Instructions to use Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality 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("Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality")

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 Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"

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": "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality 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 "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"

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 Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality"

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 "Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality" \
  --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"

MTPLX.COM: 2 to 3x speedup. The fastest way to run models on a Mac.

Qwen 3.8 27B Optimized Quality

8-bit dynamic quant. Good coding speeds and perfect quality.

The closest of the three MTPLX Qwen 3.8 builds to the original bf16 model. Every weight matrix at 8-bit, sensitive parts at 16-bit, native multi-token-prediction head kept, so MTPLX still drafts ahead and verifies in one pass. Pick this when you want the answer the full model would give and still want it fast.

Speeds

Measured on an M5 Max, fans verified at max, single stream, generation running to the model's own stop, official Qwen 3.8 sampling (temperature 1.0, top-p 0.95, top-k 20).

Run	tok/s
Coding task, medium reasoning, inside the MTPLX Mac app	48.3
Long reasoning at xhigh, 34k and 46k token answers	33.2 and 33.1

Same night, same task, the 4-bit builds: Qwen 3.6 27B Optimized Speed V2 59.9 to 60.1 tok/s, Qwen 3.8 Optimized Speed 58.7, Bare Speed 65.2. This is the quality pick, not the speed pick, and it is still well past 40 tok/s while running the full-precision distribution.

Draft acceptance on the coding task by depth: 0.96, 0.88, 0.79. Verify cost 63.5 ms per round. Depth 3 was measured at +19.9% over depth 2 on the long reasoning task.

How it is built

Every weight matrix at 8-bit with 64-weight groups.
The GDN convolution kernels and recurrent state parameters, every norm, and the whole MTP head stay 16-bit.
KL divergence to the original bf16 model on our coding battery: 0.00105. That is 21x closer than Optimized Speed and 36x closer than Bare Speed. In practice you will not tell the outputs apart from the bf16 model.


Download	29.4 GB
Peak unified memory (measured, this artifact)	32.7 GB
Context window	262,144 tokens
MTP depth	3
Sampling	temperature 1.0, top-p 0.95, top-k 20 (the official Qwen 3.8 contract)

The tuned depth and draft settings ship inside mtplx_runtime.json. MTPLX reads them on load. Speculation is exact: drafts are accepted with the probability-ratio rule plus residual resampling, so the output follows the model's own distribution at any temperature. Reasoning effort levels (xhigh, medium, low) work, and preserved thinking flows through the MTP path.

Use it

You want 36 GB of unified memory or more for this one. Mac app: download at mtplx.com, pick "Qwen 3.8 27B Optimized Quality".

Command line:

pip install mtplx
mtplx serve --model Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality

Siblings: Optimized Speed (recommended for coding) and Bare Speed (quickest burst chat speeds). On an M1 or M2 Mac use the FP16 build of this model.

Downloads last month: 3,109

Safetensors

Model size

8B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for Youssofal/Qwen3.8-27B-MTPLX-Optimized-Quality

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Qwen/Qwen3.8-27B

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