llama-3-chinese-8b-instruct-v3-gguf
Maintainer: hfl
48
✅
| 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 |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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