Instructions to use fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp")
config = load_config("fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp"

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": "fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp"

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 "fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp" \
  --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"

Hermes Agent

How to use fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp 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 "fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp"

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 fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp

Run Hermes

hermes

Atomic Chat

Qwen3.8-27B-MLX-oQ4e-mtp

oQ4e (enhanced, imatrix-calibrated ~4-bit) quantization of Qwen/Qwen3.8-27B with the native MTP head preserved, produced with oMLX 0.5.7's oQ quantizer from the bf16 conversion fcmeyer/Qwen3.8-27B-MLX-bf16-mtp.

~16 GB (≈4.9 bpw effective; 4-bit base, group size 64, affine, sensitivity-boosted tensors at higher bits, bit-protected MTP head)
Enhanced (e): imatrix calibration (128 samples × 512 tokens, oqe_code_multilingual) with Hessian-guided error compensation
Full VLM: vision tower included (image + video understanding)
MTP-preserved: mtp_num_hidden_layers: 1, enabling Lightning-MTP speculative decoding in oMLX (mtp_enabled: true)

Measured on an M5 Max (128 GB), oMLX 0.5.7

~54 tok/s decode with MTP on (depth 3), 81% draft acceptance (2.9 tok/backbone-cycle) — quantization did not degrade the draft head thanks to oQ's MTP bit protection.
Image grounding verified (color/position). See the bf16 repo for the unquantized baseline (~20 tok/s with MTP).

Usage with oMLX

Place under ~/.omlx/models/<org>/Qwen3.8-27B-MLX-oQ4e-mtp (or download via the oMLX admin dashboard) and enable MTP:

"Qwen3.8-27B-MLX-oQ4e-mtp": { "mtp_enabled": true, "max_context_window": 262144 }

Sampling defaults: temperature 1.0, top_p 0.95, top_k 20. The repo includes oq_imatrix_report.json documenting the calibration.

Also loadable as a plain quantized MLX VLM with mlx-vlm ≥ 0.6.3 (MTP tensors are ignored by loaders without MTP support).

Downloads last month: 224

Safetensors

Model size

5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for fcmeyer/Qwen3.8-27B-MLX-oQ4e-mtp

Base model

Qwen/Qwen3.8-27B

Quantized

(358)

this model