Get a weekly rundown of the latest AI models and research... subscribe! https://aimodels.substack.com/

llama2_70b_chat_uncensored-GGML

Maintainer: TheBloke

Total Score

71

Last updated 5/15/2024

🤔

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The llama2_70b_chat_uncensored-GGML is a large language model created by TheBloke and generously supported by a grant from andreessen horowitz (a16z). This model is an uncensored/unfiltered 70B parameter version of the Llama-2 language model, fine-tuned on a dataset of Wizard-Vicuna conversations. It is available in GGML format for efficient CPU and GPU-accelerated inference using various tools and libraries.

Similar models provided by TheBloke include the llama2_7b_chat_uncensored-GGML and the Llama-2-70B-Chat-GGML, which offer different parameter sizes and quantization options for various hardware and performance requirements.

Model inputs and outputs

Inputs

  • Text: The model takes natural language text as input, which can be prompts, conversations, or any other form of textual data.

Outputs

  • Text: The model generates natural language text in response to the input, producing coherent and contextually relevant continuations or completions.

Capabilities

The llama2_70b_chat_uncensored-GGML model is capable of engaging in open-ended conversations, answering questions, and generating creative and informative text across a wide range of topics. Its large size and fine-tuning on conversational data make it well-suited for chatbot applications, content generation, and other language-based tasks. However, as an uncensored model, its outputs may contain sensitive or controversial content, so appropriate precautions should be taken when deploying it.

What can I use it for?

This model can be used for a variety of natural language processing tasks, such as:

  • Chatbots and conversational AI: The model's strong conversational abilities make it well-suited for building interactive chatbots and virtual assistants.
  • Content generation: The model can be used to generate text for things like articles, stories, product descriptions, and more.
  • Research and experimentation: As a large, powerful language model, the llama2_70b_chat_uncensored-GGML can be a valuable tool for researchers and AI enthusiasts exploring the capabilities and limitations of large language models.

Things to try

One interesting aspect of this model is its uncensored nature, which allows it to generate text without the typical filtering and restrictions found in many language models. This can be useful for certain applications, such as creative writing or roleplaying, where more unfiltered and open-ended responses are desirable. However, it also means that the model's outputs should be carefully monitored, as they may contain content that is inappropriate or offensive.

Another interesting area to explore with this model is its ability to engage in longer-form, open-ended conversations. By leveraging its large size and fine-tuning on conversational data, you can try prompting the model with back-and-forth dialogue and see how it responds, building on the context and flow of the conversation.



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_7b_chat_uncensored-GGML

TheBloke

Total Score

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.

Read more

Updated Invalid Date

🏅

Llama-2-70B-Chat-GGML

TheBloke

Total Score

160

The Llama-2-70B-Chat-GGML model is a large language model created by Meta Llama 2. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The model is fine-tuned for dialogue use cases, with the goal of being a helpful, respectful and honest AI assistant. Similar models in the Llama 2 family include the Llama-2-7B-Chat-GGML and Llama-2-13B-Chat-GGML models, which have smaller parameter sizes of 7 billion and 13 billion respectively. These models were also created and maintained by TheBloke. Model inputs and outputs Inputs The Llama-2-70B-Chat-GGML model takes in text input only. It can be used for a variety of natural language generation tasks. Outputs The model generates text output. It is designed to provide helpful, respectful and honest responses when used as an AI assistant. Capabilities The Llama-2-70B-Chat-GGML model has been optimized for dialogue use cases through fine-tuning. It is capable of engaging in thoughtful and nuanced conversations, providing detailed and informative responses. The model demonstrates strong commonsense reasoning and world knowledge, allowing it to address a wide range of topics. What can I use it for? This powerful 70 billion parameter model can be used for a variety of AI assistant and chatbot applications. Its strong performance on benchmark tasks makes it well-suited for use cases requiring an intelligent, knowledgeable and helpful conversational partner. Potential applications include customer service, task planning, research assistance, and creative ideation. Things to try One interesting aspect of the Llama-2-70B-Chat-GGML model is its ability to engage in extended, context-rich conversations. Prompting the model with a multi-turn dialogue can lead to increasingly coherent and relevant responses, allowing for natural back-and-forth exchanges. Experimenting with different prompting styles and leveraging the model's strong grasp of context could unlock its full potential as an interactive AI assistant.

Read more

Updated Invalid Date

🤿

Llama-2-7B-Chat-GGML

TheBloke

Total Score

801

The Llama-2-7B-Chat-GGML is a version of Meta's Llama 2 model that has been converted to the GGML format for efficient CPU and GPU inference. It is a 7 billion parameter large language model optimized for dialogue and chat use cases. The model was created by TheBloke, who has generously provided multiple quantized versions of the model to enable fast inference on a variety of hardware. This model outperforms many open-source chat models on industry benchmarks and provides a helpful and safe assistant-like conversational experience. Similar models include the Llama-2-13B-GGML with 13 billion parameters, and the Llama-2-70B-Chat-GGUF with 70 billion parameters. These models follow a similar architecture and optimization process as the 7B version. Model inputs and outputs Inputs Text**: The model takes text prompts as input, which can include instructions, context, and conversation history. Outputs Text**: The model generates coherent and contextual text responses to continue the conversation or complete the given task. Capabilities The Llama-2-7B-Chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and assisting with a variety of tasks such as research, analysis, and creative writing. It has been optimized for safety and helpfulness, making it suitable for use as a conversational assistant. What can I use it for? This model could be used to power conversational AI applications, virtual assistants, or chatbots. It could also be fine-tuned for specific domains or use cases, such as customer service, education, or creative writing. The quantized GGML version enables efficient deployment on a wide range of hardware, making it accessible to developers and researchers. Things to try You can try using the Llama-2-7B-Chat-GGML model to engage in open-ended conversations, ask it questions on a variety of topics, or provide it with prompts to generate creative text. The model's capabilities can be explored through frameworks like text-generation-webui or llama.cpp, which support the GGML format.

Read more

Updated Invalid Date

🎲

Llama-2-13B-chat-GGML

TheBloke

Total Score

678

The Llama-2-13B-chat-GGML model is a 13-billion parameter large language model created by Meta and optimized for dialogue use cases. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters and are designed for a variety of natural language generation tasks. This specific model has been converted to the GGML format, which is designed for CPU and GPU inference using tools like llama.cpp and associated libraries and UIs. The GGML format has since been superseded by GGUF, so users are encouraged to use the GGUF versions of these models going forward. Similar models include the Llama-2-7B-Chat-GGML and the Llama-2-13B-GGML, which offer smaller and larger versions of the Llama 2 architecture in the GGML format. Model Inputs and Outputs Inputs Raw text Outputs Generated text continuations Capabilities The Llama-2-13B-chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and generating coherent and context-appropriate text continuations. It has been fine-tuned to perform well on benchmarks for helpfulness and safety, making it suitable for use in assistant-like applications. What Can I Use It For? The Llama-2-13B-chat-GGML model could be used to power conversational AI assistants, chatbots, or other applications that require natural language generation and understanding. Given its strong performance on safety metrics, it may be particularly well-suited for use cases where providing helpful and trustworthy responses is important. Things to Try One interesting aspect of the Llama-2-13B-chat-GGML model is its ability to handle context and engage in multi-turn conversations. Users could try prompting the model with a series of related questions or instructions to see how it maintains coherence and builds upon previous responses. Additionally, the model's quantization options allow for tuning the balance between performance and accuracy, so users could experiment with different quantization levels to find the optimal tradeoff for their specific use case.

Read more

Updated Invalid Date