feat: Qwen3.8-Flash-Next (qwen4_exp) support, text and vision - #101
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180B MoE: 512 routed experts at top-10 and 640 wide, hybrid Gated DeltaNet + Qwen Sparse Attention, 262K context. Adds the architecture, the vision path, and the converter features the model needed. Architecture - models/qwen4_exp.py vendored from the open mlx-lm PR #1788, imports rewritten absolute. compat.py aliases it into mlx_lm.models and self-disables the moment mlx-lm ships the type natively. One deliberate deviation from upstream: an optional rope_cs argument threaded down to Attention, defaulting to None so the text path is unchanged, so the vision path can supply 3-D MRoPE positions. - models/qwen4_exp_vision.py: tower loading, 3-D position building, interleaved MRoPE, feature splicing. The tower is a Qwen3-VL tower and mlx-vlm's implementation loads all 333 tensors strict, so it is reused rather than reimplemented. mlx-vlm is imported lazily and only there. Converter - --quantize-extras quantizes the bf16 remainder the polar path never touches, nn.Embedding above all. Not optional on this model: its n-gram/PLE table is 51.2B parameters, 28% of the weights, and skipping it yields a ~124 GiB build instead of ~52 GiB. A width that is not divisible by the group size now warns loudly rather than being dropped in silence. - --protect-expert-layers for the text and streaming converters, selected by module type plus layer index rather than expert-container name. - bits_for_path exempts a singular shared_expert; the always-on shared expert was landing in the sub-2-bit expert tier. - The QSA block indexer stays at full precision: it top-k selects which KV blocks a query reads, a discrete choice like a router, and costs 0.04 GiB. Fixes - Streaming affine-extras held every source weight alive by iterating list(model.named_modules()); on this model that is the whole 95.4 GiB bf16 n-gram table, and it killed three conversions 71-90% through that phase. - SafetensorsExpertReader.close() also catches AttributeError, since os may already be None during interpreter teardown. Tests: 626 passing, 3 skipped. New regressions cover the memory retention (with lazily created weights, which is what made the original bug invisible), the silent group-size exclusion, the singular shared_expert, and a check that rope_cs actually reaches Attention rather than being threaded and ignored.
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Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughAdds Qwen4-Exp text and vision support, compatibility registration, expert-layer protection, affine extras quantization, streaming cleanup, CLI options, packaging updates, and regression tests. ChangesQwen4-Exp model and quantization
Priority: ➖ Normal Estimated code review effort: 5 (Critical) | ~90 minutes Change: Feature · Severity of issue fixed: Medium Sequence Diagram(s)sequenceDiagram
participant CLI
participant convert_streaming
participant TurboQuantConfig
participant quantize_affine_extras
participant OutputWriter
CLI->>convert_streaming: pass extras and protection options
convert_streaming->>TurboQuantConfig: create protected-layer configuration
convert_streaming->>quantize_affine_extras: quantize eligible extra modules
quantize_affine_extras->>OutputWriter: stream quantized module data
Merge Risk: ⚪ Minimal · up to The added model, vision, conversion, cache, packaging, and optional-dependency paths include targeted regression coverage, with no unresolved concrete merge risk identified. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 29.66% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 145 functions across 15 files. (2 skipped: 2 unsupported.)
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…t [vlm] Two small changes that predate the qwen4_exp work and belong in the same release. - pyproject: add turboquant_mlx.benchmarks to the shipped packages. The README and the model cards tell people to run `python -m turboquant_mlx.benchmarks.eval_vlm_perplexity ...`, which until now only worked from a git checkout. The directory is a real package and holds pure scripts; the results JSONs beside them are records and nothing reads one at runtime. - serve_vlm: route the first mlx_vlm import through _require_mlx_vlm(). Without the [vlm] extra installed, `turboquant-serve-vlm --help` ended in a ModuleNotFoundError traceback instead of the one line that tells you how to fix it.
