Qwen3.8 Flash Next — oQ2 MLX

An oQ2 Apple-silicon conversion of Qwen/Qwen3.8-Flash-Next, quantised directly from the official BF16 checkpoint.

Original model · Qwen overview · MLX-VLM · Qwen Community License 1.0

[!WARNING] Quality hold: this first oQ2 build loads and runs, but live testing found incoherent output. Do not use it as a quality checkpoint while the mixed-precision recipe is being revised and revalidated from the original BF16 weights.

About this conversion

This repository contains an oQ2 mixed-precision MLX conversion produced directly from Qwen's BF16 weights. Calibration measured layer sensitivity using a load-tested 4-bit proxy; the final tensor quantisation reread the original BF16 checkpoint. Group size 32 covers the model's unusual 160-wide hashed n-gram embedding tables.

The upstream tokenizer, chat template, vision processor, and generation configuration are preserved. The optional upstream MTP head is not included.

[!CAUTION] oQ2 is an extreme-compression checkpoint. It trades output fidelity for memory savings and can degrade instruction following, reasoning, factual accuracy, and visual understanding. Prefer the 4-bit or 8-bit variants when quality matters more than footprint.

[!IMPORTANT] Qwen3.8 Flash Next uses the new qwen4_exp architecture. Use an oMLX or MLX-VLM build that explicitly lists qwen4_exp support. Older MLX-VLM releases cannot load this checkpoint.

Quick start

hf download TensorFold/Qwen3.8-Flash-Next-MLX-oQ2 \
  --local-dir Qwen3.8-Flash-Next-MLX-oQ2

With a compatible MLX-VLM runtime:

python -m mlx_vlm.generate \
  --model Qwen3.8-Flash-Next-MLX-oQ2 \
  --prompt "Explain sparse mixture-of-experts routing." \
  --max-tokens 512

Architecture

Qwen3.8 Flash Next combines Gated DeltaNet, Qwen Sparse Attention, sparse mixture-of-experts layers, widened gated residual streams, and hashed bigram/trigram embeddings.

For upstream evaluations, intended use, limitations, safety guidance, and the complete architecture discussion, see the original model card.

Conversion and validation

• Source: official BF16 checkpoint.
• Data-driven sensitivity calibration completed before final BF16 tensor quantisation.
• All 3,671 converted tensors and 14 indexed shards were checked locally.
• The checkpoint passed an end-to-end Apple-silicon generation smoke test.
• The release payload was scanned for credentials, personal contact details, private paths, private network information, logs, caches, and private organisation data.
• Repeatable performance results will be added after benchmarking.

This is a community conversion, not an official Qwen release.

License and attribution

The upstream model is released under the Qwen Community License 1.0. The required licence text is included in this repository.

Model design, training, evaluations, and upstream documentation belong to Qwen and the original contributors. The MLX conversion, Apple-silicon validation, and packaging are provided by TensorFold.

Choose for your Mac

64GB Macs · 128GB Macs · 256GB Macs

No measured memory tier is assigned here. The collections use published M3 Studio peaks with at least 25% nominal headroom; fit on other Macs is an estimate, and full context is not guaranteed. Start with short context and one request.

Runtime and evidence

The exact tested oMLX application version is not recorded here; a library version is not an app version. The original performance tables retain their benchmark conditions and speed figures; this documentation update adds no new test results.

Quick start and demo prompt

hf download TensorFold/Qwen3.8-Flash-Next-MLX-oQ2 --local-dir ./models/Qwen3.8-Flash-Next-MLX-oQ2

Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.

Try this in a new chat with a 128-token output limit:

Explain why the sky looks blue in three short sentences.

This is a demo prompt to try, not a recorded successful run; a captured demonstration for this documentation update is not yet available.

Follow TensorFold for new Apple Silicon releases and fixes.