| # Mistral Technical Interview QA (LoRA + RAG) |
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| ## 📌 Overview |
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| 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. |
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| 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 |
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| * Fine-tuned using **LoRA (Low-Rank Adaptation)** for memory-efficient training |
| * Supports **Retrieval-Augmented Generation (RAG)** |
| * Focused on Computer Science domains: |
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| * 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 |
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| * Model: `mistralai/Mistral-7B-Instruct-v0.2` |
| * Architecture: Decoder-only transformer |
| * Training method: Supervised fine-tuning with LoRA |
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| ## 📚 Dataset |
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| The dataset consists of approximately **2,000 technical question–answer pairs** covering multiple Computer Science domains. |
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| ### Dataset preparation: |
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| * Initial seed dataset manually curated |
| * Extended using synthetic augmentation |
| * Duplicate and semantic filtering applied |
| * Train / validation / test splits created |
| * Format: JSONL |
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| ⚠️ Note: Some data samples were generated using LLM-based augmentation and manually filtered for quality. |
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| ## ⚙️ Training Details |
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| ### Fine-tuning approach: |
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| * Parameter-efficient LoRA tuning |
| * Frozen base model weights |
| * Adapter-only training |
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| ### Configuration: |
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| * LoRA rank: 8 |
| * Learning rate: 1e-4 |
| * Epochs: 3 |
| * Sequence length: 384 |
| * Precision: FP16 |
| * Gradient accumulation used |
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| Training was performed on an NVIDIA RTX 3090 (24GB VRAM). |
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| ## 🔍 Retrieval-Augmented Generation (RAG) |
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| A semantic retriever was implemented using: |
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| * Sentence Transformers (`all-MiniLM-L6-v2`) |
| * FAISS vector database |
| * Top-k semantic search |
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| Retrieved context is injected into the prompt before generation. |
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| ## 📊 Evaluation |
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| The model was evaluated under four experimental settings: |
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| 1. Vanilla Mistral |
| 2. RAG + Vanilla |
| 3. Fine-tuned Mistral |
| 4. RAG + Fine-tuned |
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| Metrics used: |
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| * BLEU-4 |
| * ROUGE-L |
| * BERTScore |
| * Exact Match |
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| Results showed that: |
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| * 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 |
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| This model is designed for: |
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| * Interview preparation |
| * Educational tools |
| * Technical tutoring |
| * Research in retrieval-augmented systems |
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| It is not intended for: |
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| * Medical or legal decision-making |
| * Safety-critical systems |
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| ## ⚠️ Limitations |
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| * 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 |
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| 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 |
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| ### Load merged model: |
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| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
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| model = AutoModelForCausalLM.from_pretrained("yourusername/repo-name") |
| tokenizer = AutoTokenizer.from_pretrained("yourusername/repo-name") |
| ``` |
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| ### Load LoRA version: |
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| ```python |
| from transformers import AutoModelForCausalLM |
| from peft import PeftModel |
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| base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") |
| model = PeftModel.from_pretrained(base, "yourusername/repo-name") |
| ``` |
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| ## 📈 Future Work |
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| * Larger and more diverse dataset |
| * Multi-hop retrieval |
| * Improved retriever training |
| * Reinforcement learning for answer quality |
| * Human evaluation |
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| ## hookup |
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| ## 👨💻 Author |
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| Final-year Computer Science project focused on applied LLM systems, parameter-efficient fine-tuning, and retrieval-augmented generation. |
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| ## 📜 License |
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| Please refer to the base model license (Mistral AI). |
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