenizer = AutoTokenizer.from_pretrained("alifarooq77/aqi-model")
model = PeftModel.from_pretrained(base_model, "alifarooq77/aqi-model")

model.eval()

# Prepare prompt format used during training
def prepare_prompt(question: str) -> str:
    return f"### Instruction:\n{question}\n\n### Answer:\n"

# Generate response function
def generate_response(question: str) -> str:
    prompt = prepare_prompt(question)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    outputs = model.generate(
        **inputs,
        max_new_tokens=120,
        eos_token_id=tokenizer.eos_token_id,
        do_sample=True,
        temperature=0.95,
        top_p=0.9,
        repetition_penalty=1.15,
    )
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    answer = generated_text.split("### Answer:\n")[-1].strip()
    return answer

# Example usage
question = "What should be done to lower AQI?"
print("Question:", question)
print("Answer:", generate_response(question))