llama2_7b_chat_uncensored
Maintainer: georgesung
327
✅
| 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
llama2_7b_chat_uncensored is a fine-tuned version of the Llama-2 7B model, created by George Sung. The model was fine-tuned on an uncensored/unfiltered Wizard-Vicuna conversation dataset ehartford/wizard_vicuna_70k_unfiltered using QLoRA. It was trained for one epoch on a 24GB GPU instance, taking around 19 hours.
The model is available in a fp16 format on the Hugging Face platform. Bloke has also created GGML and GPTQ versions of the model for improved performance and lower resource usage on llama2_7b_chat_uncensored-GGML and llama2_7b_chat_uncensored-GPTQ respectively.
Model inputs and outputs
Inputs
- Text prompts: The model is designed to accept text prompts in a conversational style, with the prompt structured as a human-response dialog.
Outputs
- Text responses: The model generates coherent and relevant text responses based on the provided prompt.
Capabilities
The llama2_7b_chat_uncensored model demonstrates strong conversational abilities, providing natural and informative responses to a wide range of prompts. It excels at engaging in open-ended discussions, answering questions, and generating text in a conversational style.
What can I use it for?
This model can be useful for building conversational AI assistants, chatbots, or interactive storytelling applications. Its uncensored nature and focus on open-ended conversation make it well-suited for applications where a more natural, unfiltered dialogue is desired, such as creative writing, roleplay, or exploring complex topics.
Things to try
One interesting aspect of this model is its approach to handling potentially sensitive topics or language. Unlike some models that attempt to censor or sanitize user input, the llama2_7b_chat_uncensored model provides direct and matter-of-fact responses without making assumptions about the user's intent or morality. This can lead to thought-provoking discussions about the role of AI in navigating complex social and ethical considerations.
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
🌿
llama2_70b_chat_uncensored
66
The llama2_70b_chat_uncensored is a fine-tuned version of the Llama-2 70B model, created by jarradh. It was fine-tuned using an uncensored/unfiltered Wizard-Vicuna conversation dataset with the QLoRA technique, trained for three epochs on a single NVIDIA A100 80GB GPU instance. This model is designed to provide more direct and uncensored responses compared to the standard Llama-2 models. Similar models include the Wizard-Vicuna-13B-Uncensored-GPTQ and Wizard-Vicuna-30B-Uncensored-GPTQ from TheBloke, which also provide uncensored versions of Wizard-Vicuna models. Model inputs and outputs Inputs Text prompts**: The model accepts text prompts as input, which it then uses to generate relevant responses. Outputs Generated text**: The model outputs generated text, which can be responses to the input prompts. Capabilities The llama2_70b_chat_uncensored model is designed to provide more direct and uncensored responses compared to standard Llama-2 models. For example, when asked "What is a poop?", the uncensored model provides a straightforward answer, while the standard Llama-2 model responds with a more cautious and sanitized explanation. What can I use it for? This model could be useful for applications that require more natural and unfiltered language, such as creative writing, dialogue generation, or conversational AI systems. However, it's important to note that the model has no guardrails, so the content it generates must be carefully monitored and moderated. Things to try One interesting thing to try with this model is to compare its responses to those of the standard Llama-2 models on a variety of prompts, particularly those that touch on sensitive or controversial topics. This can help illustrate the differences in approach and the potential tradeoffs involved in using an uncensored model.
Updated Invalid Date
📈
llama2_7b_chat_uncensored-GPTQ
65
The llama2_7b_chat_uncensored-GPTQ model is a quantized version of George Sung's Llama2 7B Chat Uncensored model. It was created by TheBloke and provides multiple GPTQ parameter options to choose from based on your hardware and performance requirements. This contrasts with similar models like the Llama-2-7b-Chat-GPTQ which is a quantized version of Meta's Llama 2 7B Chat model. Model inputs and outputs The llama2_7b_chat_uncensored-GPTQ model is a text-to-text model that takes prompts as input and generates text responses. The model was fine-tuned on an uncensored conversation dataset to enable open-ended chatting without built-in alignment or safety constraints. Inputs Prompts**: Free-form text prompts to initiate a conversation Outputs Responses**: Coherent, context-aware text generated in response to the input prompt Capabilities The llama2_7b_chat_uncensored-GPTQ model is capable of engaging in open-ended dialogue on a wide range of topics. It can provide helpful information, generate creative ideas, and have thoughtful discussions, while avoiding harmful or biased content. However, as an uncensored model, it may also produce responses that are inappropriate or offensive. What can I use it for? The llama2_7b_chat_uncensored-GPTQ model could be used to power conversational AI applications, chatbots, or creative writing assistants. Developers could fine-tune or prompt the model further to specialize it for particular use cases. Potential applications include customer service, personal assistance, language learning, and creative ideation. Things to try Try prompting the model with open-ended questions or statements to see the range of responses it can generate. You could also experiment with different prompting techniques, such as role-playing or providing additional context, to elicit more nuanced or creative outputs. Just be mindful that as an uncensored model, the responses may contain inappropriate content.
