WizardLM-Uncensored-Falcon-40b
Maintainer: cognitivecomputations
93
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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-Uncensored-Falcon-40b is an AI model developed by cognitivecomputations that has been trained on a subset of the dataset used for the Falcon-40B-Instruct model. The intent behind this model is to remove the built-in alignment and moralizing responses, allowing the model to be used as a base for adding separate alignment (via techniques like RLHF LoRA) if desired.
Similar models include the WizardLM-13B-Uncensored, WizardLM-7B-Uncensored, WizardLM-Uncensored-Falcon-40B-GPTQ, and WizardLM-30B-Uncensored, all of which are variations of the WizardLM model trained on different-sized versions of the Falcon language model.
Model inputs and outputs
The WizardLM-Uncensored-Falcon-40b is a text-to-text transformer model, meaning it takes text as input and generates text as output. The input format is specified as "Prompt format is WizardLM", but no further details are provided.
Inputs
- Text prompts in the "WizardLM" format
Outputs
- Generated text responses to the input prompts
Capabilities
The WizardLM-Uncensored-Falcon-40b model is designed to provide more open-ended and less constrained language generation compared to models with built-in alignment and moralizing. This allows for more flexibility in how the model can be used, but also means the outputs may be less controlled.
What can I use it for?
The WizardLM-Uncensored-Falcon-40b model could be useful for a variety of text generation tasks, such as creative writing, conversational AI, and language modeling. Since the model has had the alignment and moralizing responses removed, it may be a good starting point for adding custom alignment or other fine-tuning to create specialized language models for specific use cases.
Things to try
With the uncensored nature of this model, it's important to be cautious and responsible in how you use the generated outputs. Experimenting with prompts to see the range of responses the model can produce could yield interesting results, but care should be taken to avoid generating harmful or unethical content. Consider fine-tuning the model with your own data or techniques like RLHF to align the output with your desired goals and values.
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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WizardLM-Uncensored-Falcon-40B-GPTQ
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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.
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