WizardLM-7B-Uncensored
Maintainer: cognitivecomputations
422
⛏️
| 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
WizardLM-7B-Uncensored is an AI language model created by cognitivecomputations. It is a version of the WizardLM model that has had responses containing "alignment / moralizing" removed from the training dataset. This was done with the intent of creating a WizardLM that does not have alignment built-in, allowing alignment to be added separately if desired, such as through reinforcement learning.
Similar models include the WizardLM-13B-Uncensored and WizardLM-7B-uncensored-GPTQ models, which share a similar goal of providing an "uncensored" WizardLM without built-in alignment.
Model inputs and outputs
WizardLM-7B-Uncensored is a text-to-text AI model, meaning it takes text input and generates text output. The model can be used for a variety of natural language processing tasks, such as language generation, summarization, and question answering.
Inputs
- Text prompts: The model accepts free-form text prompts as input, which it then uses to generate relevant and coherent text output.
Outputs
- Generated text: The model's primary output is generated text, which can range from short phrases to longer multi-sentence responses, depending on the input prompt.
Capabilities
WizardLM-7B-Uncensored has a wide range of capabilities, including generating human-like text, answering questions, and engaging in open-ended conversations. While the model has had alignment-related content removed from its training, it may still exhibit biases or generate controversial content, so caution is advised when using it.
What can I use it for?
WizardLM-7B-Uncensored can be used for a variety of applications, such as:
- Content generation: The model can be used to generate text for things like articles, stories, or social media posts.
- Chatbots and virtual assistants: The model's language generation capabilities can be leveraged to build conversational AI agents.
- Research and experimentation: The model's "uncensored" nature makes it an interesting subject for researchers and AI enthusiasts to explore and experiment with.
However, it's important to note that the lack of built-in alignment or content moderation means that users are responsible for the content generated by the model and should exercise caution when using it.
Things to try
One interesting thing to try with WizardLM-7B-Uncensored is to experiment with different prompting techniques to see how the model responds. For example, you could try providing the model with more structured or specialized prompts to see if it can generate content that aligns with your specific requirements. Additionally, you could explore the model's capabilities in areas like creative writing, task-oriented dialogue, or general knowledge exploration.
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-13B-Uncensored
537
WizardLM-13B-Uncensored is a large language model created by cognitivecomputations that has had alignment-focused content removed from its training dataset. The intent is to train a WizardLM model without built-in alignment, so that alignment can be added separately using techniques like reinforcement learning from human feedback (RLHF). Similar uncensored models available include the WizardLM-7B-Uncensored-GPTQ and WizardLM-30B-Uncensored-GPTQ models, also provided by the maintainer TheBloke. Model inputs and outputs Inputs Text prompts**: The model takes in text prompts as input, which can be of varying lengths. Outputs Text generation**: The model generates coherent, fluent text in response to the input prompt. Capabilities The WizardLM-13B-Uncensored model can be used for a variety of natural language processing tasks, such as text generation, summarization, and language understanding. As an uncensored model, it has fewer built-in limitations compared to some other language models, allowing for more open-ended and unfiltered text generation. What can I use it for? This model could be used for creative writing, story generation, dialogue systems, and other applications where open-ended, unfiltered text is desired. However, as an uncensored model, it is important to carefully consider the potential risks and use the model responsibly. Things to try You could try providing the model with prompts on a wide range of topics and observe the types of responses it generates. Additionally, you could experiment with different decoding parameters, such as temperature and top-k/top-p sampling, to adjust the level of creativity and risk in the generated text.
Updated Invalid Date
🤷
WizardLM-30B-Uncensored
137
WizardLM-30B-Uncensored is a large language model created by cognitivecomputations that was trained on a subset of the dataset, with responses containing alignment or moralizing removed. The intent is to train a WizardLM model without built-in alignment, so that alignment can be added separately using techniques like Reinforcement Learning from Human Feedback (RLHF) LoRA. Similar models include the WizardLM-13B-Uncensored, WizardLM-7B-Uncensored, and WizardLM-30B-Uncensored-GPTQ models, all of which share the goal of removing built-in alignment from the WizardLM architecture. Model Inputs and Outputs WizardLM-30B-Uncensored is a text-to-text model, accepting free-form text prompts as input and generating completions as output. The model can be used for a wide variety of natural language tasks, including answering questions, generating stories or articles, and engaging in open-ended conversation. Inputs Free-form text prompts of any length Outputs Completions of the input prompt, generating new relevant text Capabilities WizardLM-30B-Uncensored is a powerful language model capable of engaging in sophisticated natural language tasks. It can answer questions, generate coherent and engaging text on a wide range of topics, and even carry on conversations. By removing the built-in alignment, the model avoids potential biases or limitations in its outputs, allowing for more open-ended and creative uses. What Can I Use It For? The WizardLM-30B-Uncensored model can be used for a variety of applications, including: Chatbots and virtual assistants: The model's conversational capabilities make it well-suited for powering chatbots and virtual assistants that can engage in natural, open-ended dialogue. Content generation: The model can be used to generate text for articles, stories, scripts, and other creative writing projects. Question answering: The model can be used to answer questions on a wide range of topics, drawing upon its broad knowledge base. Research and experimentation: The uncensored nature of the model makes it an interesting subject for further research and experimentation into language model capabilities and alignment. Things to Try One interesting aspect of the WizardLM-30B-Uncensored model is its lack of built-in alignment or constraints. This means that users can experiment with prompting the model to generate content on a wide range of topics, including potentially sensitive or controversial subjects. However, it's important to keep in mind the potential risks and to use the model responsibly, as the uncensored nature means there are no built-in guardrails. Some ideas for things to try with the model include: Exploring the model's ability to engage in open-ended, free-flowing conversation on a variety of topics Experimenting with the model's creativity by prompting it to generate stories, poems, or other forms of imaginative writing Investigating the model's reasoning and analytical capabilities by asking it to solve problems or provide insights on complex topics Overall, the WizardLM-30B-Uncensored model represents an interesting and powerful language model that offers both opportunities and challenges for users to explore.
