Text Generation
MLX
Safetensors
qwen3_5
jang
jang-quantized
JANG_3S
mixed-precision
apple-silicon
conversational
Instructions to use bearzi/Qwen-3.6-27B-JANG_3S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bearzi/Qwen-3.6-27B-JANG_3S 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("bearzi/Qwen-3.6-27B-JANG_3S") 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 bearzi/Qwen-3.6-27B-JANG_3S with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bearzi/Qwen-3.6-27B-JANG_3S"
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": "bearzi/Qwen-3.6-27B-JANG_3S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use bearzi/Qwen-3.6-27B-JANG_3S with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bearzi/Qwen-3.6-27B-JANG_3S"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bearzi/Qwen-3.6-27B-JANG_3S" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bearzi/Qwen-3.6-27B-JANG_3S", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use bearzi/Qwen-3.6-27B-JANG_3S 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 "bearzi/Qwen-3.6-27B-JANG_3S"
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 bearzi/Qwen-3.6-27B-JANG_3S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bearzi/Qwen-3.6-27B-JANG_3S with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bearzi/Qwen-3.6-27B-JANG_3S"
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 "bearzi/Qwen-3.6-27B-JANG_3S" \ --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"
| { | |
| "quantization": { | |
| "method": "jang-importance", | |
| "profile": "JANG_3S", | |
| "target_bits": 3, | |
| "actual_bits": 3.34, | |
| "block_size": 64, | |
| "calibration_method": "weights", | |
| "quantization_method": "mse", | |
| "scoring_method": "weight-magnitude", | |
| "bit_widths_used": [ | |
| 3, | |
| 6 | |
| ], | |
| "quantization_scheme": "asymmetric", | |
| "quantization_backend": "mx.quantize", | |
| "hadamard_rotation": false | |
| }, | |
| "source_model": { | |
| "name": "qwen3.6-27b", | |
| "dtype": "bfloat16", | |
| "parameters": "25.1B" | |
| }, | |
| "architecture": { | |
| "type": "hybrid_ssm", | |
| "attention": "gqa", | |
| "has_vision": true, | |
| "has_ssm": true, | |
| "has_moe": false | |
| }, | |
| "runtime": { | |
| "total_weight_bytes": 11389501440, | |
| "total_weight_gb": 10.61 | |
| }, | |
| "format": "jang", | |
| "format_version": "2.0" | |
| } | |