JANGQ-AI/Qwen3.8-Flash-Next-JANG_6S

⚠️ REDOWNLOAD REQUIRED (2026-09-05): This bundle's MTP auxiliary files were reorganized. If you downloaded this model before 2026-09-05, please delete your local copy and re-download the entire repository — mixed old/new files can fail the runtime's bundle integrity check.

<p align="center"> <img src="./jangq-logo.png" alt="JANGQ" width="220"> </p>

⚠️ Tool calling — parser notice. This model emits Qwen-XML tool calls (<function=name><parameter=...> inside <tool_call>), not Hermes JSON. This bundle declares tool_parser: "qwen" in its config — use a runtime whose tool parser accepts the Qwen XML dialect. Hermes-only parsers will fail on tool turns (dropped/mangled calls). Runtime support for the correct parser is rolling out; until your runtime has it, expect tool-use errors.

<p align="center"> <a href="https://vmlx.net"><img src="vmlx-app.png" alt="vMLX — run JANG models on Apple Silicon" width="820"></a> </p>

<h3 align="center">⚡ All JANG models are meant to be run in <a href="https://vmlx.net">vMLX</a></h3>

JANGQ-AI/Qwen3.8-Flash-Next-JANG_6S

The top tier — median KL 0.0035 vs bf16, built for 128 GB Macs with the n-gram table on SSD (~83 GiB resident).

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 (6-bit).

Best experienced in vMLX. This bundle's layout — the SSD-served n-gram table, per-module mixed precision, and the native MTP head — is designed for the vMLX serving path. Access is gated (manual approval) while runtime support rolls out.

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

[image]

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 — SSD caching, fixed and fast

The 51B n-gram embedding streams directly from SSD on supporting runtimes (16 row-reads per token) — the "RAM w/ SSD-table" column above is the true resident footprint in that mode. Early runtime builds throttled in this mode; SSD-table caching is now fixed: decode runs at full speed with the table on disk — 40+ tok/s on an M5 Max for the 4-bit tier — so the biggest tiers fit comfortably on 64–128 GB machines without giving up the table.

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