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WhiteRabbitNeo 33B V1 GPTQ by TheBloke

 »  All LLMs  »  TheBloke  »  WhiteRabbitNeo 33B V1 GPTQ   URL Share it on

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WhiteRabbitNeo 33B V1 GPTQ is an open-source language model by TheBloke. Features: 33b LLM, VRAM: 17.4GB, Context: 16K, License: other, Quantized, LLM Explorer Score: 0.11.

  4-bit Base model:quantized:whiterabb... Base model:whiterabbitneo/whit...   Gptq   Llama   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/TheBloke/WhiteRabbitNeo-33B-v1-GPTQ 

WhiteRabbitNeo 33B V1 GPTQ Benchmarks

LLME Score: 0.11414
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
WhiteRabbitNeo 33B V1 GPTQ (TheBloke/WhiteRabbitNeo-33B-v1-GPTQ)
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WhiteRabbitNeo 33B V1 GPTQ Parameters and Internals

Model Type 
deepseek
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 33B V1 GPTQ
Repository 🤗https://huggingface.co/TheBloke/WhiteRabbitNeo-33B-v1-GPTQ 
Model NameWhiteRabbitNeo 33B v1
Model CreatorWhiteRabbitNeo
Base Model(s)  whiterabbitneo/WhiteRabbitNeo-33B-v1   whiterabbitneo/WhiteRabbitNeo-33B-v1
Model Size33b
Required VRAM17.4 GB
Updated2026-03-29
MaintainerTheBloke
Model Typedeepseek
Model Files  17.4 GB
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length16384
Model Max Length16384
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<｜end▁of▁sentence｜>
Vocabulary Size32256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/WhiteRabbitNeo-33B-v1-GPTQ.

Rank the WhiteRabbitNeo 33B V1 GPTQ 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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