[Models](/models) / [CodeLlama 13B](/models?family=CodeLlama%2013B#models-gallery) / WhiteRabbitNeo 13B v1 ![Meta logo](/model-logos/meta.svg) # Run WhiteRabbitNeo 13B v1 on Mac WhiteRabbitNeo 13B v1 runs 100% private on Mac inside Private LLM — no internet connection required, no data sent to any server. 13BMac ## Download the quantized weights These are the exact OmniQuant weights Private LLM runs for WhiteRabbitNeo 13B v1, published on our Hugging Face org. They're standard weights you can load in any app that supports the format, not just Private LLM. - [Mac — 4-bit OmniQuant →](https://huggingface.co/numen-tech/WhiteRabbitNeo-13B-v1-w4a16g128asym) ## Specifications Parameters 13B Context window 16K tokens License Llama 2 Quantization OmniQuant 4-bit Family CodeLlama 13B ## What WhiteRabbitNeo 13B v1 is good at [coding](/models/coding) WhiteRabbitNeo is a 13 billion parameter AI model designed for offensive and defensive cybersecurity tasks. It can help with activities like network scanning, password cracking, and exploitation, but it is intended only for legal and ethical use. The model is released as a public preview to test its capabilities and understand its societal impact. ## Which of your devices can run it ### Mac Mac (Apple Silicon, 192GB)MacBook Pro (M4 Max, 128GB)Mac Studio / Pro (Apple Silicon, 96GB)MacBook Pro (M4 Max, 64GB)MacBook Pro (M4 Max, 48GB)MacBook Pro (M4 Max, 36GB)Mac (Apple Silicon, 32GB)MacBook Air (M4, 24GB)MacBook Air (M-series, 16GB) Browse every model that fits [iPhone](/models/best-for-iphone) or [Mac](/models/best-for-mac). ## How to run WhiteRabbitNeo 13B v1 in Private LLM 1. Download Private LLM from the App Store. 2. Open the in-app model library and choose WhiteRabbitNeo 13B v1. 3. Download the model once, then chat fully offline. [![Download Private LLM on the App Store](/app-store/download-badge/en/download.svg)![Download Private LLM on the App Store](/app-store/download-badge/en/download-dark.svg)](/download)[Join our Discord](/discord) ## Variants & related models ![Mistral logo](/model-logos/mistral.svg) ### CodeNinja 1.0 OpenChat 7B 7.2B coding 8K contextiPhone & iPad · Mac [View details →](/models/codeninja-1.0-openchat-7b) ![Meta logo](/model-logos/meta.svg) ### Llama 3 WhiteRabbitNeo 8B v2.0 8B coding 8K contextiPhone & iPad · Mac [View details →](/models/llama-3-whiterabbitneo-8b-v2.0) ![Qwen logo](/model-logos/qwen.svg) ### OpenHands LM 32B v0.1 32.8B coding 33K contextMac [View details →](/models/openhands-lm-32b-v0.1) ![Qwen logo](/model-logos/qwen.svg) ### OpenHands LM 7B v0.1 7.6B coding 33K contextiPhone & iPad [View details →](/models/openhands-lm-7b-v0.1) ![Qwen logo](/model-logos/qwen.svg) ### Qwen 2.5 Coder 0.5B Unquantized 0.5B coding 33K contextiPhone & iPad · Mac [View details →](/models/qwen2.5-coder-0.5b-instruct) ![Qwen logo](/model-logos/qwen.svg) ### Qwen 2.5 Coder 1.5B 1.5B coding 33K contextiPhone & iPad · Mac [View details →](/models/qwen2.5-coder-1.5b-instruct) ## Frequently asked questions - Can I run WhiteRabbitNeo 13B v1 on iPhone? WhiteRabbitNeo 13B v1 is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline. - Can I run WhiteRabbitNeo 13B v1 on Mac? Yes. WhiteRabbitNeo 13B v1 runs on Macs with enough unified memory, such as Mac (Apple Silicon, 192GB), MacBook Pro (M4 Max, 128GB), Mac Studio / Pro (Apple Silicon, 96GB), MacBook Pro (M4 Max, 64GB), MacBook Pro (M4 Max, 48GB), MacBook Pro (M4 Max, 36GB), Mac (Apple Silicon, 32GB), MacBook Air (M4, 24GB), MacBook Air (M-series, 16GB), fully on-device in Private LLM. - Does WhiteRabbitNeo 13B v1 work offline? Yes. Once downloaded in Private LLM, WhiteRabbitNeo 13B v1 runs 100% on-device — no internet connection, and nothing is sent to any server. - Is WhiteRabbitNeo 13B v1 free to use? Private LLM is a one-time purchase with no subscription and no per-message cost. The models themselves are open source — once downloaded, they run offline with nothing to pay per use. ## Why run WhiteRabbitNeo 13B v1 in Private LLM Private LLM has run local AI on iPhone, iPad, and Mac since 2023, before Apple Intelligence existed. Inference happens on your device, so your WhiteRabbitNeo 13B v1 conversations never reach a server. The part most apps gloss over is quantization, and that is exactly where on-device quality is won or lost. Most llama.cpp and MLX wrappers ship the same off-the-shelf 4-bit RTN weights. Private LLM ships GPTQ and OmniQuant quantization, tuned per model, and our 3-bit OmniQuant models match or beat those 4-bit RTN builds on the same Apple Silicon. Run the same model both ways and you feel it in the first reply. [See how our quantization works](/en/faq#Why-can-t-Private-LLM-load-models-directly-from-Hugging-Face). ### More from Numen [![Slop or Not app icon](/app-icons/slop-or-not.png) #### Slop or Not Catch AI-written text on iPhone and Mac. The check runs on-device, so nothing you paste gets uploaded. ](https://slopornot.ai)[![Clean Links app icon](/app-icons/clean-links.png) #### Clean Links Strip trackers and clutter from any link before you share it. The cleanup happens on your device. ](https://cleanlinks.app) Specifications and summary come from WhiteRabbitNeo 13B v1's [Hugging Face model card](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-13B-v1), released under the Llama 2 license. Private LLM ships its own quantized models, built with OmniQuant quantization tuned per model, and isn't affiliated with the model's authors.