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llama-3.1-0x-mini

Text2Text Generation
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llama
text-generation
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metadata
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:

    pip install transformers

  2. 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)

  3. 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)