---
library_name: mlc-llm
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- mlc-llm
- web-llm
---
# 🧠 LOHAMEIT 3.2: Next-Gen Conversational AI

Welcome to **LOHAMEIT 3.2**, a cutting-edge conversational AI model built using the **Llama-3.2-3B-Instruct** model in MLC format `q4f16_0`. This repository leverages state-of-the-art natural language processing (NLP) techniques for scalable, efficient, and real-time conversational agents, with a focus on **multi-turn conversation tracking** and **inference optimization**. 

This project is compatible with **MLC-LLM** and **WebLLM**, enabling easy integration into various platforms for dynamic interaction.

## 🚀 Model Overview

- **Base Model**: **Llama-3.2-3B-Instruct**.
- **Format**: Quantized to **q4f16_0**.
- **Project Compatibility**: 
  - [MLC-LLM](https://github.com/mlc-ai/mlc-llm)
  - [WebLLM](https://github.com/mlc-ai/web-llm)

This model is optimized for both **server-side** and **local inference** environments, enabling real-time, contextually aware conversational capabilities with low memory and hardware requirements.

### 🌟 Key Features

- **High Efficiency**: Supports **quantized** model execution, making it ideal for resource-constrained environments.
- **Multi-Turn Conversations**: Tracks conversational context for coherent, human-like responses.
- **Optimized for Deployment**: Ready to be used in web-based interfaces via **WebLLM** or command-line applications.

## 💡 Why LOHAMEIT 3.2?

This project provides a scalable, highly efficient NLP model suitable for a wide range of applications, from interactive bots to more complex AI-driven systems. **LOHAMEIT 3.2** offers:

- **Real-Time Inference**: Designed for immediate response times, ideal for live interactions.
- **Low Hardware Requirements**: Runs efficiently on CPUs and lower-end GPUs.
- **Flexible Integration**: Works across command-line, server, and web-based applications, supporting a variety of use cases.

## 🛠️ Installation and Usage

Before starting, ensure that the **MLC-LLM** library is installed on your system. For installation instructions, visit the [MLC-LLM Installation Documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).

### Example Commands:

#### Chat:

```bash
mlc_llm chat HF://mlc-ai/Llama-3.2-3B-Instruct-q4f16_0-MLC
```

#### REST Server:

```bash
mlc_llm serve HF://mlc-ai/Llama-3.2-3B-Instruct-q4f16_0-MLC
```

#### Python API:

```python
from mlc_llm import MLCEngine

# Create engine
model = "HF://mlc-ai/Llama-3.2-3B-Instruct-q4f16_0-MLC"
engine = MLCEngine(model)

# Run chat completion
for response in engine.chat.completions.create(
    messages=[{"role": "user", "content": "What is the meaning of life?"}],
    model=model,
    stream=True,
):
    for choice in response.choices:
        print(choice.delta.content, end="", flush=True)
print("\n")

engine.terminate()
```

## 📂 Project Structure

```plaintext
LOHAMEIT-3.2/
├── models/                # Model weights and configurations
├── src/                   # Source code for model interaction and API integration
├── scripts/               # Utility scripts for deployment
├── README.md              # This file
└── requirements.txt       # Python dependencies
```

## 🔧 Installation Guide

1. **Clone the repository**:
   ```bash
   git clone https://github.com/LOHAMEIT/lohameit-3.2.git
   cd lohameit-3.2
   ```

2. **Install Dependencies**:
   Install the required dependencies using the provided `requirements.txt` file.

3. **Run the Model**:
   Follow the instructions above to either run the chat interface or deploy as a REST server.

## 🧠 Future Enhancements

- **Quantization Improvements**: Additional support for **4-bit quantization** will further reduce resource usage while maintaining performance.
- **Enhanced Conversational Tracking**: Improved context management for even more natural conversations over extended sessions.

## 📧 Contact

For questions, feedback, or collaboration, feel free to reach out at **legionkoushik3@gmail.com**.

---
A heartfelt thank you to G. Akshitha from MLRIT College for her valuable contributions and support throughout this project. Your insights were instrumental in shaping key aspects of LOHAMEIT 3.2.

## LEGION-SRCS