llama-3-chinese-8b-instruct-v3
Maintainer: hfl
47
🔗
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
llama-3-chinese-8b-instruct-v3 is a large language model developed by the Hugging Face team, specifically designed for Chinese language tasks. It is built upon the LLaMA-3 model, which was originally released by Meta, and further fine-tuned on Chinese data. This model is an instruction-following (chat) model, meaning it can be used for a variety of conversational tasks, such as question answering, task completion, and open-ended dialogue. It is part of the Chinese-LLaMA-Alpaca project, which also includes other related models like chinese-llama-2-7b and chinese-alpaca-2-13b.
Model inputs and outputs
The llama-3-chinese-8b-instruct-v3 model takes text as input and generates text as output. It can be used for a wide range of natural language processing tasks, such as language generation, question answering, and task completion.
Inputs
- Text prompts, which can be in the form of natural language instructions, questions, or open-ended statements
Outputs
- Generated text, which can be responses to the input prompts, completions of tasks, or continuations of the provided text
Capabilities
The llama-3-chinese-8b-instruct-v3 model has been shown to perform well on a variety of Chinese language tasks, including question answering, summarization, and open-ended dialogue. It can generate coherent and contextually relevant responses, and has been trained to follow instructions and complete tasks in a helpful and informative manner.
What can I use it for?
This model can be used for a wide range of applications that involve Chinese language processing, such as virtual assistants, chatbots, content generation, and research. For example, you could use it to build a Chinese-language question-answering system, generate summaries of Chinese text, or create a conversational interface for a Chinese-speaking audience.
Things to try
One interesting thing to try with llama-3-chinese-8b-instruct-v3 is to engage it in open-ended dialogue and see how it responds to follow-up questions or requests for clarification. You could also experiment with using the model for tasks like code generation, translation, or creative writing in Chinese. Additionally, you could fine-tune the model on your own Chinese language data to adapt it to your specific use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
✅
llama-3-chinese-8b-instruct-v3-gguf
48
llama-3-chinese-8b-instruct-v3-gguf is a quantized version of the Llama-3-Chinese-8B-Instruct-v3 model, created by hfl. It is a large language model trained on a mix of the hfl/Llama-3-Chinese-8B-Instruct, hfl/Llama-3-Chinese-8B-Instruct-v2, and meta-llama/Meta-Llama-3-8B-Instruct models. The quantized version is compatible with llama.cpp and other libraries that support the GGUF format. Model inputs and outputs The llama-3-chinese-8b-instruct-v3-gguf model is an instruction-following model, which means it can be used for tasks like conversation, question answering, and code generation. The model takes text as input and generates text as output. Inputs Text prompts for the model to continue or respond to Outputs Relevant text generated in response to the input prompt Capabilities The llama-3-chinese-8b-instruct-v3-gguf model is capable of engaging in open-ended dialogue, answering questions, and generating text in Chinese. It has been fine-tuned on instruction-following tasks, allowing it to follow prompts and complete requests from users. What can I use it for? This model could be useful for building Chinese language chatbots, virtual assistants, or other applications that require natural language processing and generation. Developers could integrate it into their projects to provide Chinese language capabilities, such as answering user questions, providing information, or automating workflows. Things to try One interesting thing to try with this model is prompting it with open-ended requests or instructions in Chinese and seeing how it responds. You could ask it to explain a concept, generate poetry, or solve a coding problem, and observe the quality and coherence of its output. Experimenting with different prompting styles and techniques can help you understand the model's strengths and limitations.
Updated Invalid Date
🌀
chinese-llama-2-7b
95
chinese-llama-2-7b is a large language model developed by the HFL (Hugging Face Labs) team. It is part of the Chinese-LLaMA-Alpaca-2 project, which is based on Meta's Llama-2 model. The chinese-llama-2-7b model has been expanded and optimized with a new Chinese vocabulary, and further pre-trained on large-scale Chinese data to improve its understanding of the Chinese language. This resulted in significant performance improvements compared to the first-generation Chinese-LLaMA and Chinese-Alpaca models. The model supports a 4K context and can be expanded up to 18K+ using NTK methods. The project also includes related models like the Chinese-Alpaca-2-7B and Chinese-Alpaca-2-13B instruction-following models, as well as LoRA variants of the base models. Model inputs and outputs Inputs The chinese-llama-2-7b model accepts Chinese text as input. Outputs The model generates Chinese text as output, with the ability to handle a wide range of tasks such as language generation, translation, and question answering. Capabilities The chinese-llama-2-7b model has been designed to excel at Chinese language understanding and generation tasks. It can be used for a variety of applications, such as writing assistance, content generation, and language understanding. The model's capabilities have been enhanced through incremental pre-training on large-scale Chinese data, allowing it to produce more coherent and relevant Chinese text. What can I use it for? The chinese-llama-2-7b model can be used for a wide range of Chinese language applications, such as: Automated writing and content generation: The model can be used to generate Chinese articles, stories, and other types of content. Chatbots and virtual assistants: The model can be used to power Chinese-language chatbots and virtual assistants, allowing them to engage in natural conversations. Language understanding and question answering: The model can be used to answer questions and provide information in Chinese, leveraging its improved language understanding capabilities. Machine translation: The model can be fine-tuned for Chinese-to-English or other language translation tasks. Things to try One interesting aspect of the chinese-llama-2-7b model is its ability to handle long-form Chinese text. By leveraging the NTK method, the model can be expanded to support context lengths up to 18K tokens, allowing it to engage in more coherent and in-depth conversations. Developers and researchers can experiment with this capability to explore its potential for tasks like summarization, analysis, and open-ended dialogue.
