ZySec-SecurityLLM โ MLX builds
Collection
MLX 4/6/8-bit + bf16 of ZySec-AI/SecurityLLM for Apple Silicon. โข 4 items โข Updated
How to use ahmedandaloes/ZySec-SecurityLLM-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("ahmedandaloes/ZySec-SecurityLLM-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)How to use ahmedandaloes/ZySec-SecurityLLM-MLX-8bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ahmedandaloes/ZySec-SecurityLLM-MLX-8bit"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ahmedandaloes/ZySec-SecurityLLM-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": "ahmedandaloes/ZySec-SecurityLLM-MLX-8bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'8-bit quantized MLX build of ZySec-AI/SecurityLLM, for fast local inference on Apple Silicon.
mlx-lm.GGUF builds: QuantFactory/SecurityLLM-GGUF.
pip install mlx-lm
from mlx_lm import load, generate
model, tok = load("ahmedandaloes/ZySec-SecurityLLM-MLX-8bit")
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))
Source: ZySec-AI/SecurityLLM. License per source (Apache-2.0 assumed; verify). MLX build for the Apple Silicon community. For authorized security work only.
8-bit
Base model
ZySec-AI/SecurityLLM