|
--- |
|
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) |
|
``` |
