Instructions to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jedisct1/Qwopus3.6-27B-Coder-4bit.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("jedisct1/Qwopus3.6-27B-Coder-4bit.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

How to use jedisct1/Qwopus3.6-27B-Coder-4bit.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 "jedisct1/Qwopus3.6-27B-Coder-4bit.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": "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jedisct1/Qwopus3.6-27B-Coder-4bit.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 "jedisct1/Qwopus3.6-27B-Coder-4bit.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 jedisct1/Qwopus3.6-27B-Coder-4bit.mlx

Run Hermes

hermes

OpenClaw new

How to use jedisct1/Qwopus3.6-27B-Coder-4bit.mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"

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 "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx" \
  --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"

MLX LM

How to use jedisct1/Qwopus3.6-27B-Coder-4bit.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 "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "jedisct1/Qwopus3.6-27B-Coder-4bit.mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwopus3.6-27B-Coder-4bit.mlx / README.md

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	---
	base_model: Jackrong/Qwopus3.6-27B-Coder
	library_name: mlx
	pipeline_tag: text-generation
	tags:
	- mlx
	- omlx
	- optiq
	- qwen3.6
	- tool-calling
	---

	# Qwopus3.6-27B-Coder 4-bit MLX

	This is an oMLX OptiQ 4-bit package of `Jackrong/Qwopus3.6-27B-Coder`, built from source revision `f22041be82998a006c9fde642c7900fb42aff94e`.

	This variant does not include the source model's MTP tensors. The MTP config fields are normalized to zero so it loads as the plain text-generation model.

	The tokenizer includes the `qwen3_coder` tool parser setting and a patched chat template for OpenAI-style tool calls. The template was adjusted for Swival-style coding-agent work, including array arguments for command tools and read-before-delete/edit behavior.

	## Local Validation

	Validated on June 12, 2026 with oMLX:

	- Static artifact check passed: oQ4 quantization, no MTP tensors, matching `chat_template.jinja` and embedded `tokenizer_config.json` template.
	- Direct OpenAI-compatible tool-call smoke passed for scalar string arguments and argv-array arguments.
	- Swival tool-calling gate passed 5/5 cases with all tools enabled: edit by line number, read/edit/test, batch read/write, read/delete, and argv-form command execution.

	Use it through oMLX with model id:

	```text
	Qwopus3.6-27B-Coder-4bit.mlx
	```