WizardLM-Uncensored-Falcon-40B-GPTQ
Maintainer: TheBloke
58
🔗
| 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 |
Create account to get full access
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!
Related Models
🔍
WizardLM-Uncensored-Falcon-7B-GPTQ
66
WizardLM-Uncensored-Falcon-7B-GPTQ is an experimental 4-bit GPTQ model for Eric Hartford's WizardLM-Uncensored-Falcon-7B. It was created by TheBloke using the AutoGPTQ tool. This model is part of a set of quantized models for the WizardLM-Uncensored-Falcon-7B, including GPTQ and GGML variants. It is smaller and more compact than the original model, aiming to provide a balance of performance and resource efficiency. Model inputs and outputs Inputs Text prompts Outputs Generative text responses Capabilities The WizardLM-Uncensored-Falcon-7B-GPTQ model is capable of generating coherent and contextual text based on the input prompts. It can engage in open-ended conversations, provide informative responses, and demonstrate creativity and imagination. The model has been trained on a large corpus of data, allowing it to draw from a broad knowledge base. What can I use it for? You can use WizardLM-Uncensored-Falcon-7B-GPTQ for a variety of natural language processing tasks, such as chatbots, content generation, and creative writing assistance. The uncensored nature of the model means it can be used for more open-ended and experimental applications, but it also requires additional caution and responsibility from the user. Things to try One interesting aspect of WizardLM-Uncensored-Falcon-7B-GPTQ is its ability to generate diverse and imaginative responses. You could try providing it with open-ended prompts or creative writing scenarios and see what kinds of unique and unexpected outputs it generates. Additionally, you could experiment with using different temperature and sampling settings to explore the model's range of capabilities.
Updated Invalid Date
🔎
WizardLM-Uncensored-Falcon-40B-GGML
40
The WizardLM-Uncensored-Falcon-40B-GGML model is an AI model created by TheBloke, an AI researcher and developer. It is based on Eric Hartford's 'uncensored' version of the WizardLM model, which was trained on a subset of the dataset with responses containing alignment or moralizing removed. This intent is to create a WizardLM that does not have built-in alignment, allowing it to be fine-tuned separately with techniques like RLHF. The model is available in a variety of quantized GGML formats for efficient CPU and GPU inference. Model inputs and outputs The WizardLM-Uncensored-Falcon-40B-GGML model is a text-to-text transformer model, meaning it takes textual inputs and generates textual outputs. The model can be used for a wide range of natural language processing tasks, from open-ended conversation to task-oriented dialogue to text generation. Inputs Arbitrary text prompts Outputs Coherent, contextual text responses Capabilities The WizardLM-Uncensored-Falcon-40B-GGML model has impressive language understanding and generation capabilities. It can engage in thoughtful, nuanced conversations, offering detailed and relevant responses. The model also demonstrates strong task-completion abilities, able to follow instructions and generate high-quality text outputs for a variety of applications. What can I use it for? The WizardLM-Uncensored-Falcon-40B-GGML model has a wide range of potential use cases. It could be used to power conversational AI assistants, create content such as articles or stories, help with research and analysis tasks, or even be fine-tuned for specialized applications like customer service or education. Given its 'uncensored' nature, it's important to use the model responsibly and consider potential ethical implications. Things to try One interesting aspect of the WizardLM-Uncensored-Falcon-40B-GGML model is its ability to engage in open-ended, creative conversations. You could try providing the model with thought-provoking prompts or scenarios and see the unique and insightful responses it generates. Additionally, the model's lack of built-in alignment allows for more flexibility in how it is used and fine-tuned, opening up new possibilities for customization and specialized applications.
Updated Invalid Date
🌿
WizardLM-33B-V1.0-Uncensored-GPTQ
44
The WizardLM-33B-V1.0-Uncensored-GPTQ is a quantized version of the WizardLM 33B V1.0 Uncensored model created by Eric Hartford. This model is supported by a grant from andreessen horowitz (a16z) and maintained by TheBloke. The GPTQ quantization process allows for reduced model size and faster inference, while maintaining much of the original model's performance. Model inputs and outputs Inputs Prompts**: The model accepts natural language prompts as input, which can be used to generate text. Outputs Generated text**: The model outputs coherent and contextually relevant text, which can be used for a variety of natural language processing tasks. Capabilities The WizardLM-33B-V1.0-Uncensored-GPTQ model is capable of generating high-quality text across a wide range of topics. It can be used for tasks such as story writing, dialogue generation, summarization, and question answering. The model's large size and uncensored nature allow it to tackle complex prompts and generate diverse, creative outputs. What can I use it for? The WizardLM-33B-V1.0-Uncensored-GPTQ model can be used in a variety of applications that require natural language generation, such as chatbots, content creation tools, and interactive fiction. Developers and researchers can fine-tune the model for specific domains or tasks to further enhance its capabilities. The GPTQ quantization also makes the model more accessible for deployment on consumer hardware. Things to try Try experimenting with different prompt styles and lengths to see how the model responds. You can also try giving the model specific instructions or constraints to see how it adapts its generation. Additionally, consider using the model in combination with other language models or tools to create more sophisticated applications.
Updated Invalid Date
🐍
WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ
47
The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is a 13B parameter language model created by combining Eric Hartford's WizardLM 13B V1.0 Uncensored with Kaio Ken's SuperHOT 8K. The model has been quantized to 4-bit using the GPTQ-for-LLaMa tool, which allows for increased context size up to 8K tokens. This model is an experimental new GPTQ that offers expanded context compared to the original WizardLM 13B V1.0 Uncensored. Model inputs and outputs The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model takes text prompts as input and generates coherent, detailed responses. The model has been trained on a large corpus of online text data, allowing it to understand and converse on a wide range of topics. Inputs Text prompt**: A text prompt provided to the model to initiate the generation of a response. Outputs Generated text**: The model's response to the provided text prompt, which can be up to 8192 tokens in length. Capabilities The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is a powerful language model capable of engaging in open-ended conversations, answering questions, and generating human-like text on a variety of subjects. Its expanded context size allows it to maintain coherence and provide more detailed responses compared to models with shorter context. What can I use it for? The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model can be used for a wide range of natural language processing tasks, such as chatbots, content generation, question answering, and creative writing. The increased context size makes it well-suited for applications that require longer-form, coherent responses. Things to try One interesting aspect of the WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is its ability to maintain context and narrative structure over longer text generation. Try providing the model with a multi-sentence prompt and see how it continues the story or expands on the initial ideas. The model's large knowledge base and generation capabilities make it well-suited for collaborative storytelling or worldbuilding exercises.
Updated Invalid Date