Instructions to use JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M 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("JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M")
config = load_config("JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M")

# 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 JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M"

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": "JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M 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 "JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M"

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 JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M"

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 "JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M" \
  --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-JANG_4M / README.md

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language:
  - en
library_name: mlx
license: other
license_name: qwen-community-1.0
license_link: LICENSE
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3.8-Flash-Next
tags:
  - mlx
  - jang
  - jang-4m
  - quantized
  - apple-silicon
  - vision
  - video
  - reasoning
  - agent
  - tool-use
  - qwen4_exp
  - moe
  - ngram-embedding
  - imatrix
  - awq

JANGQ-AI/Qwen3.8-Flash-Next-JANG_4M

The recommended quality/size balance — median KL 0.0039 vs bf16 at 96.6 GiB (~73 GiB resident with the SSD-served table).

A JANG bundle of Qwen/Qwen3.8-Flash-Next — the Qwen4-architecture preview: a 125B mixture-of-experts (512 experts, 6B active) with a 51B hashed n-gram embedding, Gated DeltaNet + Qwen Sparse Attention hybrid layers, gated-residual streams, and vision+video towers — quantized for Apple Silicon / MLX. Text, image and video weights are all present in this exact bundle. Native multi-token-prediction head preserved (4-bit).

Runtime support is landing — this repo is private while the serving path is finalized.

Quality (measured, 5,931 held-out positions vs bf16)

Tier	Size	RAM w/ SSD-table	median KL	top-1	top-5	top-10
JANG_1L	60.4 GiB	~41 GiB	0.0366	86.7%	97.6%	98.8%
JANG_2L	65.9 GiB	~48 GiB	0.0249	87.8%	98.2%	99.1%
JANG_4S	72.4 GiB	~53 GiB	0.0161	89.7%	98.6%	99.3%
JANG_4M	96.6 GiB	~73 GiB	0.0039	94.4%	99.7%	99.9%
JANG_6S	106.9 GiB	~83 GiB	0.0035	94.8%	99.7%	99.9%

Margin-conditioned flip curves are monotone-decreasing on every tier — quantization noise lives in the reference model's own uncertainty band, with zero disagreement at high-confidence positions on the upper tiers.

The n-gram table & memory

The 51B n-gram embedding can be served directly from SSD by supporting runtimes; the "RAM w/ SSD-table" column above is the resident footprint in that mode.

What's in the bundle

Vision + video: the full vision tower and both image and video preprocessors ship in this exact bundle — image-text-to-text and video understanding work out of the box on supporting runtimes (image and video token ids, mRoPE positions, and the merger are all present).
Multi-token prediction: the model's native MTP head is preserved (trained multi-step). Enables self-speculative decode on supporting runtimes.
Thinking + agentic: thinking mode on by default with three reasoning efforts and preserved thinking history; Hermes-style tool calling; the instruct preset gives direct non-thinking responses.
Long context: 262,144 tokens native, extensible to 1M with YaRN.

Serving contract

Thinking mode ON by default: temperature=1.0, top_p=0.95, top_k=20
Instruct mode: temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5
Reasoning efforts low / medium / xhigh (default xhigh) and preserve_thinking (default on) via chat-template kwargs
Context 262,144 native, extensible to 1M with YaRN
EOS [248046, 248044] · tool calls: Hermes-style <tool_call>

Quantized and validated by Jinho Jang — eric@jangq.ai