Mythalion-13B-GPTQ
Maintainer: TheBloke
52
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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 Mythalion-13B-GPTQ is a large language model created by PygmalionAI and quantized to 4-bit and 8-bit precision by TheBloke. It is based on the original Mythalion 13B model and provides multiple GPTQ parameter configurations to optimize for different hardware and inference requirements. Similar quantized models from TheBloke include the MythoMax-L2-13B-GPTQ and wizard-mega-13B-GPTQ.
Model inputs and outputs
The Mythalion-13B-GPTQ is a text-to-text model, taking in natural language prompts and generating relevant text responses. It was fine-tuned on various datasets to enhance its conversational and storytelling capabilities.
Inputs
- Natural language prompts or instructions
Outputs
- Generated text responses relevant to the input prompt
Capabilities
The Mythalion-13B-GPTQ model excels at natural language understanding and generation, allowing it to engage in open-ended conversations and produce coherent, contextually-appropriate text. It performs well on tasks like creative writing, dialogue systems, and question-answering.
What can I use it for?
The Mythalion-13B-GPTQ model can be used for a variety of natural language processing applications, such as building interactive chatbots, generating creative fiction and dialog, and enhancing language understanding in other AI systems. Its large scale and diverse training data make it a powerful tool for developers and researchers working on language-focused projects.
Things to try
Try giving the model prompts that involve storytelling, world-building, or roleplaying scenarios. Its strong understanding of context and ability to generate coherent, imaginative text can lead to engaging and surprising responses. You can also experiment with different quantization configurations to find the best balance between model size, inference speed, and accuracy for 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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The Llama-2-13B-chat-GPTQ model is a version of Meta's Llama 2 13B language model that has been quantized using GPTQ, a technique for reducing the model's memory footprint without significant loss in quality. This model was created by TheBloke, a prominent AI researcher and developer. TheBloke has also made available GPTQ versions of the Llama 2 7B and 70B models, as well as other quantized variants using different techniques. The Llama-2-13B-chat-GPTQ model is designed for chatbot and conversational AI applications, having been fine-tuned by Meta on dialogue data. It outperforms many open-source chat models on standard benchmarks and is on par with closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. Model inputs and outputs Inputs The model accepts text input, which can be prompts, questions, or conversational messages. Outputs The model generates text output, which can be responses, answers, or continuations of the input. Capabilities The Llama-2-13B-chat-GPTQ model demonstrates strong natural language understanding and generation capabilities. It can engage in open-ended dialogue, answer questions, and assist with a variety of natural language tasks. The model has been imbued with an understanding of common sense and world knowledge, allowing it to provide informative and contextually relevant responses. What can I use it for? The Llama-2-13B-chat-GPTQ model is well-suited for building chatbots, virtual assistants, and other conversational AI applications. It can be used to power customer service bots, AI tutors, creative writing assistants, and more. The model's capabilities also make it useful for general-purpose language generation tasks, such as content creation, summarization, and language translation. Things to try One interesting aspect of the Llama-2-13B-chat-GPTQ model is its ability to maintain a consistent personality and tone across conversations. You can experiment with different prompts and see how the model adapts its responses to the context and your instructions. Additionally, you can try providing the model with specific constraints or guidelines to observe how it navigates ethical and safety considerations when generating text.
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