FlagAlpha/Llama3-Chinese-8B-Instruct
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 23, 2024License:apache-2.0Architecture:Transformer0.1K Open Weights Warm

FlagAlpha/Llama3-Chinese-8B-Instruct is an 8 billion parameter instruction-tuned causal language model based on Llama3-8B, developed by the Llama Chinese Community and AtomEcho. This model is specifically fine-tuned for Chinese language processing and dialogue, offering enhanced performance for applications requiring robust Chinese natural language understanding and generation. It is designed for developers seeking a powerful Llama3-based model optimized for the Chinese linguistic context.

Loading preview...

Llama3-Chinese-8B-Instruct Overview

FlagAlpha/Llama3-Chinese-8B-Instruct is an 8 billion parameter instruction-tuned large language model, built upon the Llama3-8B architecture. It is a collaborative effort between the Llama Chinese Community and AtomEcho, focusing on optimizing the base Llama3 model for Chinese language understanding and generation.

Key Capabilities

  • Chinese Language Optimization: Specifically fine-tuned for robust performance in Chinese dialogue and natural language processing tasks.
  • Instruction Following: Designed to accurately follow instructions for various prompts, making it suitable for conversational AI and task-oriented applications.
  • Llama3 Foundation: Benefits from the advanced architecture and capabilities of the Llama3-8B base model.

Good For

  • Chinese NLP Applications: Ideal for developers building applications that require high-quality Chinese text generation, summarization, translation, or conversational agents.
  • Research and Development: Provides a strong foundation for further research and fine-tuning on specific Chinese datasets or domains.
  • Dialogue Systems: Well-suited for creating interactive chatbots and virtual assistants that communicate effectively in Chinese.
Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p