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WizardLM-Uncensored-Falcon-40B-GPTQ

Maintainer: TheBloke

Total Score

58

Last updated 5/16/2024

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

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Model overview

TheBloke's WizardLM-Uncensored-Falcon-40B-GPTQ is an experimental 4-bit GPTQ model based on the WizardLM-Uncensored-Falcon-40b model created by Eric Hartford. It has been quantized to 4-bits using AutoGPTQ to reduce memory usage and inference time, while aiming to maintain high performance. This model is part of a broader set of similar quantized models that TheBloke has made available.

Model inputs and outputs

Inputs

  • Prompts: The model accepts natural language prompts as input, which it then uses to generate coherent and contextual responses.

Outputs

  • Text generation: The primary output of the model is generated text, which can range from short responses to longer passages. The model aims to provide helpful, detailed, and polite answers to user prompts.

Capabilities

This 4-bit quantized model retains the powerful language generation capabilities of the original WizardLM-Uncensored-Falcon-40b model, while using significantly less memory and inference time. It can engage in open-ended conversations, answer questions, and generate human-like text on a variety of topics. Despite the quantization, the model maintains a high level of performance and coherence.

What can I use it for?

The WizardLM-Uncensored-Falcon-40B-GPTQ model can be used for a wide range of natural language processing tasks, such as:

  • Text generation: Create engaging stories, articles, or other long-form content.
  • Question answering: Respond to user questions on various topics with detailed and informative answers.
  • Chatbots and virtual assistants: Integrate the model into conversational AI systems to provide helpful and articulate responses.
  • Content creation: Generate ideas, outlines, and even full pieces of content for blogs, social media, or other applications.

Things to try

One interesting aspect of this model is its lack of built-in alignment or guardrails, as it was trained on a subset of the original dataset without responses containing alignment or moralizing. This means users can experiment with the model to explore its unconstrained language generation capabilities, while being mindful of the responsible use of such a powerful AI system.



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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