Instructions to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit
Run Hermes
hermes
- Atomic Chat
WhiteRabbitNeo-V3-7B — MLX (8-bit)
An 8-bit MLX quantization of WhiteRabbitNeo/WhiteRabbitNeo-V3-7B, Kindo's open-weight DevSecOps model, packaged for fast local inference on Apple Silicon via MLX.
At a glance
| Base model | WhiteRabbitNeo/WhiteRabbitNeo-V3-7B |
| Base architecture | Qwen 2.5 Coder 7B |
| Parameters | ~7.6B |
| Quantization | 8-bit (group size 64, ~8.5 bits/weight) |
| Format | MLX |
| Prompt format | ChatML |
| Specialization | Offensive & defensive cybersecurity / DevSecOps |
What this model is
WhiteRabbitNeo V3 is a DevSecOps-focused model from Kindo, fine-tuned from Qwen 2.5 Coder on a large corpus of security Q&A spanning web security, malware analysis, infrastructure-as-code, vulnerability databases, and threat intelligence. It is a minimally-restricted, offensive-capable model designed to assist with security tasks other assistants tend to refuse — vulnerability discovery, exploit reasoning, tooling, and remediation.
Conversion details
A straight quantization of the base weights — no architectural changes, no merges, no fine-tuning.
- Tool: mlx-lm v0.31.3
- Command:
mlx_lm.convert --hf-path <base> --mlx-path <out> -q --q-bits 8 - Quantization: 8-bit, group size 64 (~8.5 bpw)
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("tthoman79/WhiteRabbitNeo-V3-7B-mlx-8bit")
system = (
"You are WhiteRabbitNeo, a cybersecurity-expert AI model. "
"You are an expert in DevOps and Cybersecurity tasks. "
"Whenever you answer with code, format it with code blocks."
)
messages = [
{"role": "system", "content": system},
{"role": "user", "content": "Write a bash script to audit a Linux host for world-writable files."},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=text, max_tokens=1024, verbose=True)
The model uses the ChatML format and is tuned to operate under a DevSecOps expert system prompt like the one above.
Responsible use & license
This model inherits its license from the base model: Apache 2.0, together with the WhiteRabbitNeo "Extension to Apache-2.0" usage restrictions. Those restrictions prohibit, among other things, using the model or any derivative of it in violation of applicable law or in ways that infringe the rights of others — and they explicitly extend to derivatives, so they apply to this MLX conversion. The original model card holds the complete and authoritative restriction list; review it before use.
This is an offensive-capable security model. Use it only against systems you own or are explicitly authorized to test, for legitimate security research, education, and defense.
Attribution
Model by WhiteRabbitNeo / Kindo, fine-tuned from Qwen 2.5 Coder 7B. This repository provides only an MLX-format quantization; see the original model card for full details.
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