WizardLM-13B-V1-1-SuperHOT-8K-GPTQ
Maintainer: TheBloke
46
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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
WizardLM-13B-V1-1-SuperHOT-8K-GPTQ is a 13 billion parameter large language model that was created by merging WizardLM's WizardLM 13B V1.1 with Kaio Ken's SuperHOT 8K and then quantizing the model to 4-bit precision using GPTQ-for-LLaMa. This experimental model offers an increased context size of up to 8K tokens, which has been tested to work with the ExLlama library and text-generation-webui.
Model inputs and outputs
Inputs
- Prompts: The model takes prompts as input, which can be in the form of natural language text, code, or a combination of the two.
Outputs
- Text generation: The primary output of the model is generated text, which can be used for a variety of tasks such as language modeling, summarization, translation, and creative writing.
Capabilities
The WizardLM-13B-V1-1-SuperHOT-8K-GPTQ model is capable of generating coherent and contextually relevant text across a wide range of topics. Its increased context size allows it to maintain coherence and consistency over longer stretches of text, making it particularly well-suited for tasks that require sustained reasoning or storytelling.
What can I use it for?
This model can be used for a variety of natural language processing tasks, such as:
- Creative writing: The model's ability to generate coherent and contextually relevant text makes it useful for tasks like story writing, dialogue generation, and creative prompt completion.
- Task-oriented dialogue: With its increased context size, the model can be used to build interactive conversational agents that can engage in multi-turn dialogues and maintain context over longer exchanges.
- Content generation: The model can be used to generate text for a wide range of applications, such as blog posts, articles, product descriptions, and more.
Things to try
One interesting aspect of this model is its ability to leverage the extended 8K context size. By setting the appropriate parameters in tools like text-generation-webui, you can experiment with the model's performance on tasks that require maintaining coherence and consistency over longer stretches of text. Additionally, the model's quantization to 4-bit precision makes it more efficient and accessible for deployment on a variety of hardware platforms.
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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