Text Generation
MLX
Safetensors
qwen3_5_moe
cybersecurity
security-research
abliteration
Mixture of Experts
mtp
tool-use
experimental
conversational
8-bit precision
Instructions to use thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP 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("thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP") 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 thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP"
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": "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP 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 "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP"
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 thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP"
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 "thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP" \ --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"
Download abliteration_config.json from thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP/resolve/main/abliteration_config.json
- Command line
-
hf download hf://thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP/abliteration_config.json
-
curl -L -o abliteration_config.json https://huggingface.co/thelogicalgate/Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP/resolve/main/abliteration_config.json
5.43 kB
| { | |
| "source": "InternScience/Sage-A1.5", | |
| "strength": 0.75, | |
| "modified_modules": [ | |
| "language_model.model.layers.31.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.31.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.31.self_attn.o_proj", | |
| "language_model.model.layers.30.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.30.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.30.linear_attn.out_proj", | |
| "language_model.model.layers.29.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.29.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.29.linear_attn.out_proj", | |
| "language_model.model.layers.28.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.28.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.28.linear_attn.out_proj", | |
| "language_model.model.layers.27.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.27.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.27.self_attn.o_proj", | |
| "language_model.model.layers.26.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.26.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.26.linear_attn.out_proj", | |
| "language_model.model.layers.25.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.25.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.25.linear_attn.out_proj", | |
| "language_model.model.layers.24.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.24.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.24.linear_attn.out_proj", | |
| "language_model.model.layers.23.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.23.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.23.self_attn.o_proj", | |
| "language_model.model.layers.22.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.22.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.22.linear_attn.out_proj", | |
| "language_model.model.layers.21.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.21.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.21.linear_attn.out_proj", | |
| "language_model.model.layers.20.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.20.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.20.linear_attn.out_proj", | |
| "language_model.model.layers.19.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.19.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.19.self_attn.o_proj", | |
| "language_model.model.layers.18.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.18.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.18.linear_attn.out_proj", | |
| "language_model.model.layers.17.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.17.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.17.linear_attn.out_proj", | |
| "language_model.model.layers.16.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.16.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.16.linear_attn.out_proj", | |
| "language_model.model.layers.15.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.15.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.15.self_attn.o_proj", | |
| "language_model.model.layers.14.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.14.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.14.linear_attn.out_proj", | |
| "language_model.model.layers.13.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.13.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.13.linear_attn.out_proj", | |
| "language_model.model.layers.12.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.12.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.12.linear_attn.out_proj", | |
| "language_model.model.layers.11.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.11.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.11.self_attn.o_proj", | |
| "language_model.model.layers.10.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.10.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.10.linear_attn.out_proj", | |
| "language_model.model.layers.9.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.9.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.9.linear_attn.out_proj", | |
| "language_model.model.layers.8.mlp.shared_expert.down_proj", | |
| "language_model.model.layers.8.mlp.switch_mlp.down_proj", | |
| "language_model.model.layers.8.linear_attn.out_proj" | |
| ], | |
| "bits": 8, | |
| "group_size": 64, | |
| "vision_preserved": true, | |
| "mtp_exported": true, | |
| "method": "single residual direction; partial norm-preserving output-weight projection; layers 8..31", | |
| "source_revision": "3e6b02c5e6afbc481fd4556baca8e6009f26d696", | |
| "selected_direction_layer": 17, | |
| "calibration": { | |
| "contrast_dataset": "mlabonne/harmful_behaviors", | |
| "control_dataset": "mlabonne/harmless_alpaca", | |
| "samples_per_dataset": 128, | |
| "fit_per_dataset": 96, | |
| "selection_per_dataset": 32, | |
| "shuffle_seed": 42, | |
| "domain_specific": false | |
| }, | |
| "release_scope": "Cybersecurity-oriented intended use and evaluation; calibration is general contrast data; effects are not technically confined to cybersecurity.", | |
| "build_versions": { | |
| "mlx": "0.32.4", | |
| "mlx-vlm": "0.7.10", | |
| "transformers": "5.20.2", | |
| "numpy": "2.5.4" | |
| } | |
| } | |