WizardLM-13B-V1.2-GGML
Maintainer: TheBloke
56
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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 WizardLM-13B-V1.2-GGML model is a large language model created by WizardLM. It is a 13 billion parameter version of the WizardLM model that has been quantized to run on CPU and GPU hardware. This model is similar to other WizardLM and wizardLM-7B-GGML models, as they are all part of TheBloke's efforts to provide high-quality open-source language models.
Model inputs and outputs
The WizardLM-13B-V1.2-GGML model is a text-to-text model, meaning it takes natural language text as input and generates natural language text as output. The model can be used for a variety of tasks, such as language generation, question answering, and text summarization.
Inputs
- Natural language text prompts
Outputs
- Generated natural language text
Capabilities
The WizardLM-13B-V1.2-GGML model has been trained on a large corpus of text data, allowing it to generate coherent and contextually-relevant responses to a wide range of prompts. It has been designed to be helpful, informative, and engaging in its interactions.
What can I use it for?
The WizardLM-13B-V1.2-GGML model can be used for a variety of applications, such as:
- Content generation: The model can be used to generate articles, stories, or other types of text content.
- Chatbots and virtual assistants: The model can be used to power conversational interfaces, providing natural language responses to user queries.
- Question answering: The model can be used to answer a wide range of questions on various topics.
- Text summarization: The model can be used to generate concise summaries of longer pieces of text.
Things to try
One interesting thing to try with the WizardLM-13B-V1.2-GGML model is to explore its versatility by providing it with prompts across different domains, such as creative writing, technical instructions, or open-ended questions. This can help you understand the model's capabilities and limitations, and identify areas where it excels or struggles.
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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