| --- |
| 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)) |
| |
