---
license: llama3.1
library_name: transformers
datasets:
- lmsys/lmsys-chat-1m
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text2text-generation
---

# 0x Mini

## Overview
0x Mini is a state-of-the-art language model developed by Ozone AI, designed to deliver high-quality text generation capabilities while maintaining a compact and efficient architecture. Built on the latest advancements in natural language processing, 0x Mini is optimized for both speed and accuracy, making it a strong contender in the space of language models. It is particularly well-suited for applications where resource constraints are a concern, offering a lightweight alternative to larger models like GPT while still delivering comparable performance.

## Features
- **Compact and Efficient**: 0x Mini is designed to be lightweight, making it suitable for deployment on resource-constrained devices.
- **High-Quality Text Generation**: The model is trained on a diverse dataset to generate coherent, contextually relevant, and human-like text.
- **Versatile Applications**: Suitable for tasks such as text completion, summarization, translation, and more.
- **Fast Inference**: Optimized for speed, ensuring quick and efficient responses.
- **Open-Source and Community-Driven**: Built with transparency and collaboration in mind, 0x Mini is available for the community to use, modify, and improve.

## Use Cases
- **Text Completion**: Assist users with writing tasks by generating coherent and contextually appropriate text.
- **Summarization**: Summarize long documents into concise and meaningful summaries.
- **Chatbots**: Power conversational AI systems with 0x Mini.
- **Content Creation**: Generate creative content such as stories, poems, or marketing copy.
- **Education**: Assist students with research, essay writing, and language learning.

## Getting Started
To get started with 0x Mini, follow these steps:

1. **Install the Model**:
   ```bash
   pip install transformers
   ```

2. **Load the Model**:
   ```python
   from transformers import AutoModelForCausalLM, AutoTokenizer

   model_name = "ozone-ai/llama-3.1-0x-mini"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)
   ```

3. **Generate Text**:
   ```python
   input_text = "Once upon a time"
   inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
   outputs = model.generate(**inputs, max_length=50)
   generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
   print(generated_text)
   ```