Qwen3.8-Flash-Next-MLX-4bit

<p align="center"> <a href="https://tensorfold.dev"> <img src="https://huggingface.co/spaces/TensorFold/README/resolve/main/tensorfold-logo.png" alt="TensorFold" width="160"> </a> </p> <p align="center"> <img src="https://img.shields.io/badge/Qwen-Qwen3.8-615CED?style=for-the-badge&logo=qwen&logoColor=white" alt="Qwen Qwen3.8"> <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX"> <img src="https://img.shields.io/badge/TensorFold-oMLX-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="TensorFold oMLX"> </p> <h1 align="center">Qwen3.8 Flash Next — MLX 4-bit</h1> <p align="center"> A native Apple-silicon conversion of <a href="https://huggingface.co/Qwen/Qwen3.8-Flash-Next">Qwen/Qwen3.8-Flash-Next</a>, quantised directly from the official BF16 checkpoint. </p> <p align="center"> <a href="https://huggingface.co/Qwen/Qwen3.8-Flash-Next">Original model</a> · <a href="https://qwen.ai/blog?id=qwen3.8-flash-next">Qwen overview</a> · <a href="https://github.com/ml-explore/mlx-vlm">MLX-VLM</a> · <a href="LICENSE">Qwen Community License 1.0</a> </p>

About this conversion

This repository contains a 4-bit affine MLX conversion of Qwen3.8 Flash Next. It was produced directly from Qwen's BF16 weights using group size 32. The smaller group is intentional: it also covers the model's 160-wide hashed n-gram embedding tables instead of leaving them in BF16.

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

[!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.

[!CAUTION] Do not attach a Qwen3.8 27B MTP drafter to this model. The hidden sizes differ and the drafter is incompatible with Flash Next.

Quick start

hf download TensorFold/Qwen3.8-Flash-Next-MLX-4bit \
  --local-dir Qwen3.8-Flash-Next-MLX-4bit

With a compatible MLX-VLM runtime:

python -m mlx_vlm.generate \
  --model Qwen3.8-Flash-Next-MLX-4bit \
  --prompt "Explain sparse mixture-of-experts routing." \
  --max-tokens 512

Measured performance

Validated on an Apple M3 Studio with text-only generation after model load:

The standalone result is a short smoke test; the longer oMLX figures better represent sustained chat generation. Results vary with prompt length, cache state, sampling settings, runtime version, and memory pressure.

Architecture

Qwen3.8 Flash Next is an experimental vision-language architecture combining 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.
• All 3,671 converted tensors and 22 indexed shards were checked locally.
• The release payload was scanned for credentials, personal contact details, private paths, private network information, logs, caches, and private organisation data.
• Deterministic standalone and warmed oMLX server generation tests passed on Apple silicon.
• Quantisation can reduce output quality relative to BF16. Test the model on representative workloads before production use.

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.

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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-4bit --local-dir ./models/Qwen3.8-Flash-Next-MLX-4bit

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.

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