Upload README.md with huggingface_hub

README.md

@@ -1,55 +1,46 @@
----
-license: mit
-tags:
-  - tinyllama
-  - lora
-  - cli
-  - fine-tuning
-  - qna
-  - transformers
-  - peft
-library_name: transformers
-datasets:
-  - custom
-language: en
-model_type: causal-lm
+# 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.
+
 ---
 
-# 🔧 CLI LoRA-TinyLlama
+## 🔧 Project Overview
 
-A fine-tuned version of [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on a custom dataset of command-line Q&A, using **LoRA** (Low-Rank Adaptation). Built for fast, accurate help on common CLI topics.
+- **Base model**: [TinyLLaMA/TinyLLaMA-1.1B-Chat-v1.0](https://huggingface.co/TinyLLaMA/TinyLLaMA-1.1B-Chat-v1.0)
+- **Fine-tuning method**: QLoRA
+- **Library**: `transformers`, `peft`, `trl`, `datasets`
+- **Training file**: [`training.ipynb`](./training.ipynb)
 
 ---
 
-## 🧩 Base Model
+## 🧠 Objective
 
-- Model: `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
-- Fine-Tuning Method: [LoRA](https://arxiv.org/abs/2106.09685)
-- Libraries Used: `transformers`, `peft`, `datasets`, `accelerate`
+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.
 
 ---
 
-## 📚 Dataset
+## 📂 Files Included
 
-- Custom dataset with **150+ Q&A pairs** covering:
-  - `git`, `bash`, `grep`, `tar`, `venv`
-- Raw file: `cli_questions.json`
-- Tokenized version: `tokenized_dataset/`
+- `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
 
 ---
 
-## 🛠️ Training Configuration
+## 📊 Results
 
-```python
-from peft import LoraConfig
+| Metric       | Value         |
+|--------------|---------------|
+| Training Loss| *<your value>* |
+| Eval Accuracy| *<your value>* |
+| Epochs       | *<your value>* |
 
-base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+---
 
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.1,
-    bias="none",
-    task_type="CAUSAL_LM"
-)
+## 📎 Sample Q&A
 
+```bash
+Q: How to stash changes in Git?
+A: Use `git stash` to save your changes temporarily. Retrieve later using `git stash pop`.