llama2_7b_chat_uncensored-GGML
Maintainer: TheBloke
114
🛠️
| 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 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.
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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