CLI-LoRA-TinyLLaMA

Fine-tuned TinyLLaMA-1.1B model using QLoRA on a custom CLI Q&A dataset (Git, Bash, tar/gzip, grep, venv) for the Fenrir Security Internship Task.

🔧 Project Overview

Base model: TinyLLaMA/TinyLLaMA-1.1B-Chat-v1.0
Fine-tuning method: QLoRA
Library: transformers, peft, trl, datasets
Training file: training.ipynb

🧠 Objective

To fine-tune a small language model on real-world command-line Q&A data (no LLM-generated text) and build a command-line chatbot agent capable of providing accurate CLI support.

📂 Files Included

training.ipynb: Full training notebook (cleaned, token-free)
adapter_config.json: LoRA adapter configuration
adapter_model.safetensors: Trained adapter weights
eval_logs.json: Sample evaluation results (accuracy, loss, etc.)
README.md: This file

📊 Results

Metric	Value
Training Loss
Eval Accuracy
Epochs

📎 Sample Q&A

Q: How to stash changes in Git?
A: Use `git stash` to save your changes temporarily. Retrieve later using `git stash pop`.