Instructions to use nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed 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("nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed")

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 nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"

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": "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed 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 "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"

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 nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed

Run Hermes

hermes

OpenClaw new

How to use nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"

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 "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed" \
  --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 nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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

nom666

Update MTPLX Forge README

768649c verified 29 days ago

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	---
	language: en
	pipeline_tag: text-generation
	library_name: mlx
	license: apache-2.0
	base_model: Jackrong/Qwopus3.6-27B-Coder
	tags:
	- mlx
	- mtplx
	- mtp
	- speculative-decoding
	- apple-silicon
	- qwopus
	- code
	- agent
	---

	# Qwopus3.6-27B-Coder · MTPLX 4-bit Speed

	The first [MTPLX](https://github.com/youssofal/MTPLX) build of Qwopus3.6-27B-Coder — Jackrong's agentic-coding fine-tune of Qwopus3.6-27B-v2 (repo-level coding, multi-turn tool orchestration, 67.0% SWE-bench Verified in no-thinking mode), with native multi-token-prediction speculative decoding on Apple Silicon. No external drafter, exact rejection sampling. 8-bit sibling: [nom666/Qwopus3.6-27B-Coder-MTPLX-8bit-Quality](https://huggingface.co/nom666/Qwopus3.6-27B-Coder-MTPLX-8bit-Quality).

	Forged with `mtplx forge build` from the original BF16 [Jackrong/Qwopus3.6-27B-Coder](https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder):

	- Body: flat 4-bit MLX affine quantization, group size 64
	- MTP head: preserved in BF16 (`mtp_policy: keep_bf16`), packed as `mtp.safetensors` sidecar
	- Size: ~15 GB
	- Calibrated MTP contract included (`mtplx_runtime.json`)

	## Measured performance (Apple M5 Max, 128 GB, MTPLX 1.0.3)

	\| Mode \| Decode \| Acceptance by depth \|
	\|---\|---\|---\|
	\| MTP depth 2 (winner) \| 57.3 tok/s \| 97% / 90% \|

	The Coder's MTP head was trained with draft=2, which shows: depth-2 acceptance is exceptional and depth 2 beats depth 3. Verification suite: `long-code-uncapped`, 2048-token budget. Use `--depth 2` when serving.

	## Usage

	```bash
	brew install youssofal/mtplx/mtplx # or pipx install mtplx
	mtplx pull nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed
	mtplx quickstart --model nom666/Qwopus3.6-27B-Coder-MTPLX-4bit-Speed \
	--depth 2 --paged-kv-quantization q8 --batching-preset agent --reasoning off
	```

	Serves OpenAI-compatible (`/v1/chat/completions`) and Anthropic-compatible (`/v1/messages`) endpoints with warm-prefix KV reuse, SSD session cache, continuous batching, and vision support. Full 262144-token context; only 16 of 64 layers carry KV (hybrid Gated DeltaNet architecture).

	## Notes

	- This fine-tune targets no-thinking agentic use — pair with `--reasoning off`.
	- Runtime contract tier is `forge-local`: verified on the forging machine (M5 Max).
	- Quantized with MTPLX 1.0.3 Forge. All credit for the fine-tune to Jackrong; base Qwen3.6-27B (Apache-2.0).