Instructions to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality 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("Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality") config = load_config("Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality") # 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 Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"
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": "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality 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 "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"
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 Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"
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 "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality" \ --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"
MTPLX: the fastest way to run Qwen 3.8 on a Mac. Native multi-token-prediction speculative decoding on Apple Silicon, two to three times the speed of plain decoding, exact at any temperature.
Qwen 3.8 Flash-Next Optimized Quality
8-bit body and MTP head, BF16 structural tensors, and a 4-bit n-gram table. Higher-fidelity Flash-Next build.
Qwen's 125B-A6B Flash-Next preview, the Qwen4-generation architecture with GDN hybrid MoE, Qwen Sparse Attention and the 51B-parameter n-gram memory, at 8 bits: the highest-fidelity way to run it on a Mac, with its native multi-token-prediction head drafting through MTPLX's speculative path. It is built for Mac Studio with 256 GB or 512 GB. On a 128 GB Mac, pick Optimized Speed, the recommended build.
The 32 GB n-gram table streams from SSD, so only the weights stay in memory: about 128.5 GiB, which leaves about 59.5 GiB for context and the session cache on a 256 GB Mac.
How it is built
- The experts, attention and the multi-token-prediction head at 8 bits with 64-weight groups, twice the precision of Optimized Speed.
- The structural weights stay in BF16.
- The n-gram table at 4 bits, as a separate sidecar that MTPLX streams from SSD. The vision tower is preserved in the weights.
| Download | 170 GB (includes the 32 GB n-gram table) |
| Weights in memory (n-gram on SSD) | about 128.5 GiB |
| Recommended Macs | 256 GB and 512 GB (on 128 GB, use Optimized Speed) |
| Context window | 262,144 tokens |
| MTP depth | adaptive, ceiling 3 |
| Sampling | temperature 1.0, top-p 0.95, top-k 20 (the official Qwen 3.8 contract) |
Speed on 256 GB and 512 GB Macs has not been measured yet. The 8-bit weights move twice the bytes per token of Optimized Speed, so it decodes slower.
The serving contract ships inside mtplx_runtime.json. MTPLX reads it on
load. Drafts are accepted with the probability-ratio rule plus residual
resampling, so the output follows the model's own distribution at any
temperature.
Use it
Mac app (MTPLX 2.12.0 or later): download at mtplx.com, pick "Qwen 3.8 Flash-Next Optimized Quality". On Macs with 256 GB or more it is listed second, after Optimized Speed.
Command line (MTPLX 2.12.0 or later):
pip install mtplx
mtplx serve --model Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality --model-id mtplx-flash-next-optimized-quality
Siblings: Optimized Speed (the recommended build) and Bare Speed (flat 4-bit, the quickest build).
Base model: Qwen/Qwen3.8-Flash-Next
(Qwen Community License; the upstream model card is preserved in this repo as
README-upstream-qwen.md).
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