Instructions to use EigenLabs/Qwen3.8-27B-4bit-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EigenLabs/Qwen3.8-27B-4bit-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("EigenLabs/Qwen3.8-27B-4bit-mtp")
config = load_config("EigenLabs/Qwen3.8-27B-4bit-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 EigenLabs/Qwen3.8-27B-4bit-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 "EigenLabs/Qwen3.8-27B-4bit-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": "EigenLabs/Qwen3.8-27B-4bit-mtp"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use EigenLabs/Qwen3.8-27B-4bit-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 "EigenLabs/Qwen3.8-27B-4bit-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 EigenLabs/Qwen3.8-27B-4bit-mtp

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EigenLabs/Qwen3.8-27B-4bit-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 "EigenLabs/Qwen3.8-27B-4bit-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 "EigenLabs/Qwen3.8-27B-4bit-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 MLX W4/g64 + Embedded W4 MTP

This is EigenLabs/Qwen3.8-27B-4bit at exact revision 301e9e2767fd0efcfab7883004720ba3c9a552a1 with the previously separate MTP proposal head (EigenLabs/Qwen3.8-27B-MTP-4bit at exact revision 329261c5e0b3f9c233485e682cb3b67b88c20a55) embedded in the same indexed safetensors weight set under the mtp.* prefix.

Target and vision tower tensors: byte-identical to the pinned target revision.
MTP head tensors: byte-identical to the pinned head revision (W4/g64 affine, BF16 scales/biases, one full-attention layer, ~239 MB), re-keyed from the standalone layout (fc.weight, layers.0.*, …) to mtp.*.
Declared via mtplx_mtp / mtplx_mtp_quantization in config.json (prefix: "mtp.", block_size: 3). The head shares the target embedding and LM head; the target backbone remains authoritative for token acceptance and final output.

This repository supersedes the external-head pairing: the draft/target revision pins that previously lived in a README are now structural — one manifest, one weight hash, nothing to drift.

Measured (M4 Max, 546 GB/s, temperature 0)

Configuration	decode tok/s
target only	20.3–20.6
embedded MTP	28–31.5

Downloads last month: 134

Safetensors

Model size

28B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for EigenLabs/Qwen3.8-27B-4bit-mtp

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Qwen/Qwen3.8-27B

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