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Inline comments:
In `@models/qwen4_exp_vision.py`:
- Around line 134-144: Update splice_image_features to explicitly reject inputs
with batch size greater than one before processing masks or scattering features.
Preserve the existing single-batch behavior and raise a clear ValueError for
unsupported multi-sample batches.
In `@quantize_model.py`:
- Around line 97-100: Update _eligible to apply the existing _should_quantize
linear-selection guard before accepting generic to_quantized extras, while
preserving router exclusions and keeping embeddings eligible. Ensure small
nn.Linear projections rejected by _should_quantize remain full precision.
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CHANGELOG.mdcompat.pyconfig.pyconvert.pyconvert_streaming.pymodels/qwen4_exp.pymodels/qwen4_exp_vision.pyquantize_model.pystream/safetensors_reader.pytests/test_layer_protection.pytests/test_qwen4_exp.pytests/test_qwen4_exp_vision.pytests/test_safetensors_reader_teardown.pytests/test_streaming_extras_memory.py
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Automated review triageCI: all green — CodeQL, Analyze (python), build sdist, pytest on 3.10 / 3.11 / 3.12. CodeQL
CodeRabbitNo actionable code comments. One pre-merge warning: docstring coverage 25.93% against an 80% threshold, over 135 functions in 13 files. Measured per file before deciding:
Chasing the threshold would mean adding boilerplate to one-line constructors and to vendored upstream code. Not doing that; flagging it here so the decision is explicit rather than ignored. |
Both from CodeRabbit review on #101. - quantize_affine_extras accepted any module exposing to_quantized, so --quantize-extras re-quantized the narrow linears the polar path rejects on purpose (output_dims < 32: Kimi K3's AttnRes score projections, and Qwen3.8-Flash-Next's 96 hyper-connection block_inject_weight matrices of shape (4, 640)). Quantization noise on a gating vector costs quality for ~0 bytes. Embeddings are unaffected -- _should_quantize rejects every nn.Embedding and claiming them is this tier's whole purpose. - splice_image_features counted placeholders across the batch but scattered into row 0 only, so a batch of 2 could pass the count check and silently leave the other rows' placeholder embeddings zeroed. Now rejects batch > 1 explicitly. Tests: 628 passing, 3 skipped.
…base Found while triaging CodeQL #92 (py/import-of-mutable-attribute) on #101. - convert_cache_to_turboquant replaced every KVCache *subclass* as well, and qwen4_exp's _AttnCache is one: it carries the sparse-attention indexer keys. Those were dropped, so each decode step saw only its own key, fell under indexer_budget and attended densely -- no error, a different model. On the toy model: max logit drift 0.22 with the indexer dropped, 0.003 with 8-bit KV and the indexer kept. Only exact KVCache converts now; subclasses stay at full precision with a once-per-process warning (serve converts per request). _AttnCache is the only KVCache subclass in mlx-lm 0.31.3 or any local port, so no other model changes. - qwen4_exp called a `from mlx_lm.models.base import` copy of scaled_dot_product_attention, which the --kv-fused patch misses when the model is imported after the patch is installed. It now resolves the function through the module at call time, as laguna and kimi_k3 already do. Documented as the second deliberate deviation from the vendored upstream. Tests: 3 new, each failing on the old code on its own assertion. 631 passing, 3 skipped.
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| tiny model, prefill 12 + decode 10 past the budget | max logit error |
|---|---|
plain KVCache, indexer dropped (full precision) |
0.216 |
| 8-bit TurboQuant KV, indexer dropped (what shipped) | 0.216 |
| 8-bit TurboQuant KV, indexer kept | 0.003 |
Fix: only exact KVCache converts. Subclasses stay at full precision with a warning printed once per process; serve converts once per request, so a per-call warning would repeat. _AttnCache is the only KVCache subclass in mlx-lm 0.31.3 or any local port, so no other model changes. Carrying the indexer through a TurboQuant cache would also work (trim, state and merge would need it too). That can be a follow-up; it is not a pre-merge fix.
