Text Generation
MLX
Safetensors
English
qwen3_5_moe
reasoning
distillation
chain-of-thought
qwen
qwen3.6
mixture-of-experts
Mixture of Experts
lora
unsloth
abliterated
uncensored
conversational
Instructions to use deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use deadbydawn101/RavenX-Sec-35B-Security-RATH-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("deadbydawn101/RavenX-Sec-35B-Security-RATH-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
- Unsloth Studio
How to use deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx", max_seq_length=2048, ) - Pi
How to use deadbydawn101/RavenX-Sec-35B-Security-RATH-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 "deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx"
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-35B-Security-RATH-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use deadbydawn101/RavenX-Sec-35B-Security-RATH-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 "deadbydawn101/RavenX-Sec-35B-Security-RATH-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 deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx
Run Hermes
hermes
- MLX LM
How to use deadbydawn101/RavenX-Sec-35B-Security-RATH-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 "deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "deadbydawn101/RavenX-Sec-35B-Security-RATH-mlx" # 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-35B-Security-RATH-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "add_prefix_space": false, | |
| "audio_bos_token": "<|audio_start|>", | |
| "audio_eos_token": "<|audio_end|>", | |
| "audio_token": "<|audio_pad|>", | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "image_token": "<|image_pad|>", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "model_max_length": 262144, | |
| "model_specific_special_tokens": { | |
| "audio_bos_token": "<|audio_start|>", | |
| "audio_eos_token": "<|audio_end|>", | |
| "audio_token": "<|audio_pad|>", | |
| "image_token": "<|image_pad|>", | |
| "video_token": "<|video_pad|>", | |
| "vision_bos_token": "<|vision_start|>", | |
| "vision_eos_token": "<|vision_end|>" | |
| }, | |
| "pad_token": "<|vision_pad|>", | |
| "padding_side": "right", | |
| "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", | |
| "processor_class": "Qwen3VLProcessor", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "TokenizersBackend", | |
| "tool_parser_type": "qwen3_coder", | |
| "unk_token": null, | |
| "video_token": "<|video_pad|>", | |
| "vision_bos_token": "<|vision_start|>", | |
| "vision_eos_token": "<|vision_end|>" | |
| } | |