# 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.

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## 🚀 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

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## 🧠 Base Model

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

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## 📚 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.

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## ⚙️ 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).

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## 🔍 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.

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## 📊 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

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## 🧪 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

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## ⚠️ 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

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## 🔐 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.

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## 📦 How to Use

### Load merged model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

### Load LoRA version:

```python
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")
```

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## 📈 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.

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## 📜 License

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