Qwen3.5-35B-A3B-APEX-GGUF

Qwen3.5-35B-A3B — APEX GGUF (torch imatrix)

MoE-aware, mixed-precision APEX quantization of Qwen/Qwen3.5-35B-A3B — a 35B-total / ~3B-active MoE (40 layers, 256 routed + 1 shared expert, hybrid GatedDeltaNet linear-attention + periodic full attention, with a NextN/MTP self-speculative head).

The importance matrix here is generated with a PyTorch band-serialized generator rather than llama-imatrix, for two concrete reasons on this architecture:

1. llama.cpp's imatrix tool is impractical for qwen35moe. The GatedDeltaNet linear-attention is a serial state-space recurrence; the imatrix collection callback breaks the GPU path that makes normal inference fast, so it falls back to a single CPU thread that no thread count can parallelize.
2. The torch generator covers the MTP/NextN head (blk.40.*) that llama-imatrix does not — it is memory-bounded (only band layers resident) so it runs on modest hardware regardless of model size.

update: -v2 files: identical bit allocation to the originals, except the 48 attn_gate coefficients are kept at F32 instead of the tier's attention band type. They parameterise a nonlinearity that multiplies the state path, so quantising them lets error compound along the sequence; F32 costs about 31 MB. Prefer -v2.

The -torch imatrix — what it is

Qwen3.5-35B-A3B-torch.imatrix is a standard GGUF-format importance matrix (in_sum2 + counts per tensor), bit-compatible with llama-quantize --imatrix. It was generated from the HF safetensors with a band-serialized PyTorch forward, calibrated on a general text corpus, covering all 40 transformer layers plus the NextN/MTP head.

Validation vs a reference llama.cpp imatrix

Compared per-tensor against bartowski's canonical llama.cpp imatrix (Qwen_Qwen3.5-35B-A3B-imatrix.gguf), computed independently on different calibration data:

Median correlation 0.966 against an independently-computed imatrix (different calibration data) validates the mapping and values. The only weakly-correlated tensors are the ssm_out projections (post-nonlinearity inputs) — a known property that does not affect quantization quality (the quantizer needs only relative per-channel importance). The torch imatrix is a strict superset (only-real = []).

PPL parity (measured)

The truer test: does the torch imatrix produce a quant as good as the llama.cpp one? Same i-compact recipe quantized with each imatrix, perplexity over 200×512-token wikitext-2 windows:

The torch and llama.cpp quants differ by 0.02 PPL — inside the ±0.073 error bars, i.e. statistically indistinguishable. The torch-generated imatrix produces a quant as good as the canonical llama.cpp one.

Repeated on a diverse code-heavy corpus (HumanEval + MBPP + GSM8K + prose, 342 windows) — more representative of a coding model than Wikipedia prose:

Parity holds on code/math too (Δ0.008, inside ±0.014) — the imatrix records per-channel activation magnitudes, so the method is domain-agnostic. (Absolute PPL is far lower here only because code is more predictable than prose; not comparable across corpora.)

Calibration used the diverse calibration_datav3 (prose + code + multilingual), so these quants are not domain-handicapped. The imatrix's calibration corpus is a knob: a code-weighted calibration would favor coding channels further, at a small cost elsewhere — useful if you're specializing for a single domain.

Files

• Qwen3.5-35B-A3B-torch.imatrix — the PyTorch-generated importance matrix (this repo).
• Qwen3.5-35B-A3B-APEX-i-quality-torch.gguf (23.5 GB) — largest/highest-fidelity tier, quantized with the torch imatrix above.
• Qwen3.5-35B-A3B-APEX-i-compact-torch.gguf (17.4 GB) — smaller, more aggressive tier.
• *-v2.gguf — same tiers with the state/gate coefficients (ssm_alpha, ssm_beta) stored at F32. All other tensors are unchanged.
• llama.cpp reference imatrix — not re-hosted; see bartowski/Qwen_Qwen3.5-35B-A3B-GGUF.

Attribution

• Base model: Qwen — Qwen/Qwen3.5-35B-A3B.
• Reference imatrix for validation: bartowski — Qwen_Qwen3.5-35B-A3B-GGUF.
• APEX recipe & toolkit: LocalAI — localai-org/apex-quant.
• Quantization engine: llama.cpp (ggml-org).

Unofficial community quantization; not affiliated with or endorsed by Qwen.