Text Generation
MLX
Safetensors
English
qwen3
security
cybersecurity
pentest
CVSS
OWASP
red-team
bug-bounty
128k-context
MLX
Safetensors
4-bit precision
apple-silicon
ravenx
rath-protocol
tool-calling
conversational
Instructions to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit 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("deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit") 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 deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
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": "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit 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 "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
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 deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit
Run Hermes
hermes
- MLX LM
How to use deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deadbydawn101/RavenX-Sec-8B-Security-RATH-128k-mlx-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
v4.0: config.json — 610K examples, 6-step RATH protocol
Browse files- config.json +66 -70
config.json
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"transformers_version": "4.56.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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"architectures": [
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.0",
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"use_cache": true,
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"vocab_size": 151936
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