---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
license: apache-2.0
tags:
- conversational-ai
- chatbot
- lora
- qlora
- peft
- nlp
- openassistant
- fine-tuning
---

# Model Card for Lumo

**Lumo** is a lightweight conversational AI adapter fine-tuned using **QLoRA** on top of the open-source **TinyLLaMA 1.1B Chat** base model. It is designed for **learning, experimentation, and student projects**, with a focus on accessibility and transparency.

**Note:** This repository contains **only the LoRA adapter weights**, not the base model.

## Model Details

### Model Description

- **Developed by:** Aditya Verma
- **Model type:** Conversational Language Model (LoRA Adapter)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)

### Model Sources

- **Repository:** Adi362/Lumo
- **Base Model:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
- **Training Framework:** Hugging Face Transformers + PEFT

## Uses

### Direct Use

This model is intended for:
- Local conversational chatbots
- Educational AI experiments
- Student projects involving LLMs
- Learning how LoRA fine-tuning works
- Prototyping lightweight AI assistants

*The adapter must be loaded together with the base TinyLLaMA model.*

### Downstream Use

The adapter can be:
- Combined with other LoRA adapters
- Further fine-tuned on domain-specific datasets
- Integrated into APIs or applications
- Used as a base for research or experimentation

### Out-of-Scope Use

This model is **not intended** for:
- High-stakes decision making
- Medical, legal, or financial advice
- Production-grade commercial systems without further evaluation
- Safety-critical applications

## Bias, Risks, and Limitations

- **Bias:** The model may reflect biases present in the training data (OpenAssistant).
- **Hallucinations:** It can produce incorrect or misleading information.
- **Factuality:** Responses should not be treated as factual guarantees.
- **Performance:** Capabilities are limited by the small size (1.1B parameters) and scope of the base model.

### Recommendations

Users (both direct and downstream) should:
- Validate outputs independently.
- Avoid using the model for critical applications.
- Apply additional safety layers when deploying in public-facing systems.

## How to Get Started with the Model

Use the code below to load the base model and the Lumo adapter.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
LORA_MODEL = "Adi362/Lumo"

# 1. Load Base Model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float32,
    device_map=None
)

# 2. Load Lumo Adapter
model = PeftModel.from_pretrained(model, LORA_MODEL)
model.eval()

## Training Details

### Training Data

The model was trained on a filtered subset of the **OpenAssistant Conversations** dataset.

- **Dataset Name:** OpenAssistant Conversations (English, filtered)
- **Data Type:** Human–assistant dialogue pairs
- **Content:** Diverse conversational topics, instructions, and queries.

### Training Procedure

#### Preprocessing

The dataset underwent the following preprocessing steps:
- **Filtering:** Retained only English language conversations.
- **Formatting:** Constructed user–assistant pairs and formatted them using standard chat-style prompts to suit the base model's expectations.

#### Training Hyperparameters

- **Training regime:** **QLoRA** (4-bit base model quantization + LoRA adapters)
- **Precision:** 4-bit (nf4)
- **Optimizer:** Paged AdamW (8-bit)
- **Learning Rate:** 2e-4
- **Epochs:** 2
- **Batch Size:** 1 (with gradient accumulation)
- **Trainable Parameters:** ~1.1% of total model parameters

#### Speeds, Sizes, Times

- **Training Time:** ~4–5 hours on a single GPU.

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

No formal benchmark datasets were used for this version. The model is intended for educational purposes and low-stakes experimentation.

#### Factors

Evaluation focused on:
- **Language:** English only.
- **Domain:** General conversational ability and basic instruction following.

#### Metrics

Evaluation was qualitative, focusing on:
1.  **Coherence:** Ability to maintain a conversation flow.
2.  **Instruction Following:** Ability to execute simple prompts.
3.  **Identity:** Correctly identifying itself as an AI assistant.

### Results

The model demonstrates basic conversational fluency and can handle simple instructions. As a lightweight adapter (~1.1B parameters), it may struggle with complex reasoning or highly specific factual queries compared to larger models.

## Model Examination

*Not applicable for this version.*

## Environmental Impact

Carbon emissions were estimated based on the training hardware and duration.

- **Hardware Type:** NVIDIA Tesla T4 (Cloud GPU)
- **Hours used:** ~4-5 hours
- **Cloud Provider:** Google Colab
- **Compute Region:** Unknown (Colab default)
- **Carbon Emitted:** Negligible (Low-scale training not formally measured).

## Technical Specifications

### Model Architecture and Objective

- **Base Architecture:** Transformer (TinyLLaMA 1.1B)
- **Adaptation Method:** Low-Rank Adaptation (LoRA)
- **Objective:** Causal Language Modeling (Next-token prediction)

### Compute Infrastructure

#### Hardware

- **GPU:** Single NVIDIA Tesla T4 (16GB VRAM)

#### Software

- **Orchestration:** Google Colab
- **Libraries:** Hugging Face Transformers, PEFT, PyTorch, BitsAndBytes

## Citation

**BibTeX:**

```bibtex
@misc{verma2025lumo,
  author = {Verma, Aditya},
  title = {Lumo: A LoRA-fine-tuned conversational adapter based on TinyLLaMA},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/Adi362/Lumo](https://huggingface.co/Adi362/Lumo)}}
}

**APA:**

> Verma, A. (2025). *Lumo: A LoRA-fine-tuned conversational adapter based on TinyLLaMA* [Large Language Model]. Hugging Face. https://huggingface.co/Adi362/Lumo

## Glossary

* **LoRA (Low-Rank Adaptation):** A parameter-efficient fine-tuning technique that freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer, significantly reducing the number of trainable parameters.
* **QLoRA (Quantized LoRA):** An efficient fine-tuning approach that quantizes the base model to 4-bit precision (reducing memory usage) while keeping the LoRA adapters in higher precision for training.
* **PEFT (Parameter-Efficient Fine-Tuning):** A library by Hugging Face that enables efficient adaptation of pre-trained language models to various downstream applications without fine-tuning all the model's parameters.
* **TinyLlama:** A compact 1.1 billion parameter language model pre-trained on around 1 trillion tokens, designed to be run on edge devices and consumer hardware.

## More Information

This model was created as a student project to demonstrate the feasibility of fine-tuning valid conversational assistants on consumer-grade hardware (Google Colab free tier) using the QLoRA technique.

## Model Card Authors

Aditya Verma

## Model Card Contact

For bugs, feature requests, or general feedback, please open an issue on the [Project GitHub Repository](https://github.com/Adi362/Lumo) or the Hugging Face Community tab.

### Framework versions

- PEFT 0.8.2