Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

opena2a
/
nanomind-security-analyst-mlx

Text Generation
MLX
Safetensors
English
qwen3
apple-silicon
security
ai-agent-security
nanomind
conversational
8-bit precision
Model card Files Files and versions
xet
Community

Instructions to use opena2a/nanomind-security-analyst-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use opena2a/nanomind-security-analyst-mlx 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("opena2a/nanomind-security-analyst-mlx")
    
    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 opena2a/nanomind-security-analyst-mlx with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "opena2a/nanomind-security-analyst-mlx"
    Configure the model in Pi
    # Install Pi:
    npm install -g @earendil-works/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "mlx-lm": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "opena2a/nanomind-security-analyst-mlx"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • MLX LM

    How to use opena2a/nanomind-security-analyst-mlx with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "opena2a/nanomind-security-analyst-mlx"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "opena2a/nanomind-security-analyst-mlx"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "opena2a/nanomind-security-analyst-mlx",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
  • Hermes Agent

    How to use opena2a/nanomind-security-analyst-mlx 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 "opena2a/nanomind-security-analyst-mlx"
    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 opena2a/nanomind-security-analyst-mlx
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use opena2a/nanomind-security-analyst-mlx with OpenClaw:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "opena2a/nanomind-security-analyst-mlx"
    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 "opena2a/nanomind-security-analyst-mlx" \
      --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"

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Gated model
You can list files but not access them

Preview of files found in this repository
  • .gitattributes
    1.57 kB
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • README.md
    4.78 kB
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • chat_template.jinja
    4.17 kB
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • config.json
    1.02 kB
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • generation_config.json
    239 Bytes
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • model.safetensors
    1.83 GB
    xet
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • model.safetensors.index.json
    49.8 kB
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • tokenizer.json
    11.4 MB
    xet
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago
  • tokenizer_config.json
    700 Bytes
    Add NanoMind Security Analyst MLX 8-bit (Metal-stable Apple Silicon build) 4 months ago