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README.md

@@ -17,41 +17,68 @@ library_name: peft
 pipeline_tag: text-generation
 ---
 
-# CLI LoRA TinyLlama Fine-Tuning (Fenrir Internship)
+# 🔧 CLI LoRA TinyLLaMA Fine-Tuning (Fenrir Internship Project)
 
-🚀 This model is a LoRA fine-tuned version of **TinyLlama-1.1B-Chat** on a custom dataset of command-line (CLI) Q&A. It was developed as part of a 24-hour AI/ML internship task by Fenrir Security Pvt Ltd.
+🚀 This repository presents a **LoRA fine-tuned version of TinyLLaMA-1.1B-Chat** trained on a custom dataset of CLI Q&A. Developed as part of a 24-hour AI/ML internship task by **Fenrir Security Pvt Ltd**, this lightweight model functions as a domain-specific command-line assistant.
+
+---
 
 ## 📁 Dataset
-A carefully curated set of 200+ CLI Q&A pairs across tools like:
-- Git
-- Bash
-- `grep`, `tar`, `gzip`
-- `venv` and Python virtual environments
+
+A curated collection of 200+ real-world CLI Q&A pairs covering:
+
+- Git (branching, stash, merge, rebase)
+- Bash (variables, loops, file manipulation)
+- `grep`, `tar`, `gzip` (command syntax, flags)
+- Python environments (`venv`, pip)
+
+Stored in `cli_questions.json`.
+
+---
 
 ## ⚙️ Model Details
-- **Base Model:** `TinyLlama-1.1B-Chat-v1.0`
-- **Fine-Tuning Method:** QLoRA via PEFT
-- **Hardware:** Local system (CPU or limited GPU)
-- **Epochs:** 3 (with early stopping)
-- **Tokenizer:** Inherited from base model
-- **Parameter Efficient:** ~7MB adapter weights only
+
+| Field              | Value                                      |
+|-------------------|--------------------------------------------|
+| Base Model         | `TinyLlama/TinyLlama-1.1B-Chat-v1.0`       |
+| Fine-Tuning Method | QLoRA via `peft`                           |
+| Epochs             | 3 (with early stopping)                    |
+| Adapter Size       | ~7MB (LoRA weights only)                   |
+| Hardware           | Local CPU (low-resource)                   |
+| Tokenizer          | Inherited from base model                  |
+
+---
 
 ## 📊 Evaluation
-- Accuracy on known test Q&A: ~92%
-- Manual evaluation on unseen CLI inputs showed context-aware completions
-- Very low hallucination due to domain-specific training
+
+| Metric                     | Result         |
+|----------------------------|----------------|
+| Accuracy on Eval Set       | ~92%           |
+| Manual Review              | High relevance |
+| Hallucination Rate         | Very low       |
+| Inference Time (CPU)       | < 1s / query   |
+
+All results are stored in `eval_results.json`.
+
+---
 
 ## 🧠 Files Included
-- `adapter_model.safetensors`
-- `adapter_config.json`
-- `README.md` (you are here)
-- (Optional) `eval_logs.json`, `training.ipynb`
 
-## 📦 Usage
+- `adapter_model.safetensors` — fine-tuned LoRA weights
+- `adapter_config.json` — LoRA hyperparameters
+- `training.ipynb` — complete training notebook
+- `agent.py` — CLI interface to test the model
+- `cli_questions.json` — training dataset
+- `eval_results.json` — eval results
+- `requirements.txt` — dependencies
+
+---
+
+## 📦 Inference Example
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel, PeftConfig
+from peft import PeftModel
 
 base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
 tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")