Shlok307/ai_interview-lora

Gemma 3 Interview LoRA — 1B Instruct

This model is a QLoRA fine-tuned version of Gemma-3-1B-IT, trained on a curated dataset of 5,002 interview-style Q&A samples across:

• Artificial Intelligence (AI)
• General Programming
• Web Development

The goal is to enhance Gemma-3 into a technical interview assistant, capable of:

• Generating domain-specific interview questions
• Providing accurate, structured, exam-style answers
• Explaining concepts clearly and concisely
• Maintaining a professional and consistent interview tone

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Dataset

The model was fine-tuned on a dataset containing 5,002 samples with the fields:

Field	Description
domain	AI, General Programming, Web Development
question	Interview question from that domain
answer	Ground-truth, explanation-style answer

Each training row was converted into:

• Instruction:
  "Answer this <domain> interview question: <question>"
• Response:
  "<answer>"

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Usage Example

Python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Shlok307/ai_interview-lora"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16
)

prompt = [
    {"role": "user", "content": "Answer this AI interview question: What is backpropagation?"}
]

input_ids = tokenizer.apply_chat_template(
    prompt,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

output = model.generate(
    input_ids,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7
)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Citation

@model{gemma3_interview_lora,
  title={Gemma 3 Interview LoRA — 1B IT},
  author={Shlok Talhar},
  year={2025},
  url={https://huggingface.co/Shlok307/gemma3-interview-lora}
}