Updated Invalid Date
⛏️
WizardLM-Uncensored-Falcon-7b
55
WizardLM-Uncensored-Falcon-7b is a large language model trained by cognitivecomputations using the Falcon-7B model as a base. It was trained on a subset of the original dataset, with responses containing alignment or moralizing removed. The goal is to create a more neutral WizardLM model, allowing alignment to be added separately if desired. Similar models provided by the maintainer include the WizardLM-Uncensored-Falcon-40b, WizardLM-7B-Uncensored, WizardLM-13B-Uncensored, and WizardLM-Uncensored-Falcon-7B-GPTQ. Model inputs and outputs WizardLM-Uncensored-Falcon-7b is a text-to-text model, taking in textual prompts and generating text responses. The input prompts follow a specific format: Inputs Prompt**: A text prompt for the model to generate a response to, following the WizardLM format. Outputs Response**: The model's generated text response to the provided prompt. Capabilities WizardLM-Uncensored-Falcon-7b can be used for a variety of natural language tasks, such as open-ended conversation, question answering, summarization, and creative writing. By removing the built-in alignment and moralizing from the original WizardLM, it provides a more neutral foundation that can be further customized for specific use cases. What can I use it for? The lack of built-in alignment makes WizardLM-Uncensored-Falcon-7b well-suited for applications where a more flexible, customizable language model is required. This could include chatbots, content generation tools, research assistants, and creative writing applications. The model can be fine-tuned or combined with additional techniques like RLHF to imbue it with desired traits or behaviors. Things to try Given the uncensored nature of this model, it's important to carefully consider the potential implications and use cases. Responsible use and monitoring is crucial. Some interesting things to explore could include: Fine-tuning the model on specific datasets to optimize it for particular tasks or domains. Combining the model with RLHF techniques to instill desired behaviors and traits. Exploring the model's capabilities for open-ended conversation, creative writing, and task-oriented dialogue. Investigating ways to safely deploy the model in real-world applications while mitigating potential risks.
Updated Invalid Date
🌐
WizardLM-33B-V1.0-Uncensored
59
WizardLM-33B-V1.0-Uncensored is a large language model developed by cognitivecomputations as a retraining of the WizardLM/WizardLM-30B-V1.0 model with a filtered dataset. This model aims to reduce refusals, avoidance, and bias compared to the previous versions. It is important to note that since LLaMA itself has inherent ethical beliefs, there is no such thing as a "truly uncensored" model. However, this model is intended to be more compliant than the earlier WizardLM/WizardLM-7B-V1.0 version. Model inputs and outputs WizardLM-33B-V1.0-Uncensored is a text-to-text model, meaning it takes text as input and generates text as output. The model is trained using Vicuna-1.1 style prompts, where the user provides a prompt, and the model generates a response. Inputs Text prompts provided by the user Outputs Generated text responses from the model Capabilities WizardLM-33B-V1.0-Uncensored has demonstrated strong performance on a variety of tasks, including the Open LLM Leaderboard, where it scored an average of 54.41 across various benchmarks. The model excels in areas like the AI2 Reasoning Challenge (63.65), HellaSwag (83.84), and Winogrande (77.66). What can I use it for? The WizardLM-33B-V1.0-Uncensored model can be used for a wide range of text-generation tasks, such as content creation, dialogue systems, and language translation. However, it's important to note that this model is "uncensored" and has no built-in guardrails, so users are responsible for the content they generate and publish. Things to try One interesting aspect of WizardLM-33B-V1.0-Uncensored is its potential for further fine-tuning or prompt engineering. By leveraging the model's strong baseline performance, users can explore ways to customize and adapt it for their specific use cases, such as adding alignment or other desired behaviors through techniques like RLHF LoRA.
Updated Invalid Date