WhiteRabbitNeo-13B-GGUF
Maintainer: TheBloke
46
👨🏫
| 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
The WhiteRabbitNeo-13B-GGUF is a large language model created by WhiteRabbitNeo and maintained by TheBloke. It is a 13B parameter model that has been quantized into GGUF format, a new open-source format designed to replace GGML which is no longer supported by llama.cpp.
This GGUF version of the model was quantized using hardware from Massed Compute, a company that provides GPU resources. The GGUF format offers numerous advantages over GGML, including better tokenization, support for special tokens, and metadata support.
The WhiteRabbitNeo-13B-GGUF model is similar to other large language models like the neural-chat-7B-v3-1-GGUF and the Llama-2-13B-chat-GGUF in that they are all quantized into the GGUF format and supported by the llama.cpp framework.
Model inputs and outputs
Inputs
- Text: The model accepts text input, which can be in the form of natural language prompts, instructions, or code.
Outputs
- Text: The model generates text output, which can be continuations of the input, translations, summaries, or responses to prompts.
Capabilities
The WhiteRabbitNeo-13B-GGUF model is a powerful text-to-text generation model capable of a wide range of natural language processing tasks. It can be used for tasks like text generation, summarization, translation, and more. The model has been trained on a diverse corpus of data, allowing it to tackle a variety of topics and genres.
What can I use it for?
The WhiteRabbitNeo-13B-GGUF model can be used for a variety of applications, such as:
- Content generation: The model can be used to generate articles, stories, product descriptions, and other types of written content.
- Chatbots and virtual assistants: The model can be used to power conversational AI systems, providing natural language responses to user queries.
- Text summarization: The model can be used to summarize long-form text, such as news articles or research papers, into concise summaries.
- Translation: The model can be used to translate text between different languages.
Things to try
One interesting thing to try with the WhiteRabbitNeo-13B-GGUF model is to experiment with different prompting strategies. By varying the format, tone, and content of the input prompts, you can often elicit quite different responses from the model, highlighting its versatility and flexibility. Additionally, you can try fine-tuning the model on domain-specific data to further enhance its capabilities for specialized use cases.
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
🔄
neural-chat-7B-v3-1-GGUF
56
The neural-chat-7B-v3-1-GGUF model is a 7B parameter autoregressive language model created by TheBloke. It is a quantized version of Intel's Neural Chat 7B v3-1 model, optimized for efficient inference using the new GGUF format. This model can be used for a variety of text generation tasks, with a particular focus on open-ended conversational abilities. Similar models provided by TheBloke include the openchat_3.5-GGUF, a 7B parameter model trained on a mix of public datasets, and the Llama-2-7B-chat-GGUF, a 7B parameter model based on Meta's Llama 2 architecture. All of these models leverage the GGUF format for efficient deployment. Model inputs and outputs Inputs Text prompts**: The model accepts text prompts as input, which it then uses to generate new text. Outputs Generated text**: The model outputs newly generated text, continuing the input prompt in a coherent and contextually relevant manner. Capabilities The neural-chat-7B-v3-1-GGUF model is capable of engaging in open-ended conversations, answering questions, and generating human-like text on a variety of topics. It demonstrates strong language understanding and generation abilities, and can be used for tasks like chatbots, content creation, and language modeling. What can I use it for? This model could be useful for building conversational AI assistants, virtual companions, or creative writing tools. Its capabilities make it well-suited for tasks like: Chatbots and virtual assistants**: The model's conversational abilities allow it to engage in natural dialogue, answer questions, and assist users. Content generation**: The model can be used to generate articles, stories, poems, or other types of written content. Language modeling**: The model's strong text generation abilities make it useful for applications that require understanding and generating human-like language. Things to try One interesting aspect of this model is its ability to engage in open-ended conversation while maintaining a coherent and contextually relevant response. You could try prompting the model with a range of topics, from creative writing prompts to open-ended questions, and see how it responds. Additionally, you could experiment with different techniques for guiding the model's output, such as adjusting the temperature or top-k/top-p sampling parameters.
