Instructions to use Sawfwair/Qwen3.8-Flash-Next-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sawfwair/Qwen3.8-Flash-Next-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("Sawfwair/Qwen3.8-Flash-Next-MLX-4bit")
config = load_config("Sawfwair/Qwen3.8-Flash-Next-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 Sawfwair/Qwen3.8-Flash-Next-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 "Sawfwair/Qwen3.8-Flash-Next-MLX-4bit"

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": "Sawfwair/Qwen3.8-Flash-Next-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Sawfwair/Qwen3.8-Flash-Next-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 "Sawfwair/Qwen3.8-Flash-Next-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 Sawfwair/Qwen3.8-Flash-Next-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Sawfwair/Qwen3.8-Flash-Next-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 "Sawfwair/Qwen3.8-Flash-Next-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 "Sawfwair/Qwen3.8-Flash-Next-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"

Qwen3.8-Flash-Next MLX 4-bit

This is a reproducible MLX conversion of Qwen/Qwen3.8-Flash-Next at immutable revision f5d08274bafd880402bd16f5e3e6c514136ec06c.

MLX affine Q4/group-64 for eligible language, MTP, and vision matrices; the 160-wide n-gram table uses Q4/group-32. Routers, norms, biases, convolutions, and incompatible shapes remain dense.

Artifact payload: 97.55 GiB
Quantized Q2 modules: 0
Quantized Q4/group-32 modules: 128
Quantized Q4/group-64 modules: 927
Source: 180B parameters including 125B main, 51B n-gram embedding, and 4B MTP
Native context: 262,144 tokens

Runtime status

The tensor inventory, source hashes, MLX packing, fused-expert split, convolution layout, and zero-centered RMSNorm conversion are validated by the bundled MERERUN_CONVERSION.json. A Qwen4Exp-aware MLX runtime is required; do not expect an older mlx-lm or mlx-vlm release to dispatch this new architecture.

License

This redistribution retains the upstream Qwen Community License 1.0 in LICENSE. Review it before use. In particular, it contains attribution/display requirements for very large commercial products and separate-license conditions for certain commercial Model-as-a-Service and AI Work Assistant uses. The model is not gated; downloading or using it does not remove those terms.

The upstream model card is preserved as README.upstream.md.

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Safetensors

Model size

30B params

Tensor type

BF16

U32

I64

MLX

Hardware compatibility

4-bit

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