Instructions to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 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-bf16") config = load_config("ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16") # 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
- Pi
How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 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-bf16"
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-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 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-bf16"
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-bf16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 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-bf16"
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-bf16" \ --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-bf16
Qwen/Qwen3.8-Flash-Next を MLX 形式へ変換したもの(bf16・非量子化)。
- 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: 331 GiB
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-bf16 --dtype bfloat16
使い方
pip install -U mlx-vlm
python -m mlx_vlm generate --model ToPo-ToPo/Qwen3.8-Flash-Next-mlx-bf16 \
--prompt "この画像を説明してください" --image path/to/image.jpg --max-tokens 512
推論には 355 GB 超のメモリが要る。量子化版は 4bit(111 GB)/ 8bit(200 GB)。
MTP ヘッド(mtp.*)は変換時に除外される。投機デコードに使う場合は公式 bf16 から
別途切り出すこと。
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Model size
177B params
Tensor type
BF16
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I64 ·
Hardware compatibility
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Base model
Qwen/Qwen3.8-Flash-Next