CybersecurityCode

WhiteRabbitNeo / DeepHat

One of the best-known open families for security and DevSecOps work: reviewing code for weaknesses, test scenarios, explaining attacks.

Developer
Kindo, USA
First release
Dec 2023
Latest release
Apr 2025
Sizes
7B – 70B
License
Commercial use with conditionsmixed: later versions are Apache 2.0 with a usage-restriction addendum, earlier ones use the Llama 2 and Llama 3.1 licenses
Russian
Not supported
Ready-made builds
GGUF, 4-bit, MLX (Apple)
Running
On your own serverNeeds a GPU
Industries
Security, Software development
Deploy on your server Hugging Face

What it does

  • Finding weak spots in code and configurations
  • Reviewing incidents and explaining attack techniques
  • Drafting scripts and procedures for the security team

Where it is used

Information security teamsDevelopment and DevOpsSecurity audits

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
fits

Versions

  1. DeepHat V1 7B (бывш. WhiteRabbitNeo V3)
  2. WhiteRabbitNeo 2.5 на Qwen2.5-Coder 7B
  3. Llama-3.1 WhiteRabbitNeo 2 8B и 70B
  4. Llama-3 WhiteRabbitNeo 8B v2.0
  5. WhiteRabbitNeo 7B v1.5a и 33B v1.5
  6. WhiteRabbitNeo 13B и 33B v1

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems. Quantization compresses a model so it takes less video memory and runs on more modest hardware. Answers change slightly, so quality is checked on your own examples.

Frequently asked questions

Can WhiteRabbitNeo / DeepHat be used in a commercial project?

With conditions. License: mixed: later versions are Apache 2.0 with a usage-restriction addendum, earlier ones use the Llama 2 and Llama 3.1 licenses. Restrictions vary — region, company revenue, attribution requirements. Have a lawyer check the terms before a commercial launch.

What hardware does WhiteRabbitNeo / DeepHat need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Without a GPU the model is not practical. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does WhiteRabbitNeo / DeepHat support Russian?

No. The developer documentation lists the supported languages and Russian is not among them.

Where can I download WhiteRabbitNeo / DeepHat and what does it cost?

The WhiteRabbitNeo / DeepHat weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

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Details

Source: huggingface.co/DeepHat/DeepHat-V1-7B. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.