Instructions to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 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("ahmedandaloes/security-llama3.2-3b-MLX-bf16")

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

How to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ahmedandaloes/security-llama3.2-3b-MLX-bf16"

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": "ahmedandaloes/security-llama3.2-3b-MLX-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ahmedandaloes/security-llama3.2-3b-MLX-bf16"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ahmedandaloes/security-llama3.2-3b-MLX-bf16"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ahmedandaloes/security-llama3.2-3b-MLX-bf16",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 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 "ahmedandaloes/security-llama3.2-3b-MLX-bf16"

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 ahmedandaloes/security-llama3.2-3b-MLX-bf16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ahmedandaloes/security-llama3.2-3b-MLX-bf16 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ahmedandaloes/security-llama3.2-3b-MLX-bf16"

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 "ahmedandaloes/security-llama3.2-3b-MLX-bf16" \
  --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"

security-llama3.2-3b — MLX bf16

Full-precision (bf16) MLX build of viettelsecurity-ai/security-llama3.2-3b, for fast local inference on Apple Silicon.

Precision: bf16 — no quantization, identical weights to source.
Weights unchanged from source — format + precision conversion only.
Converted with mlx-lm.

Builds

Build
MLX-4bit
MLX-6bit
MLX-8bit
MLX-bf16

GGUF builds: prithivMLmods/Security-Llama3.2-3B-GGUF.

Usage

pip install mlx-lm

from mlx_lm import load, generate
model, tok = load("ahmedandaloes/security-llama3.2-3b-MLX-bf16")
p = tok.apply_chat_template([{"role":"user","content":"Name a common web vulnerability."}], add_generation_prompt=True)
print(generate(model, tok, prompt=p, max_tokens=200, verbose=True))

Attribution

Source: viettelsecurity-ai/security-llama3.2-3b. License per source (Apache-2.0 assumed; verify). MLX build for the Apple Silicon community. For authorized security work only.

Downloads last month: 37

Safetensors

Model size

3B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for ahmedandaloes/security-llama3.2-3b-MLX-bf16

Base model

viettelsecurity-ai/security-llama3.2-3b

Quantized

(5)

this model

Collection including ahmedandaloes/security-llama3.2-3b-MLX-bf16

security-llama3.2-3b — MLX builds

Collection

MLX 4/6/8-bit + bf16 of viettelsecurity-ai/security-llama3.2-3b for Apple Silicon. • 4 items • Updated Jul 11