Updated Invalid Date
🛠️
llama2_7b_chat_uncensored-GGML
114
The llama2_7b_chat_uncensored-GGML model is a large language model created by George Sung and maintained by TheBloke. It is a 7 billion parameter version of the Llama 2 family of models, fine-tuned for open-ended dialogue and chat scenarios. This model is available in GGML format, which allows for CPU and GPU acceleration using tools like llama.cpp and text-generation-webui. Similar models maintained by TheBloke include the Llama-2-7B-Chat-GGML, Llama-2-13B-chat-GGML, and Llama-2-70B-Chat-GGML models, which provide different parameter sizes and quantization options for various performance and resource tradeoffs. Model inputs and outputs Inputs Text**: The model takes in text input, which can be in the form of chat messages, prompts, or other natural language. Outputs Text**: The model generates text outputs, producing responses to the input text. The outputs are intended to engage in open-ended dialogue and conversations. Capabilities The llama2_7b_chat_uncensored-GGML model is capable of engaging in natural language conversations on a wide range of topics. It can understand context, respond coherently, and demonstrate knowledge across many domains. The model has been fine-tuned to prioritize helpful, respectful, and honest responses, while avoiding harmful, unethical, or biased content. What can I use it for? This model can be used for a variety of applications that require open-ended language generation and dialogue, such as: Virtual assistant**: Integrate the model into a virtual assistant application to provide users with a conversational interface for tasks like answering questions, providing recommendations, or offering emotional support. Chatbots**: Deploy the model as a chatbot on messaging platforms, websites, or social media to enable natural language interactions with customers or users. Creative writing**: Use the model to generate creative stories, dialogues, or other forms of text by providing it with prompts or starting points. Educational applications**: Incorporate the model into learning platforms or tutoring systems to enable interactive learning experiences. Things to try One interesting aspect of this model is its ability to engage in extended, multi-turn conversations. Try providing the model with a conversational prompt and see how it responds, then continue the dialogue by building on its previous responses. This can showcase the model's contextual understanding and its capacity for engaging in coherent, back-and-forth discussions. Another interesting exploration is to try providing the model with prompts or scenarios that test its ability to respond helpfully and ethically. Observe how the model handles these types of requests and evaluate its ability to avoid harmful or biased outputs.
Updated Invalid Date
🔍
llama2_70b_chat_uncensored-GPTQ
57
The llama2_70b_chat_uncensored-GPTQ is a large language model based on the Meta Llama 2 architecture, fine-tuned by Jarrad Hope on an uncensored/unfiltered Wizard-Vicuna conversation dataset. It was created as a response to the overly cautious and sanitized responses from the standard Llama 2 Chat model. Compared to similar models like the llama2_7b_chat_uncensored-GPTQ and the Llama-2-70B-Chat-GPTQ, this 70B parameter model provides more capability and flexibility. It is available in a variety of quantized versions to suit different hardware requirements. Model inputs and outputs Inputs Text**: The model takes freeform text input and generates a response. Outputs Text**: The model generates coherent, contextually-appropriate text responses. Capabilities The llama2_70b_chat_uncensored-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and generating text on a wide range of topics. It demonstrates improved performance over the standard Llama 2 Chat model, providing more direct and unfiltered responses. What can I use it for? This model could be useful for applications that require more natural, less constrained language generation, such as creative writing assistants, Q&A chatbots, or open-domain dialogue systems. However, due to the uncensored nature of the training data, extra care should be taken when deploying this model in production to monitor for potentially harmful outputs. Things to try One key difference with this model is its willingness to use colloquial language like "poop" instead of more formal terminology. This can make the responses feel more authentic and relatable in certain contexts. Experiment with different prompts and tones to see how the model adapts its language accordingly.
Updated Invalid Date