transformers safetensors qwen2 text-generation conversational en zh dataset:lmsys/lmsys-chat-1m base_model:Qwen/Qwen2.5-14B-Instruct base_model:finetune:Qwen/Qwen2.5-14B-Instruct license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- Qwen/Qwen2.5-14B-Instruct
datasets:
- lmsys/lmsys-chat-1m
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---

# 0x Lite

## We'd like to give a special thanks to ShuttleAI for making this possible.

## Join our Discord: https://discord.gg/J9AEasuK5e

## Overview
0x Lite is a state-of-the-art language model developed by Ozone AI, designed to deliver ultra-high-quality text generation capabilities while maintaining a compact and efficient architecture. Built on the latest advancements in natural language processing, 0x Lite 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 Lite 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 Lite 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 Lite.
- **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 Lite, 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/0x-lite"
   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)
   ```

# Chinese

# 0x Lite

## 概览
0x Lite 是由 Ozone AI 开发的最先进的语言模型，旨在提供超高质量的文本生成能力，同时保持紧凑和高效的架构。基于自然语言处理领域的最新进展，
0x Lite 在速度和准确性方面都进行了优化，在语言模型领域中是一个强有力的竞争者。它特别适合资源受限的应用场景，为那些希望获得与 GPT 等大型模
型相当性能但又需要轻量级解决方案的用户提供了一个理想选择。

## 特性
- **紧凑高效**：0x Lite 被设计成轻量化，适用于资源受限设备上的部署。
- **高质量文本生成**：该模型经过多样化的数据集训练，能够生成连贯、上下文相关且接近人类水平的文本。
- **多用途应用**：适合完成如文本补全、摘要、翻译等任务。
- **快速推理**：优化了速度，确保迅速高效的响应。
- **开源及社区驱动**：秉持透明和协作的理念，0x Lite 向社区开放，供用户使用、修改和完善。

## 应用场景
- **文本补全**：通过生成连贯且上下文相关的文本帮助用户完成写作任务。
- **摘要**：将长文档总结为简短而有意义的摘要。
- **聊天机器人**：利用 0x Lite 动力支持会话式 AI 系统。
- **内容创作**：生成创意性内容，如故事、诗歌或营销文案。
- **教育**：协助学生进行研究、写作及语言学习。

## 入门指南
要开始使用 0x Lite，请按照以下步骤操作：

1. **安装模型**：
   ```bash
   pip install transformers
   ```

2. **加载模型**：
   ```python
   from transformers import AutoModelForCausalLM, AutoTokenizer

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

3. **生成文本**：
   ```python
   input_text = "从前有一段时间"
   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)
   ```

---

Translated by 0x-Lite