|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- qlora |
|
|
- tinyllama |
|
|
- cli |
|
|
- command-line |
|
|
- fine-tuning |
|
|
- low-resource |
|
|
- internship |
|
|
- fenrir |
|
|
model_type: TinyLlamaForCausalLM |
|
|
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
|
|
datasets: |
|
|
- custom-cli-qa |
|
|
library_name: peft |
|
|
pipeline_tag: text-generation |
|
|
--- |
|
|
|
|
|
# 🔧 CLI LoRA TinyLLaMA Fine-Tuning (Fenrir Internship Project) |
|
|
|
|
|
🚀 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 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 |
|
|
|
|
|
| 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 |
|
|
|
|
|
| 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` — 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 |
|
|
|
|
|
base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") |
|
|
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") |
|
|
|
|
|
peft_model = PeftModel.from_pretrained(base_model, "Harish2002/cli-lora-tinyllama") |
|
|
peft_model.eval() |
|
|
|
|
|
prompt = "How do I initialize a new Git repository?" |
|
|
inputs = tokenizer(prompt, return_tensors="pt") |
|
|
outputs = peft_model.generate(**inputs, max_new_tokens=64) |
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
|
|
|
