What are the hardware requirements for WhiteRabbitNeo 13B V1?

WhiteRabbitNeo 13B V1 requires approximately 26 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed WhiteRabbitNeo 13B V1 and how large is it?

WhiteRabbitNeo 13B V1 is developed by whiterabbitneo, a model with 13b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does WhiteRabbitNeo 13B V1 perform on standard benchmarks?

WhiteRabbitNeo 13B V1 has the following published scores: MMLU 43.04. Compare against reference models on this page or on the LLM Explorer leaderboards.

WhiteRabbitNeo 13B V1 by whiterabbitneo — VRAM 26GB, 16K context

Name: WhiteRabbitNeo 13B V1
Author: whiterabbitneo

WhiteRabbitNeo 13B V1 is an open-source language model by whiterabbitneo. Features: 13b LLM, VRAM: 26GB, Context: 16K, License: llama2, Quantized, Code Generating, LLM Explorer Score: 0.18, Arc: 48.6, HellaSwag: 68.7, MMLU: 43, GSM8K: 22.4.

Codegen Custom code Endpoints compatible Fp16 Llama Pytorch Quantized Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-13B-v1

WhiteRabbitNeo 13B V1 Benchmarks

ARC: 48.55 vs 96.7 (so35)^-49.8%

HellaSwag: 68.7 vs 95.3 (gpt4)^-27.9%

MMLU: 43.04 vs 88.3 (so35)^-51.3%

GSM8K: 22.37 vs 96.4 (so35)^-76.8%

LLME Score: 0.17962

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

What is the LLM Explorer Rank (Score)

WhiteRabbitNeo 13B V1 (WhiteRabbitNeo/WhiteRabbitNeo-13B-v1)

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WhiteRabbitNeo 13B V1 Parameters and Internals

Model Type

Cybersecurity, Text generation

Additional Notes

The model is being released as a public preview to assess its societal impact, particularly in offensive and defensive cybersecurity.

LLM Name	WhiteRabbitNeo 13B V1
Repository 🤗	https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-13B-v1
Model Size	13b
Required VRAM	26 GB
Updated	2026-05-19
Maintainer	whiterabbitneo
Model Type	llama
Model Files	5.0 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 1.2 GB: 6-of-6
Quantization Type	fp16
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	16384
Model Max Length	16384
Transformers Version	4.36.0.dev0
Tokenizer Class	CodeLlamaTokenizer
Padding Token	</s>
Vocabulary Size	32016
Torch Data Type	bfloat16

Quantized Models of the WhiteRabbitNeo 13B V1

Model	Likes	Downloads	VRAM
WhiteRabbitNeo 13B V1 GGUF	0	856	4 GB

Best Alternatives to WhiteRabbitNeo 13B V1

Best Alternatives	Context / RAM	Downloads	Likes
CodeLlama 13B Python Fp16	16K / 26 GB	466	25
CodeLlama 13B Instruct Fp16	16K / 26 GB	479	28
CodeLlama 13B Fp16	16K / 26 GB	27	67
...Llama 13B Instruct Hf 4bit MLX	16K / 7.8 GB	223	3
Codellama 13B Bnb 4bit	16K / 7.2 GB	88	5
Trinity 13B	16K / 26 GB	15	15
Trinity 13B 6.0bpw H6 EXL2	16K / 10 GB	10	2
Trinity 13B 5.0bpw H6 EXL2	16K / 8.4 GB	3	1
Trinity 13B 3.0bpw H6 EXL2	16K / 5.2 GB	2	1
Trinity 13B 4.0bpw H6 EXL2	16K / 6.8 GB	2	1

Rank the WhiteRabbitNeo 13B V1 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20260328a

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WhiteRabbitNeo 13B V1 by whiterabbitneo