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

Libraries

How to use Jundot/Qwen3.8-27B-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("Jundot/Qwen3.8-27B-oQ4e-mtp")
config = load_config("Jundot/Qwen3.8-27B-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 Jundot/Qwen3.8-27B-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 "Jundot/Qwen3.8-27B-oQ4e-mtp"

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Jundot/Qwen3.8-27B-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 "Jundot/Qwen3.8-27B-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 "Jundot/Qwen3.8-27B-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"

Qwen3.8-27B-oQ4e-mtp

Updated 2026-10-29: Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy.

This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930).

A mixed-precision MLX checkpoint of Qwen/Qwen3.8-27B, a 27B dense vision-language model built on the Qwen3.5 architecture, with a hybrid layout of Gated DeltaNet and Gated Attention layers, image and video understanding and a 262,144-token native context. It includes the language model, the vision encoder and the multi-token prediction (MTP) head.

The effective average is 4.840 bits/weight for the language backbone and 5.044 bits/weight for the complete checkpoint.

Quantization and Bit Distribution

oQ4e allocates bits by measured layer sensitivity, then rounds each group with an importance matrix (128 samples x 512 tokens) and a weighted least-squares refit of its scale and bias.

Storage format	Logical weights	Share of total	Tensor storage	Effective bits/weight
Affine 4-bit, group 64	19.393B	69.807%	10.160 GiB	4.50
Affine 5-bit, group 64	6.579B	23.680%	4.212 GiB	5.50
Affine 6-bit, group 64	1.282B	4.615%	0.970 GiB	6.50
Affine 8-bit, group 64	0.011B	0.041%	0.011 GiB	8.50
BF16	0.516B	1.857%	0.961 GiB	16.00
Total	27.781B	100%	16.314 GiB	5.044

Storage Breakdown

Component	Logical weights	Storage
Language backbone	26.896B	16.272 GB / 15.155 GiB
Vision encoder	0.461B	0.921 GB / 0.858 GiB
MTP head	0.425B	0.323 GB / 0.301 GiB
Total	27.781B	17.517 GB / 16.314 GiB

License

Apache-2.0, following the source model. See LICENSE.

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Safetensors

Model size

28B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

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

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Qwen/Qwen3.8-27B

Quantized

(1617)

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