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

Libraries

How to use ToPo-ToPo/Qwen3.8-27B-mlx-4bit 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("ToPo-ToPo/Qwen3.8-27B-mlx-4bit")
config = load_config("ToPo-ToPo/Qwen3.8-27B-mlx-4bit")

# 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 ToPo-ToPo/Qwen3.8-27B-mlx-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/Qwen3.8-27B-mlx-4bit"

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": "ToPo-ToPo/Qwen3.8-27B-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ToPo-ToPo/Qwen3.8-27B-mlx-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ToPo-ToPo/Qwen3.8-27B-mlx-4bit"

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 "ToPo-ToPo/Qwen3.8-27B-mlx-4bit" \
  --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 ToPo-ToPo/Qwen3.8-27B-mlx-4bit 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 "ToPo-ToPo/Qwen3.8-27B-mlx-4bit"

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 ToPo-ToPo/Qwen3.8-27B-mlx-4bit

Run Hermes

hermes

Atomic Chat

ToPo-ToPo/Qwen3.8-27B-mlx-4bit

MLX 4bit conversion of Qwen/Qwen3.8-27B for Apple Silicon (mlx-vlm).

Provenance (self-converted from official weights)

Source: Qwen/Qwen3.8-27B (license: apache-2.0)
Tool: mlx-vlm 0.6.9 — mlx_vlm.convert --hf-path Qwen/Qwen3.8-27B --mlx-path . -q --q-bits 4 --q-group-size 64
Effective: 4.695 bits/weight
Architecture: Qwen3_5ForConditionalGeneration (model_type: qwen3_5), 64 layers with 3:1 linear/full attention, vision tower included (333 vision tensors).

Validation

Text and image input verified with mlx_vlm.generate.
OpenAI-compatible gateway (local-llm-server): text, image (data URI) and native tool calling (finish_reason: tool_calls) all verified.
End-to-end agent runs (MCP tools, write → read → report) completed without errors.

Throughput and memory use depend on your hardware, so measure them on your own machine.

Note on MTP

The official checkpoint declares mtp_num_hidden_layers: 1, but the mlx-vlm qwen3_5 conversion does not carry the model.mtp.* weights. A drafter split from the official bf16 checkpoint is published separately as ToPo-ToPo/Qwen3.8-27B-MTP-bf16 (a 4bit variant is also available).

Usage

from mlx_vlm import load, generate
model, processor = load("ToPo-ToPo/Qwen3.8-27B-mlx-4bit")

Downloads last month: 105

Safetensors

Model size

5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

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