qwen2.5-3b-interview-feedback-lora

Qwen2.5-3B-Instruct Fine-tuned for AI Interview Feedback Generation

This is a LoRA (Low-Rank Adaptation) fine-tuned version of Qwen/Qwen2.5-3B-Instruct specifically optimized for generating constructive, actionable interview feedback. The model analyzes interview responses and provides detailed feedback on communication skills, technical accuracy, and areas for improvement.

Model Details

• Base Model: Qwen/Qwen2.5-3B-Instruct
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Data: 1,550 curated interview question-answer-feedback samples
• Framework: PEFT 0.17.1
• Use Case: AI-powered mock interview systems, interview preparation platforms, automated feedback generation
• License: Apache 2.0 (inherited from base model)

Intended Use

This model is designed for:

• Mock Interview Platforms: Providing automated, constructive feedback to interview candidates
• Interview Preparation Tools: Helping job seekers practice and improve their interview responses
• Career Coaching Applications: Assisting career coaches in evaluating candidate responses
• Educational Tools: Training students on professional communication and interview skills

Key Features

Constructive Feedback: Provides actionable suggestions for improvement
Structured Analysis: Evaluates communication, technical accuracy, and completeness
Encouraging Tone: Balances critique with positive reinforcement
Domain-Specific: Optimized for various interview types (technical, behavioral, HR)

Usage

Installation

pip install transformers peft torch

Loading the Model

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

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-3B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "Charishma047/qwen2.5-3b-interview-feedback-lora"
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    "Charishma047/qwen2.5-3b-interview-feedback-lora"
)

Generating Interview Feedback

def generate_feedback(question, answer):
    """
    Generate interview feedback for a given question-answer pair.
    
    Args:
        question (str): The interview question asked
        answer (str): The candidate's response
    
    Returns:
        str: Generated feedback
    """
    prompt = f"""Given the following interview question and candidate's answer, provide constructive feedback:

Question: {question}

Answer: {answer}

Feedback:"""

    messages = [
        {"role": "system", "content": "You are an experienced interview coach providing constructive feedback."},
        {"role": "user", "content": prompt}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    inputs = tokenizer([text], return_tensors="pt").to(model.device)
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract only the feedback portion
    feedback = response.split("Feedback:")[-1].strip()
    
    return feedback

# Example usage
question = "Tell me about a time you handled a difficult team member."
answer = "I once worked with someone who didn't meet deadlines. I talked to them and helped them organize their work better."

feedback = generate_feedback(question, answer)
print(feedback)

Example Output

Question: "What are your strengths and weaknesses?"

Candidate Answer: "My strength is that I work hard. My weakness is that I'm a perfectionist."

Generated Feedback:

Your response demonstrates self-awareness, which is positive. However, consider enhancing 
your answer with specific examples. Instead of saying "I work hard," describe a situation 
where your work ethic led to a successful outcome. For the weakness section, the 
"perfectionist" answer is quite common - try to share a genuine area for improvement and, 
importantly, explain the steps you're taking to address it. This shows growth mindset and 
professional maturity. Also, quantify your achievements when possible to make your 
strengths more tangible.

Training Details

Training Data

• Dataset Size: 1,550 interview samples
• Data Composition:
  • Technical interview questions and responses
  • Behavioral interview scenarios
  • HR screening questions
  • Role-specific questions across various industries
• Feedback Quality: Each sample includes expert-crafted constructive feedback

Training Configuration

The model was fine-tuned using LoRA with the following approach:

• Target Modules: Query and Value projection layers
• Training Framework: PEFT (Parameter-Efficient Fine-Tuning)
• Base Model: Qwen2.5-3B-Instruct
• Optimization: Focused on maintaining base model capabilities while specializing in feedback generation

Evaluation

The model was evaluated using multiple methodologies:

1. Perplexity Measurement: Assessed language modeling quality
2. LLM-as-Judge Evaluation: Used advanced models to evaluate feedback quality
3. Human Evaluation: Domain experts reviewed generated feedback for:
   • Constructiveness
   • Actionability
   • Appropriateness
   • Helpfulness

Results demonstrated significant improvement in feedback quality compared to the base model, with particular strength in providing actionable suggestions and maintaining an encouraging tone.

Limitations

• Language: Primarily optimized for English language interviews
• Domain Coverage: Best performance on common interview scenarios; may be less effective for highly specialized technical domains
• Context Window: Inherits the base model's context limitations
• Cultural Context: Feedback style reflects professional standards common in Western business culture
• Not a Replacement: Should be used as a supplementary tool, not a replacement for human career coaches or interview experts

Bias and Ethical Considerations

• The model has been trained to provide constructive, unbiased feedback regardless of the content
• Users should be aware that AI-generated feedback may not capture all nuances of human communication
• This model should be used as a learning aid, and candidates should seek diverse perspectives
• Feedback should be interpreted in the context of the specific role and industry

Model Architecture

This is a LoRA adapter that modifies the Qwen2.5-3B-Instruct base model. LoRA adds trainable rank decomposition matrices to specific layers while keeping the base model frozen, enabling efficient fine-tuning with minimal additional parameters.

Advantages of this approach:

• Efficient training and inference
• Preserves base model's general capabilities
• Easy to share and deploy (only adapter weights needed)
• Can be combined with other adapters

Citation

If you use this model in your research or application, please cite:

@misc{qwen2.5-3b-interview-feedback-lora,
  author = {Charishma Tamarana},
  title = {Qwen2.5-3B Fine-tuned for Interview Feedback Generation},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Charishma047/qwen2.5-3b-interview-feedback-lora}
}

Also cite the base model:

@article{qwen2.5,
  title={Qwen2.5: A Party of Foundation Models},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024}
}

Acknowledgments

• Base Model: Qwen Team for the excellent Qwen2.5-3B-Instruct foundation model
• Framework: HuggingFace for transformers and PEFT libraries
• Training Infrastructure: Google Colab for providing accessible compute resources

Contact

Developer: Charishma Tamarana
HuggingFace: @Charishma047

For questions, issues, or collaboration opportunities, please open an issue in the model repository.

License

This model inherits the Apache 2.0 license from Qwen2.5-3B-Instruct base model. See LICENSE for details.

---

Model Version: 1.0
Last Updated: November 2025
Status: Production Ready