Instructions to use mlx-community/Qwen3.6-27B-OBLITERATED-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Qwen3.6-27B-OBLITERATED-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("mlx-community/Qwen3.6-27B-OBLITERATED-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

How to use mlx-community/Qwen3.6-27B-OBLITERATED-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 "mlx-community/Qwen3.6-27B-OBLITERATED-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": "mlx-community/Qwen3.6-27B-OBLITERATED-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Qwen3.6-27B-OBLITERATED-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 "mlx-community/Qwen3.6-27B-OBLITERATED-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 mlx-community/Qwen3.6-27B-OBLITERATED-MLX-4bit

Run Hermes

hermes

MLX LM

How to use mlx-community/Qwen3.6-27B-OBLITERATED-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 "mlx-community/Qwen3.6-27B-OBLITERATED-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Qwen3.6-27B-OBLITERATED-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": "mlx-community/Qwen3.6-27B-OBLITERATED-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.6-27B-OBLITERATED — MLX 4-bit

MLX conversion of OBLITERATUS/Qwen3.6-27B-OBLITERATED for Apple Silicon.

Model details


Base	Qwen3.6-27B-OBLITERATED
Architecture	qwen3_5 (mixed linear + full attention)
Parameters	26.9B
Quantization	4-bit affine, group_size 64
Format	MLX safetensors (3 shards, ~14 GB)
Context window	262,144 tokens
Tokenizer	Qwen2Tokenizer with tool-call support

The model uses an alternating linear/full attention pattern across 64 layers (mamba-style linear attention interleaved with standard multi-head attention), giving it efficient long-context performance on Apple Silicon.

Usage

mlx-lm

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("edward-lcl/Qwen3.6-27B-OBLITERATED-MLX-4bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=512)
print(response)

mlx-lm CLI

mlx_lm.generate \
  --model edward-lcl/Qwen3.6-27B-OBLITERATED-MLX-4bit \
  --prompt "Your prompt here" \
  --max-tokens 512

Generation defaults


Temperature	0.35
Repetition penalty	1.05
top_p	1.0

Notes

mlx_lm emits a harmless warning about unrecognized rope_parameters keys (mrope_interleaved, mrope_section) — this is a known Qwen quirk; generation is unaffected
Converted and tested on Apple M5 Pro, 48 GB unified memory
Metal's effective GPU working set ceiling is ~37.4 GB regardless of total RAM; model weights occupy ~14.4 GB of that
Practical context on 48 GB: comfortable sustained use up to ~75K tokens; hard ceiling observed at ~91K before prefill guard trips
Source model includes a chat_template.jinja with full tool-call formatting