wizard-mega-13B-GGML

Maintainer: TheBloke

Total Score

58

Last updated 5/28/2024

🎯

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The wizard-mega-13B-GGML is a large language model created by OpenAccess AI Collective and quantized by TheBloke into GGML format for efficient CPU and GPU inference. It is based on the original Wizard Mega 13B model, which was fine-tuned on the ShareGPT, WizardLM, and Wizard-Vicuna datasets. The GGML format models provided here offer a range of quantization options to trade off between performance and accuracy.

Similar models include WizardLM's WizardLM 7B GGML, Wizard Mega 13B - GPTQ, and June Lee's Wizard Vicuna 13B GGML. These models all leverage the original Wizard Mega 13B as a starting point and provide various quantization methods and formats for different hardware and inference needs.

Model inputs and outputs

The wizard-mega-13B-GGML model is a text-to-text transformer, meaning it takes natural language text as input and generates natural language text as output. The input can be any kind of text, such as instructions, questions, or prompts. The output is the model's response, which can range from short, direct answers to more open-ended, multi-sentence generations.

Inputs

  • Natural language text prompts, instructions, or questions

Outputs

  • Generated natural language text responses

Capabilities

The wizard-mega-13B-GGML model demonstrates strong text generation capabilities, able to engage in open-ended conversations, answer questions, and complete a variety of language tasks. It can be used for applications like chatbots, question-answering systems, content generation, and more.

What can I use it for?

The wizard-mega-13B-GGML model can be a powerful tool for a variety of language-based applications. For example, you could use it to build a chatbot that can engage in natural conversations, a question-answering system to help users find information, or a content generation system to produce draft articles, stories, or other text-based content. The flexibility of the model's text-to-text capabilities means it can be adapted to many different use cases.

Companies could potentially monetize the wizard-mega-13B-GGML model by incorporating it into products and services that leverage its language understanding and generation abilities, such as customer service chatbots, writing assistants, or specialized content creation tools.

Things to try

One interesting thing to try with the wizard-mega-13B-GGML model is to experiment with different prompting strategies. By crafting prompts that provide context, instructions, or constraints, you can guide the model to generate responses that align with your specific needs. For example, you could try prompting the model to write a story about a particular topic, or to answer a question in a formal, professional tone.

Another idea is to fine-tune the model on your own specialized dataset, which could allow it to perform even better on domain-specific tasks. The GGML format makes the model easy to integrate into various inference frameworks and applications.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

↗️

wizardLM-7B-GGML

TheBloke

Total Score

157

The wizardLM-7B-GGML model is a large language model developed by TheBloke, a prominent AI model creator. This model is part of the WizardLM family of models, which range in scale from 7 billion to 70 billion parameters. The wizardLM-7B-GGML model is available in a variety of quantized GGML formats, providing options for different performance and resource requirements. Similar models from TheBloke include the Llama-2-7B-GGML and Llama-2-13B-GGML models, which are based on Meta's Llama 2 architecture and also available in quantized GGML formats. Model inputs and outputs Inputs Text**: The wizardLM-7B-GGML model takes text input and generates text output. Outputs Text**: The model generates coherent, contextual text based on the input. Capabilities The wizardLM-7B-GGML model is a powerful language model capable of a wide range of natural language processing tasks, such as text generation, question answering, and language understanding. It can be used to create engaging dialogues, summarize text, and even generate creative content. What can I use it for? The wizardLM-7B-GGML model can be used for a variety of projects, including chatbots, content creation, and language learning applications. Its quantized GGML formats make it suitable for deployment on CPU and GPU systems, allowing for efficient inference on a range of hardware. Things to try One interesting aspect of the wizardLM-7B-GGML model is its ability to generate coherent and context-aware text. Try providing it with prompts that require reasoning, such as "Explain the economic impact of the recent policy changes in a way that a 10-year-old would understand." The model should be able to generate a clear and simplified explanation, demonstrating its language understanding and generation capabilities.