2. CodeQL #92 itself
The --kv-fused patch replaces mlx_lm.models.base.scaled_dot_product_attention and re-binds copies only in modules already imported. It reaches qwen4_exp today through its mlx_lm.models alias, but would miss it if the model were imported after the patch was installed. Attention now resolves the function through the module at call time, as laguna and kimi_k3 already do. This is documented as the second deliberate deviation from the vendored upstream. The alert should close on the rescan.
Tests: 3 new tests, each failing on the old code on its own assertion (cache type swapped; _AttnCache replaced; the spy on base never called). 631 passing, 3 skipped.
2 participants
Adds support for Qwen/Qwen3.8-Flash-Next — 180B MoE, 512 routed experts at top-10 and 640 wide, hybrid Gated DeltaNet + Qwen Sparse Attention — plus the converter features it needed and the vision path.
Result: a 52.00 GiB build that runs fully resident on a 64 GB Mac at ~15 tok/s with vision. Every published MLX build of this model is ≥63.0 GiB and none of them load resident.
Architecture
models/qwen4_exp.py— vendored from the still-open mlx-lm PR #1788, imports rewritten absolute.compat.pyaliases it intomlx_lm.modelsand self-disables once mlx-lm ships the type natively, so this file stops being consulted with no code change. One deliberate deviation from upstream, documented in the module docstring: an optionalrope_csargument threaded toAttention, defaulting toNoneso the text path is unchanged.models/qwen4_exp_vision.py(new) — the tower is a Qwen3-VL tower: all 333 vision tensors load into mlx-vlm's implementationstrict=True, so it is reused rather than reimplemented. Adds 3-D position building, interleaved MRoPE (mrope_section [11, 11, 10]), and feature splicing. mlx-vlm is imported lazily and only here — the text model keeps no vision dependency.Converter
--quantize-extras,--extras-bits,--extras-group-sizenn.Embeddingabove all. Not optional on this model: its n-gram/PLE table is 51.2B parameters, 28% of the weights. Skipping it gives a ~124 GiB build instead of ~52 GiB.--protect-expert-layers,--protect-bits.experts.and cannot match this model'sswitch_mlp.Two silent-miss fixes in the same area:
bits_for_pathexemptedshared_experts(plural, Qwen3/Kimi spelling) but not the singularshared_expertthis model uses, so an always-on expert was landing in the sub-2-bit tier.Fixes
list(model.named_modules()), which keeps each original module alive for the whole loop; on this model that is the entire 95.4 GiB bf16 n-gram table. It killed three 180B conversions 71–90% of the way through that phase. Now iterates paths and releases each module.SafetensorsExpertReader.close()also catchesAttributeError— at interpreter teardownosmay already beNone, which printed an ignored-exception traceback on every streaming exit.Tests
626 passing, 3 skipped (+28). The new regressions target the failure modes that were hard to see:
shared_expertrope_csactually reachesAttention, rather than being threaded and ignoredAlso in this PR (second commit) — two changes that predate this work
Folded in so they ship in the same release rather than trailing it:
pyproject: shipturboquant_mlx.benchmarks. The README and the modelcards tell people to run
python -m turboquant_mlx.benchmarks.eval_vlm_perplexity ...,which until now worked only from a git checkout. It is a real package
(
__init__.pypresent) holding pure scripts.serve_vlm: route the firstmlx_vlmimport through_require_mlx_vlm().Without the
[vlm]extra,turboquant-serve-vlm --helpended in aModuleNotFoundErrortraceback instead of the one-line install instruction.Notes for review
models/qwen4_exp.pyis vendored from an unmerged PR. When #1788 lands, the alias self-disables and the file can be deleted; therope_csdeviation is the only thing to re-apply.qwen4_exp).🤖 Generated with Claude Code
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Summary by CodeRabbit
New Features
Bug Fixes
Tests