LLaMA3-iterative-DPO-final-GGUF
Maintainer: bartowski
70
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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 LLaMA3-iterative-DPO-final-GGUF model is a series of quantized versions of the LLaMA3-iterative-DPO-final model, created by maintainer bartowski. The model was quantized using llama.cpp to provide various file sizes and tradeoffs between quality and memory usage. This allows users to choose the version that best fits their hardware and performance requirements.
Similar models include the Meta-Llama-3-8B-Instruct-GGUF, which is a series of quantized versions of Meta's Llama-3-8B Instruct model, also created by bartowski.
Model inputs and outputs
Inputs
- System prompt: Provides the context and instructions for the assistant
- User prompt: The text input from the user
Outputs
- Assistant response: The generated text response from the model
Capabilities
The LLaMA3-iterative-DPO-final-GGUF model is capable of generating human-like text responses based on the provided prompts. It can be used for a variety of text-to-text tasks, such as open-ended conversation, question answering, and creative writing.
What can I use it for?
The LLaMA3-iterative-DPO-final-GGUF model can be used for projects that require natural language generation, such as chatbots, virtual assistants, and content creation tools. The different quantized versions allow users to balance performance and memory usage based on their specific hardware and requirements.
Things to try
One interesting aspect of the LLaMA3-iterative-DPO-final-GGUF model is the range of quantized versions available. Users can experiment with the different file sizes and bit depths to find the optimal balance of quality and memory usage for their use case. For example, the Q6_K version provides very high quality with a file size of 6.59GB, while the Q4_K_S version has a smaller file size of 4.69GB with slightly lower quality, but still good performance.
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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