Read more

Updated Invalid Date

💬

WizardLM-13B-V1.2-GGML

TheBloke

Total Score

56

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.

Read more

Updated Invalid Date

🌐

wizard-mega-13B-GPTQ

TheBloke

Total Score

107

The wizard-mega-13B-GPTQ model is a 13-billion parameter language model created by the Open Access AI Collective and quantized by TheBloke. It is an extension of the original Wizard Mega 13B model, with multiple quantized versions available to choose from based on desired performance and VRAM requirements. Similar models include the wizard-vicuna-13B-GPTQ and WizardLM-7B-GPTQ models, which provide alternative architectures and training datasets. Model inputs and outputs The wizard-mega-13B-GPTQ model is a text-to-text transformer model, taking natural language prompts as input and generating coherent and contextual responses. The model was trained on a large corpus of web data, allowing it to engage in open-ended conversations and tackle a wide variety of tasks. Inputs Natural language prompts or instructions Conversational context, such as previous messages in a chat Outputs Coherent and contextual natural language responses Continuations of provided prompts Answers to questions or instructions Capabilities The wizard-mega-13B-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and generating human-like text on a wide range of topics. It has demonstrated strong performance on language understanding and generation tasks, and can adapt its responses to the specific context and needs of the user. What can I use it for? The wizard-mega-13B-GPTQ model can be used for a variety of applications, such as building conversational AI assistants, generating creative writing, summarizing text, and even providing explanations and information on complex topics. The quantized versions available from TheBloke allow for efficient deployment on both GPU and CPU hardware, making it accessible for a wide range of use cases. Things to try One interesting aspect of the wizard-mega-13B-GPTQ model is its ability to engage in multi-turn conversations and adapt its responses based on the context. Try providing the model with a series of related prompts or questions, and see how it builds upon the previous responses to maintain a coherent and natural dialogue. Additionally, experiment with different prompting techniques, such as providing instructions or persona information, to see how the model's outputs can be tailored to your specific needs.

Read more

Updated Invalid Date

🚀

WizardLM-13B-Uncensored-GGML

TheBloke

Total Score

57

The WizardLM-13B-Uncensored-GGML is an AI model created by Eric Hartford and maintained by TheBloke. It is a 13-billion parameter language model based on the LLaMA architecture, trained on a subset of the dataset with responses containing alignment or moralizing removed. This aims to produce an uncensored model that can have alignment added separately, such as through a RLHF LoRA. Similar models maintained by TheBloke include the WizardLM-30B-Uncensored-GGML, the Wizard-Vicuna-7B-Uncensored-GGML, and the wizardLM-7B-GGML. Model inputs and outputs The WizardLM-13B-Uncensored-GGML model takes text prompts as input and generates coherent, context-appropriate text as output. The model can be used for a variety of natural language tasks, including content generation, question answering, and language translation. Inputs Text prompts**: The model takes natural language text prompts as input, which can be of varying lengths. Outputs Generated text**: The model outputs generated text that is coherent, context-appropriate, and grammatically correct. The length of the output can be specified. Capabilities The WizardLM-13B-Uncensored-GGML model is capable of generating high-quality, natural-sounding text on a wide range of topics. Due to its large size and training on a diverse dataset, the model can engage in open-ended conversation, answer questions, and even write creative fiction or poetry. What can I use it for? The WizardLM-13B-Uncensored-GGML model can be used for a variety of natural language processing tasks, such as content generation, summarization, translation, and question answering. It could be particularly useful for applications that require engaging, context-appropriate language, such as chatbots, writing assistants, and creative writing tools. Things to try One interesting aspect of the WizardLM-13B-Uncensored-GGML model is its lack of built-in alignment or censorship, which allows for more open-ended and potentially controversial outputs. Users could experiment with prompts that explore the model's limits and capabilities in this regard, while being mindful of the responsibility involved in publishing the generated content.

Read more

Updated Invalid Date