MythoMax-L2-13B-GGML
Maintainer: TheBloke
81
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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
MythoMax-L2-13B-GGML is an AI model created by the AI researcher Gryphe, and further optimized and quantized by TheBloke. It is an improved variant of Gryphe's MythoLogic-L2 and Huginn models, combining their robust understanding and extensive writing capabilities. The model uses a unique tensor merge technique to blend these capabilities, resulting in strong performance on both roleplaying and storywriting tasks.
TheBloke has provided quantized versions of the model in GGML format, which can be used for efficient CPU and GPU inference. These include 4-bit, 5-bit and 8-bit quantized models as well as GPTQ models for GPU acceleration. There are also GGUF models available, which provide improved compatibility with the latest version of llama.cpp.
Model inputs and outputs
Inputs
- Text: The model takes text as input, which it uses to generate further text outputs.
Outputs
- Text: The model generates natural language text outputs, which can be used for a variety of purposes such as creative writing, roleplay, and language tasks.
Capabilities
The MythoMax-L2-13B-GGML model excels at both roleplaying and storywriting due to its unique tensor merge technique, which combines the strengths of the MythoLogic-L2 and Huginn models. It is able to generate coherent and engaging text across a range of styles and genres.
What can I use it for?
The MythoMax-L2-13B-GGML model can be used for a variety of text generation tasks, such as:
- Creative writing and storytelling
- Roleplaying and interactive fiction
- Language modeling and downstream NLP applications
The quantized versions provided by TheBloke allow for efficient inference on both CPU and GPU, making the model accessible to a wide range of users and use cases.
Things to try
One interesting aspect of the MythoMax-L2-13B-GGML model is its ability to generate long, coherent responses. This can be particularly useful for roleplaying and interactive fiction scenarios, where the model can maintain a consistent narrative and character over an extended exchange.
Researchers and developers may also want to explore fine-tuning the model on domain-specific data to further improve its performance on specialized tasks. The Gryphe's original unquantised fp16 model could be a good starting point for further training and customization.
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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