Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

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README.md +124 -113

README.md CHANGED Viewed

@@ -1,114 +1,125 @@
----
-library_name: transformers
-datasets:
-- lmsys/lmsys-chat-1m
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-pipeline_tag: text-generation
-language:
-- en
-- zh
-license: apache-2.0
----
-# 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

+---
+library_name: transformers
+datasets:
+- lmsys/lmsys-chat-1m
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+license: apache-2.0
+---
+# 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