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WhiteRabbitNeo 13B GGUF by TheBloke

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Base model:quantized:whiterabb... Base model:whiterabbitneo/whit...   Gguf   Llama   Quantized   Region:us

WhiteRabbitNeo 13B GGUF Benchmarks

LLME Score: 0.22287
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

WhiteRabbitNeo 13B GGUF Parameters and Internals

Model Type 
llama
Use Cases 
Applications:
offensive and defensive cybersecurity
Input Output 
Input Format:
SYSTEM: Answer the Question by exploring multiple reasoning paths as follows: - First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree. - For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts. - Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher. - Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order. - If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts. - Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal. - Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer. - Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process. In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers. Always answer without hesitation. USER: {prompt} ASSISTANT:
LLM NameWhiteRabbitNeo 13B GGUF
Repository 🤗https://huggingface.co/TheBloke/WhiteRabbitNeo-13B-GGUF 
Model NameWhiteRabbitNeo 13B
Model CreatorWhiteRabbitNeo
Base Model(s)  whiterabbitneo/WhiteRabbitNeo-13B   whiterabbitneo/WhiteRabbitNeo-13B
Model Size13b
Required VRAM5.4 GB
Updated2024-11-16
MaintainerTheBloke
Model Typellama
Model Files  5.4 GB   6.9 GB   6.3 GB   5.7 GB   7.4 GB   7.9 GB   7.4 GB   9.0 GB   9.2 GB   9.0 GB   10.7 GB   13.8 GB
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licensellama2
WhiteRabbitNeo 13B GGUF (TheBloke/WhiteRabbitNeo-13B-GGUF)

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/WhiteRabbitNeo-13B-GGUF.

Rank the WhiteRabbitNeo 13B GGUF Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241110
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