Llama 3.1 0x Mini

🚀 0x Mini

0x Mini is a cutting - edge language model developed by Ozone AI. It offers high - quality text generation with a compact and efficient architecture, making it a great choice for resource - constrained scenarios.

🚀 Quick Start

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

Install the Model

pip install transformers

Load the Model

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)

Generate Text

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)

✨ 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.

💻 Usage Examples

Basic Usage

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)

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)

Advanced Usage

# For more complex text generation tasks, you can adjust parameters like temperature and top - k.
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)

input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=100, temperature = 0.7, top_k = 50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

📚 Documentation

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.

📄 License

The license for 0x Mini is llama3.1

📦 Information Table