---
title: ByteBot
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "5.34.2"
python_version: "3.11"
app_file: app.py
pinned: false
---


# 🤖 Qwen Alpaca GGUF

A fine tuned **Qwen2.5-0.5B-Instruct** language model trained on the **tatsu-lab/alpaca** instruction dataset. The model was fine tuned using **Unsloth**, quantized to **GGUF (Q4_K_M)**, and can be run locally with **Ollama** or **llama.cpp**.

---

## 🚀 Model Details

| Property | Value |
|----------|-------|
| Base Model | Qwen2.5-0.5B-Instruct |
| Fine Tuning | LoRA |
| Framework | Unsloth |
| Dataset | tatsu-lab/alpaca |
| Format | GGUF |
| Quantization | Q4_K_M |
| Inference | Ollama, llama.cpp |
| Language | English |

---

## ✨ Features

- Instruction following chatbot
- Lightweight 0.5B parameter model
- GGUF format for efficient CPU inference
- Optimized using Q4_K_M quantization
- Compatible with Ollama
- Compatible with llama.cpp
- Easy local deployment

---

## 📦 Model File

```
Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
```

---

# 🛠 Fine Tuning Pipeline

```
Base Model
        │
        ▼
Qwen2.5-0.5B-Instruct
        │
        ▼
Alpaca Instruction Dataset
        │
        ▼
LoRA Fine Tuning
        │
        ▼
Unsloth
        │
        ▼
Merge LoRA Adapters
        │
        ▼
GGUF Conversion
        │
        ▼
Q4_K_M Quantization
        │
        ▼
Inference using Ollama / llama.cpp
```

---

# 📚 Dataset

This model was fine tuned using the **tatsu-lab/alpaca** instruction dataset.

The dataset contains thousands of instruction and response pairs designed to improve instruction following ability.

---

# ⚡ Run with Ollama

Create a Modelfile

```text
FROM ./Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
```

Create the model

```bash
ollama create fineqwen -f Modelfile
```

Run the model

```bash
ollama run fineqwen
```

---

# 🖥 Example Terminal

```text
$ ollama create fineqwen -f Modelfile

transferring model...
creating new layer...
writing manifest...
success

$ ollama run fineqwen

>>> Explain Transformers in simple words.

Transformers are neural networks designed to process sequences using
self-attention, allowing them to understand relationships between words
efficiently.
```

---

# 🦙 Run using llama.cpp

```bash
llama-cli \
-hf ciphermosaic/qwen-alpaca-gguf \
--jinja
```

or

```bash
llama-cli \
-m Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
```

---

# 🌐 Hugging Face Space

Interactive chatbot available on Hugging Face Spaces.

---

# 📊 Quantization

This model uses

```
Q4_K_M
```

Benefits

- Smaller model size
- Faster inference
- Lower RAM usage
- Minimal quality loss

---

# 🧰 Tech Stack

- Python
- Hugging Face Transformers
- Unsloth
- PEFT (LoRA)
- GGUF
- llama.cpp
- Ollama
- Hugging Face Hub
- Gradio

---

# 📁 Repository Structure

```
.
├── Modelfile
├── Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
├── README.md
├── config.json
```

---

# 👨‍💻 Author

**CipherMosaic**

GitHub: https://github.com/CipherMosaic

Hugging Face: https://huggingface.co/ciphermosaic

---

## ⭐ If you found this project useful, consider giving it a star!