What are the hardware requirements for WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2?

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 requires approximately 17.1 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 33B V1.4.0bpw H6 EXL2 and how large is it?

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 is developed by LoneStriker, a model with 33b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2?

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 by LoneStriker — VRAM 17.1GB, 16K context

Name: WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2
Author: LoneStriker

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 is an open-source language model by LoneStriker. Features: 33b LLM, VRAM: 17.1GB, Context: 16K, License: other, Quantized, Code Generating, LLM Explorer Score: 0.1.

Codegen Endpoints compatible Exl2 Llama Pytorch Quantized Region:us Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/WhiteRabbitNeo-33B-v1-4.0bpw-h6-exl2

WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 Parameters and Internals

Model Type

cybersecurity, offensive, defensive

Use Cases

Areas:

cybersecurity

Primary Use Cases:

offensive and defensive cybersecurity, vulnerability assessment

Limitations:

Not intended for military use, Cannot be used to exploit vulnerabilities based on personal characteristics

Considerations:

Users must adhere to ethical guidelines while using the model.

Additional Notes

The model is a public preview to assess its societal impact.

LLM Name	WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/WhiteRabbitNeo-33B-v1-4.0bpw-h6-exl2
Model Size	33b
Required VRAM	17.1 GB
Updated	2026-08-04
Maintainer	LoneStriker
Model Type	llama
Model Files	8.6 GB: 1-of-2 8.5 GB: 2-of-2
Quantization Type	exl2
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	other
Context Length	16384
Model Max Length	16384
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<｜end▁of▁sentence｜>
Vocabulary Size	32256
Torch Data Type	bfloat16

Rank the WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to WhiteRabbitNeo 33B V1.4.0bpw H6 EXL2

Best Alternatives	Context / RAM	Downloads	Likes
...erpreter DS 33B 4.0bpw H6 EXL2	16K / 17.1 GB	8	4
...rpreter DS 33B 4.65bpw H6 EXL2	16K / 19.8 GB	5	3
...erpreter DS 33B 5.0bpw H6 EXL2	16K / 21.2 GB	6	1
...erpreter DS 33B 6.0bpw H6 EXL2	16K / 25.3 GB	8	0
...erpreter DS 33B 8.0bpw H8 EXL2	16K / 33.5 GB	5	1
...der 33B V2 Base 8.0bpw H8 EXL2	16K / 33.5 GB	1	1
... Coder 33B Base 6.0bpw H6 EXL2	16K / 25.3 GB	3	1
...Coder 33B Base 4.65bpw H6 EXL2	16K / 19.8 GB	1	1
...er 33B Instruct 4.0bpw H6 EXL2	16K / 17.1 GB	5	5
...er 33B Instruct 8.0bpw H8 EXL2	16K / 33.6 GB	6	3

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/WhiteRabbitNeo-33B-v1-4.0bpw-h6-exl2.