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

Libraries

How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit 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-Flash-Next-mlx-8bit")
config = load_config("ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit")

# 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-Flash-Next-mlx-8bit 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-Flash-Next-mlx-8bit"

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": "ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit 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-Flash-Next-mlx-8bit"

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-Flash-Next-mlx-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit 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-Flash-Next-mlx-8bit"

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-Flash-Next-mlx-8bit" \
  --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-Flash-Next-mlx-8bit

Qwen/Qwen3.8-Flash-Next を MLX 形式へ変換したもの（8bit 量子化）。

変換元: Qwen/Qwen3.8-Flash-Next（bf16 公式重み, revision de4b8e4）
変換ツール: mlx-vlm 0.6.17 / mlx 0.32.0
サイズ: 186 GiB（9.018 bits per weight）

変換コマンド

python -m mlx_vlm convert --hf-path Qwen/Qwen3.8-Flash-Next \
    --mlx-path Qwen3.8-Flash-Next-mlx-8bit \
    -q --q-bits 8 --q-group-size 32

--q-group-size 32 は必須。n-gram 埋め込みの最終次元が 160 で、既定の 64 では weight.shape[-1] % group_size != 0 により 128 シャード（51B パラメータ）が量子化対象から外れ、bf16 のまま残る。

使い方

pip install -U mlx-vlm
python -m mlx_vlm generate --model ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit \
    --prompt "この画像を説明してください" --image path/to/image.jpg --max-tokens 512

MTP ドラフター（投機デコード）

ドラフターは別リポジトリ Qwen3.8-Flash-Next-MTP-bf16。 mlx-vlm 0.7.0 以降が必要（qwen4_exp_mtp はリリース版 0.6.17 に未収録）:

pip install "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm@main"

python -m mlx_vlm generate --model ToPo-ToPo/Qwen3.8-Flash-Next-mlx-8bit \
    --draft-model ToPo-ToPo/Qwen3.8-Flash-Next-MTP-bf16 \
    --draft-kind mtp --draft-block-size 2 --prompt "..." --max-tokens 512

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Safetensors

Model size

177B params

Tensor type

U32

BF16

I64

MLX

Hardware compatibility

8-bit

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