Instructions to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed 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-Bare-Speed"

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-Bare-Speed"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed 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-Bare-Speed"

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-Bare-Speed

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed 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-Bare-Speed"

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-Bare-Speed" \
  --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"

SUPPORT COMING IN MTPLX v2.10. This pack needs MTPLX 2.10, which is about to release. The current MTPLX 2.9.x cannot serve it yet. Update to 2.10 when it lands and this model works out of the box in the app and the CLI.

MTPLX.COM: 2 to 3x speedup. The fastest way to run models on a Mac.

Qwen 3.8 Flash-Next Bare Speed

Flat 4-bit quantization. Quickest Flash-Next speeds for chat and coding.

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 — running natively on MTPLX from day 0, with its native multi-token-prediction head drafting through MTPLX's speculative lane. Every expert at flat 4-bit for the fastest Flash-Next build. If you want the higher- quality sibling, pick Optimized Speed.

The 32 GB n-gram embedding table streams from SSD by default, so the model fits a 96 GB+ Apple Silicon Mac with headroom — the weights stay resident, the table does not have to.

Speeds

Measured on an M5 Max, fans verified at max, single stream, real server (mtplx serve), official Qwen 3.8 sampling (temperature 1.0, top-p 0.95, top-k 20 — sampled, not greedy).

Run	tok/s
Coding task, MTP speculative decode (the default)	75.9
Same task, plain autoregressive	47.0

That is a 1.6x speculative multiplier through the product serve path, on sampled output that follows the model's own distribution.

How it is built

Every MoE expert and dense matrix at 4-bit with 64-weight groups. Nothing promoted — this is the flat, fastest build.
The GDN convolution and recurrent-state parameters, every norm, the QSA indexer, and the MTP head stay 16-bit.
The n-gram embedding table ships as a separate ngram-table.safetensors sidecar that MTPLX streams from SSD (resident is opt-in on very large machines). The vision tower is preserved in the weights.


Download	106.3 GB (includes the 32 GB n-gram table)
Resident weights (n-gram on SSD)	~74 GB + working set
Recommended Macs	96 GB+ unified memory
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)

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: download at mtplx.com, pick "Qwen 3.8 Flash-Next Bare Speed".

Command line:

pip install mtplx
mtplx serve --model Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed

Sibling: Optimized Speed (dynamic quant with 8-bit attention — higher quality, slightly slower).

Base model: Qwen/Qwen3.8-Flash-Next (Qwen Community License; the upstream model card is preserved in this repo as README-upstream-qwen.md).

Downloads last month: -

Safetensors

Model size

21B params

Tensor type

BF16

U32

I64

MLX

Hardware compatibility

4-bit

Model tree for Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

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this model