AtulDeshpande/mistral-interview-assistant

Mistral Technical Interview QA (LoRA + RAG)

📌 Overview

This model is a fine-tuned version of Mistral-7B-Instruct designed to answer technical interview questions in core Computer Science domains. It is trained on a curated dataset of technical question–answer pairs and evaluated under multiple experimental setups including vanilla LLM, fine-tuning, Retrieval-Augmented Generation (RAG), and hybrid approaches.

The goal of this project is to investigate the effectiveness of parameter-efficient fine-tuning and retrieval augmentation for improving technical question answering in low-resource settings.

---

🚀 Key Features

• Fine-tuned using LoRA (Low-Rank Adaptation) for memory-efficient training

• Supports Retrieval-Augmented Generation (RAG)

• Focused on Computer Science domains:

  • Data Structures and Algorithms
  • Operating Systems
  • Computer Networks
  • Databases
  • OOP and Core CS fundamentals

• Lightweight adapter weights for fast deployment

• Evaluated using BLEU, ROUGE, BERTScore, and Exact Match

---

🧠 Base Model

• Model: mistralai/Mistral-7B-Instruct-v0.2
• Architecture: Decoder-only transformer
• Training method: Supervised fine-tuning with LoRA

---

📚 Dataset

The dataset consists of approximately 2,000 technical question–answer pairs covering multiple Computer Science domains.

Dataset preparation:

• Initial seed dataset manually curated
• Extended using synthetic augmentation
• Duplicate and semantic filtering applied
• Train / validation / test splits created
• Format: JSONL

⚠️ Note: Some data samples were generated using LLM-based augmentation and manually filtered for quality.

---

⚙️ Training Details

Fine-tuning approach:

• Parameter-efficient LoRA tuning
• Frozen base model weights
• Adapter-only training

Configuration:

• LoRA rank: 8
• Learning rate: 1e-4
• Epochs: 3
• Sequence length: 384
• Precision: FP16
• Gradient accumulation used

Training was performed on an NVIDIA RTX 3090 (24GB VRAM).

---

🔍 Retrieval-Augmented Generation (RAG)

A semantic retriever was implemented using:

• Sentence Transformers (all-MiniLM-L6-v2)
• FAISS vector database
• Top-k semantic search

Retrieved context is injected into the prompt before generation.

---

📊 Evaluation

The model was evaluated under four experimental settings:

1. Vanilla Mistral
2. RAG + Vanilla
3. Fine-tuned Mistral
4. RAG + Fine-tuned

Metrics used:

• BLEU-4
• ROUGE-L
• BERTScore
• Exact Match

Results showed that:

• Fine-tuning improves answer quality and domain specificity
• RAG improves factual grounding
• The hybrid approach provides the best performance

---

🧪 Intended Use

This model is designed for:

• Interview preparation
• Educational tools
• Technical tutoring
• Research in retrieval-augmented systems

It is not intended for:

• Medical or legal decision-making
• Safety-critical systems

---

⚠️ Limitations

• Dataset size is relatively small
• Synthetic data may introduce bias
• Performance may vary for unseen or highly specialized domains
• Retrieval quality depends on the vector database

---

🔐 Ethical Considerations

The dataset does not contain personally identifiable or sensitive information. The model may generate incorrect or outdated information and should be used with human oversight.

---

📦 How to Use

Load merged model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("yourusername/repo-name")
tokenizer = AutoTokenizer.from_pretrained("yourusername/repo-name")

Load LoRA version:

from transformers import AutoModelForCausalLM
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = PeftModel.from_pretrained(base, "yourusername/repo-name")

---

📈 Future Work

• Larger and more diverse dataset
• Multi-hop retrieval
• Improved retriever training
• Reinforcement learning for answer quality
• Human evaluation

hookup

👨‍💻 Author

Final-year Computer Science project focused on applied LLM systems, parameter-efficient fine-tuning, and retrieval-augmented generation.

---

📜 License

Please refer to the base model license (Mistral AI).