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.
| Item | Value |
|---|---|
| Base model | Qwen/Qwen3.8-Flash-Next |
| Format | MLX safetensors |
| Quantisation | oQ2 mixed precision; 2-bit affine base, protected layers at 5/8-bit |
| Base group size | 32 |
| Weight shards | 14 |
| Weight size | 67.67 GB (63.02 GiB) |
| Configured context | 262,144 tokens |
| Architecture | qwen4_exp vision-language sparse MoE |
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_exparchitecture. Use an oMLX or MLX-VLM build that explicitly listsqwen4_expsupport. 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.
| Architecture detail | Upstream value |
|---|---|
| Language-model parameters | 125B total / 6B active |
| N-gram embedding | 51B parameters |
| Layers | 48 |
| Routed / active experts | 512 / 10, plus 1 shared |
| Attention heads / KV heads | 24 / 2 |
| Hidden size | 2,560 |
| Native configured context | 262,144 tokens |
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.