DeepSeek-Coder-V2-Lite-Instruct-GGUF
Maintainer: bartowski
61
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| 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
The DeepSeek-Coder-V2-Lite-Instruct-GGUF model is a quantized version of the original DeepSeek-Coder-V2-Lite-Instruct model, created using llama.cpp by the maintainer bartowski. This model is designed for text-to-text tasks and offers a range of quantized versions to suit different performance and storage requirements.
Model inputs and outputs
The DeepSeek-Coder-V2-Lite-Instruct-GGUF model takes in a user prompt and generates a response from the assistant. The model does not have a separate system prompt input.
Inputs
- Prompt: The user's input text that the model will generate a response to.
Outputs
- Assistant response: The text generated by the model in response to the user's prompt.
Capabilities
The DeepSeek-Coder-V2-Lite-Instruct-GGUF model is capable of a wide range of text-to-text tasks, including language generation, question answering, and code generation. It can be used for tasks such as chatbots, creative writing, and programming assistance.
What can I use it for?
The DeepSeek-Coder-V2-Lite-Instruct-GGUF model can be used for a variety of applications, such as building conversational AI assistants, generating creative content, and assisting with programming tasks. For example, you could use it to create a chatbot that can engage in natural conversations, generate stories or poems, or help with coding challenges.
Things to try
One interesting thing to try with the DeepSeek-Coder-V2-Lite-Instruct-GGUF model is to experiment with the different quantized versions available, as they offer a range of performance and storage trade-offs. You could test out the various quantization levels and see how they impact the model's capabilities and efficiency on 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!
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