Updated Invalid Date
🤖
Llama-2-13B-chat-GGUF
185
The Llama-2-13B-chat-GGUF model is a 13 billion parameter large language model created by TheBloke that is optimized for conversational tasks. It is based on Meta's Llama 2 model, which is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. TheBloke has provided GGUF format model files, which is a new format introduced by the llama.cpp team on August 21st 2023 that supersedes the previous GGML format. Similar models provided by TheBloke include the Llama-2-7B-Chat-GGML and Llama-2-13B-GGML models, which use the older GGML format. TheBloke has also provided a range of quantized versions of these models in both GGML and GGUF formats to optimize for performance on different hardware. Model inputs and outputs Inputs Text prompts**: The model accepts text prompts as input, which can include instructions, queries, or any other natural language text. Outputs Generated text**: The model outputs generated text, continuing the input prompt in a coherent and contextual manner. The output can be used for a variety of language generation tasks such as dialogue, story writing, and answering questions. Capabilities The Llama-2-13B-chat-GGUF model is particularly adept at conversational tasks, as it has been fine-tuned by TheBloke specifically for chat applications. It can engage in open-ended dialogues, answer follow-up questions, and provide helpful and informative responses. Compared to open-source chat models, the Llama-2-Chat series from Meta has been shown to outperform on many benchmarks and provide outputs that are on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety. What can I use it for? The Llama-2-13B-chat-GGUF model can be used for a wide variety of language generation tasks, but it is particularly well-suited for building conversational AI assistants and chatbots. Some potential use cases include: Customer service chatbots**: Deploying the model as a virtual customer service agent to handle queries, provide information, and guide users through processes. Intelligent personal assistants**: Integrating the model into smart home devices, productivity apps, or other applications to provide a natural language interface. Dialogue systems**: Building interactive storytelling experiences, roleplaying games, or other applications that require fluent and contextual dialogue. Things to try One interesting aspect of the Llama-2-Chat models is their ability to maintain context and engage in multi-turn dialogues. Try providing the model with a sequence of related prompts and see how it responds, building on the previous context. You can also experiment with different temperature and repetition penalty settings to adjust the creativity and coherence of the generated outputs. Another thing to explore is the model's performance on more specialized tasks, such as code generation, problem-solving, or creative writing. While the Llama-2-Chat models are primarily designed for conversational tasks, they may still demonstrate strong capabilities in these areas due to the breadth of their training data.
Updated Invalid Date
❗
CodeLlama-70B-hf-GGUF
43
The CodeLlama-70B-hf-GGUF is a large language model created by Code Llama and maintained by TheBloke. It is a 70 billion parameter model designed for general code synthesis and understanding tasks. The model is available in several different quantized versions optimized for various tradeoffs between size, speed, and quality using the new GGUF format. Similar models include the CodeLlama-7B-GGUF and CodeLlama-13B-GGUF, which scale the model down to 7 and 13 billion parameters respectively. Model inputs and outputs The CodeLlama-70B-hf-GGUF model takes in text as input and generates text as output. It is designed to be a versatile code generation and understanding tool, capable of tasks like code completion, infilling, and general instruction following. Inputs Text**: The model accepts natural language text prompts as input. Outputs Text**: The model generates natural language text in response to the input prompt. Capabilities The CodeLlama-70B-hf-GGUF model excels at a variety of code-focused tasks. It can generate new code to solve programming problems, complete partially written code, and even translate natural language instructions into functioning code. The model also demonstrates strong code understanding capabilities, making it useful for tasks like code summarization and refactoring. What can I use it for? The CodeLlama-70B-hf-GGUF model could be used in a number of interesting applications. Developers could integrate it into code editors or IDEs to provide intelligent code assistance. Educators could use it to help students learn programming by generating examples and explanations. Researchers might leverage the model's capabilities to advance the field of automated code generation and understanding. And entrepreneurs could explore building commercial products and services around the model's unique abilities. Things to try One interesting thing to try with the CodeLlama-70B-hf-GGUF model is to provide it with partial code snippets and see how it completes or expands upon them. You could also experiment with giving the model natural language descriptions of programming problems and have it generate solutions. Additionally, you might try using the model to summarize or explain existing code, which could be helpful for code review or onboarding new developers to a codebase.
Updated Invalid Date
🖼️
Llama-2-7B-Chat-GGUF
377
The Llama-2-7B-Chat-GGUF model is a 7 billion parameter large language model created by Meta. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are designed for dialogue use cases and have been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align them to human preferences for helpfulness and safety. Compared to open-source chat models, the Llama-2-Chat models outperform on many benchmarks and are on par with some popular closed-source models like ChatGPT and PaLM in human evaluations. The model is maintained by TheBloke, who has generously provided GGUF format versions of the model with various quantization levels to enable efficient CPU and GPU inference. Similar GGUF models are also available for the larger 13B and 70B versions of the Llama 2 model. Model inputs and outputs Inputs Text**: The model takes text prompts as input, which can be anything from a single question to multi-turn conversational exchanges. Outputs Text**: The model generates text continuations in response to the input prompt. This can range from short, concise responses to more verbose, multi-sentence outputs. Capabilities The Llama-2-7B-Chat-GGUF model is capable of engaging in open-ended dialogue, answering questions, and generating text on a wide variety of topics. It demonstrates strong performance on tasks like commonsense reasoning, world knowledge, reading comprehension, and mathematical problem solving. Compared to earlier versions of the Llama model, the Llama 2 chat models also show improved safety and alignment with human preferences. What can I use it for? The Llama-2-7B-Chat-GGUF model can be used for a variety of natural language processing tasks, such as building chatbots, question-answering systems, text summarization tools, and creative writing assistants. Given its strong performance on benchmarks, it could be a good starting point for building more capable AI assistants. The quantized GGUF versions provided by TheBloke also make the model accessible for deployment on a wide range of hardware, from CPUs to GPUs. Things to try One interesting thing to try with the Llama-2-7B-Chat-GGUF model is to engage it in multi-turn dialogues and observe how it maintains context and coherence over the course of a conversation. You could also experiment with providing the model with prompts that require reasoning about hypotheticals or abstract concepts, and see how it responds. Additionally, you could try fine-tuning or further training the model on domain-specific data to see if you can enhance its capabilities for particular applications.
Updated Invalid Date