Updated Invalid Date
📈
chinese-alpaca-2-13b
84
The chinese-alpaca-2-13b model is a Chinese language model developed by the HFL (Huawei Ark Lab) team. It is based on the second generation of the Chinese LLaMA and Alpaca models, which have been expanded and optimized with a larger Chinese vocabulary beyond the original LLaMA-2 model. Through incremental pre-training on large-scale Chinese data, the model has seen significant performance improvements in fundamental Chinese language understanding compared to the first-generation models. This model is related to other Chinese LLaMA and Alpaca models, including the Chinese-LLaMA-2-7B-16K, Chinese-LLaMA-2-LoRA-7B-16K, Chinese-LLaMA-2-13B-16K, and Chinese-LLaMA-2-LoRA-13B-16K models, as well as the Chinese-Alpaca-2-7B, Chinese-Alpaca-2-LoRA-7B, Chinese-Alpaca-2-13B, and Chinese-Alpaca-2-LoRA-13B models. Model inputs and outputs Inputs The chinese-alpaca-2-13b model takes Chinese text as input. Outputs The model generates Chinese text as output, making it suitable for a variety of natural language processing tasks such as text generation, language translation, and conversational responses. Capabilities The chinese-alpaca-2-13b model excels at understanding and generating high-quality Chinese text. It has been optimized for tasks like open-ended dialogue, answering questions, and providing informative and coherent responses. The model's large 13 billion parameter size and extensive pre-training on Chinese data allow it to handle complex Chinese language understanding and generation with high accuracy. What can I use it for? The chinese-alpaca-2-13b model can be used for a wide range of Chinese language tasks, such as: Chatbots and conversational AI**: The model's strong language understanding and generation capabilities make it well-suited for building conversational assistants and chatbots that can engage in natural-sounding Chinese dialogues. Content generation**: The model can be used to generate various types of Chinese text, including articles, stories, and creative writing, by fine-tuning on specific datasets. Question answering**: The model can be used to build systems that can accurately answer questions on a variety of Chinese-language topics, leveraging its broad knowledge base. Translation**: The model's understanding of Chinese language structure and semantics can be used to develop Chinese-to-English (or other language) translation systems. Things to try One interesting aspect of the chinese-alpaca-2-13b model is its ability to handle long-form Chinese text. The model supports a 4K context window, which can be expanded up to 18K+ using the NTK method, allowing it to maintain coherence and understanding over extended passages of Chinese input. This makes the model well-suited for tasks like summarization, essay generation, and long-form dialogue. Another key feature is the model's strong performance on safety and truthfulness benchmarks, such as TruthfulQA and ToxiGen. This suggests the model has been carefully trained to generate responses that are informative and truthful, while avoiding potentially harmful or toxic content. Developers can leverage these safety features when building applications that require reliable and trustworthy Chinese language models.
Updated Invalid Date
🧠
chinese-alpaca-2-7b
157
The chinese-alpaca-2-7b model is a large language model developed by the HFL team. It is based on the Llama-2 model released by Meta and is the second generation of the Chinese LLaMA and Alpaca models. This model has been expanded and optimized with a larger Chinese vocabulary beyond the original Llama-2 model. Through large-scale Chinese data pre-training, the model has significantly improved its understanding of the Chinese language compared to the first-generation models. The Chinese-Alpaca-2-13B model is a larger variant of this model. Model inputs and outputs The chinese-alpaca-2-7b model is a text-to-text model, meaning it takes in text input and generates text output. It can be used for a variety of natural language processing tasks, such as language generation, question answering, and text summarization. Inputs Natural language text in Chinese Outputs Generated natural language text in Chinese Capabilities The chinese-alpaca-2-7b model has been trained on a large corpus of Chinese data, giving it strong capabilities in understanding and generating high-quality Chinese text. It can be used for a variety of tasks, such as engaging in open-ended conversations, answering questions, and generating creative text. What can I use it for? The chinese-alpaca-2-7b model can be used for a wide range of applications that require Chinese language understanding and generation, such as: Chatbots and virtual assistants**: The model can be used to build chatbots and virtual assistants that can engage in natural conversations in Chinese. Content generation**: The model can be used to generate Chinese text for a variety of purposes, such as articles, stories, and product descriptions. Language learning**: The model can be used to help learners improve their Chinese language skills by providing them with high-quality examples of Chinese text and engaging in interactive language practice. Things to try One interesting aspect of the chinese-alpaca-2-7b model is its ability to generate coherent and contextually relevant text across a wide range of topics. You can try prompting the model with open-ended questions or writing prompts in Chinese and see the type of responses it generates. Additionally, you can experiment with different techniques for fine-tuning the model on your own Chinese language data to adapt it to your specific use case.
Updated Invalid Date