Qwen3.6-27B-Omnimerge-v4-MLX-4bit

Instructions to use shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shaffhausen/Qwen3.6-27B-Omnimerge-v4-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("shaffhausen/Qwen3.6-27B-Omnimerge-v4-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 shaffhausen/Qwen3.6-27B-Omnimerge-v4-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 "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit"

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": "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use shaffhausen/Qwen3.6-27B-Omnimerge-v4-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 "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "shaffhausen/Qwen3.6-27B-Omnimerge-v4-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": "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use shaffhausen/Qwen3.6-27B-Omnimerge-v4-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 "shaffhausen/Qwen3.6-27B-Omnimerge-v4-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 shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit"

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 "shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit" \
  --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"

Qwen3.6-27B-Omnimerge-v4 — MLX 4-bit

4-bit MLX quantization of ManniX-ITA/Qwen3.6-27B-Omnimerge-v4 for native Apple Silicon inference via mlx-lm.

The base model is a same-base DARE-TIES (Omnimerge_v2 method) merge of Qwen/Qwen3.6-27B with three Qwen3.6 fine-tunes (rico03, Esper3.1, kai-os Opus-Reasoning-anchor) plus an MLP-passthrough surgery that fixes Qwen3.6's reasoning-tag-emission fragility. Benchmark numbers and the full method writeup live on the base model card.

Quantization

Type: MLX 4-bit (-q --q-bits 4)
Group size: 64
Effective bits/weight: 4.501 (per the mlx_lm.convert quantizer)
Shape on disk: 3 safetensors shards, ~15 GB total
Build env: mlx==0.30.0 + mlx-cuda==0.30.0 + mlx-lm==0.30.7 on Linux + RTX 3090 (CUDA backend used only for the conversion step; end users run the native Apple Silicon MLX runtime, which has no CUDA dependency).

Conversion recipe: omnimergekit/scripts/mlx_convert.sh — the canonical OmniMergeKit MLX-conversion runner. See MLX_CONVERT.md for the full pin rationale and disk-budget notes.

Usage

Tested with mlx-lm >= 0.30.7 on macOS (M1/M2/M3/M4):

pip install -U mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MLX-4bit")
prompt = "Write a Rust function that returns the n-th Fibonacci number iteratively."
print(generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True))

The base model emits Qwen3.6 reasoning tags (<think>...</think>); strip them in post-processing or use the chat template that wraps them appropriately.

Memory & speed

Empirically (M-series, 32 GB+ recommended):

Resident memory: ~16-17 GB
Speed: comparable to other Qwen3 27B 4-bit MLX builds; depends on chip generation
Context length: inherits the base model's 256k context window (RAM permitting)

The vision tower is not included in the MLX export — this is a text-only build. For multimodal use, prefer the GGUF release with bartowski/Qwen_Qwen3.6-27B-GGUF's mmproj, or run BF16 via transformers.

Base merge: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4
GGUF release: ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-GGUF
Ollama tags: mannix/omnimerge-v4
Methodology + scripts: mann1x/omnimergekit

License

Apache 2.0 — inherits from Qwen3.6 base. See the base model card for the full attribution list (Qwen team, rico03, ValiantLabs, kai-os, mergekit community).

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Safetensors

Model size

4B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit

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Qwen3.6-27B-Omnimerge-v4 — MLX 4-bit

Quantization

Usage

Memory & speed

Related

License

Model tree for shaffhausen/Qwen3.6-27B-Omnimerge-v4-MLX-4bit