---

license: apache-2.0

base_model: Qwen/Qwen3.5-35B-A3B

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

- gguf

- moe

- apex

- quantized

- imatrix

- torch-imatrix

- qwen3_5_moe

- llama.cpp

---

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.

1. 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.

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:

| Metric | Value |

|--------|-------|

| Tensors covered (torch) | 523 (incl. 13 MTP-head tensors) |

| Tensors covered (bartowski) | 510 (no MTP head) |

| Per-tensor correlation (median) | 0.966 |

| Per-tensor correlation (mean) | 0.877 |

| Lowest-correlation tensors | blk.*.ssm_out.weight only |

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:

| i-compact quantized with | PPL | Δ vs bf16 |

|--------------------------|----:|----------:|

| bf16 (reference) | 6.620 | — |

| bartowski llama.cpp imatrix | 6.756 | +2.05% |

| this torch imatrix | 6.775 | +2.34% |

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:

| i-compact quantized with | PPL (code-heavy) | Δ vs bf16 |

|--------------------------|-----------------:|----------:|

| bf16 (reference) | 2.247 | — |

| bartowski llama.cpp imatrix | 2.310 | +2.77% |

| this torch imatrix | 2.318 | +3.15% |

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).
• APEX quants (-torch suffix) — coming: i-quality, i-compact, i-mini,

quantized with the torch imatrix above.

• 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.