Ornith-1.5-35B-A3B-EXL3-3.5bpw

Community EXL3 quantization of ornith-ai/Ornith-1.5-35B-A3B. Not an official Ornith release.

Why this one is different

Most EXL3 quants of MoE models use the -hq switch, which gives attention and shared experts a small bitrate bump (5 bpw). On this architecture that is the wrong place to save bits: the model is 90% routed-expert parameters (256 experts, top-8 routing), which is highly redundant and cheap to compress, while the attention backbone — in particular the GatedDeltaNet (linear attention) projections that form the only information pathway in 30 of 40 layers — is small but extremely sensitive.

Layer map used here (popularized by brandonmusic for GLM-5.2):

• Routed expert matrices: 3.5 bpw, MCG codebook
• Everything else BF16: GatedDeltaNet in/out projections, full-attention q/k/v/o, shared experts, embeddings, lm_head, norms, router
• MTP draft layer at 4 bpw, vision tower BF16

Quantization

• exllamav3 1.4.2 convert, MCG codebook, stock calibration (250 rows x 2048 tokens)
• Exact per-module bitrates in quantization_config.json
• Artifact size: 20 GB

Base-vs-quantization check

exllamav3 eval/model_diff vs the BF16 base, 100 rounds x 2048 tokens (wikitext):

Checkpoint	KL(A->B)	Top-1 agreement
Ornith-1.5-35B-A3B-EXL3-3.5bpw	0.0540	90.5%
EXL3 2.75 bpw	0.1005	87.1%
EXL3 3 bpw	0.0732	88.9%
EXL3 3.5 bpw	0.0540	90.5%
reference: plain -hq 3 bpw	0.2509	79.9%

BF16 base perplexity on the same data: 8.717; all three unpruned rungs are within noise of it (8.698 / 8.711 / 8.719).

Runtime — tested recipe for 4x RTX 3090 (24 GB)

Served with TabbyAPI (exllamav3 backend), tensor-parallel across 4 GPUs:

model:
  model_dir: /path/to/models
  model_name: Ornith-1.5-35B-A3B-EXL3-3bpw   # match your local dir name
  backend: exllamav3
  max_seq_len: 262144        # native context
  cache_mode: Q4
  max_batch_size: 16         # recurrent-state slots; keep >= expected concurrency
  tensor_parallel: true
  gpu_split_auto: true

Notes, all learned the hard way:

• exllamav3 1.4.3 runtime patch required for this layer map. The TP import for unquantized GatedDeltaNet projections has a copy-paste bug: in exllamav3/modules/quant/fp16.py, tp_import_split_n, change id_w = exported["suh"] to id_w = exported["weight"]. Without it, TP loading crashes with KeyError: 'suh'. Single-GPU loading is unaffected.
• max_batch_size must cover your concurrency: GatedDeltaNet layers keep per-sequence recurrent state; the default of 4 slots exhausts quickly under parallel requests.
• Keep agent-harness context budgets consistent with the server: max_input_tokens + max_output_tokens <= max_seq_len (with margin).
• Throughput at 4x 3090 TP4: ~50 tok/s single stream, ~180 tok/s aggregate at concurrency 4; weights occupy ~4.5-5.5 GB per GPU.
• vLLM with --quantization exl3 also works; TabbyAPI is what we validated.

Reproducing this quant (exact pinned recipe)

Conversion environment

Component	Version
exllamav3 (convert)	1.4.2
Python	3.14.7
torch	2.11.0+cu128
triton	3.6.0
CUDA toolkit	13.3 (V13.3.73)
NVIDIA driver	610.57.04
Hardware	4x RTX 3090 (TP4 conversion)

Conversion command

python convert.py \
  -i <bf16_model_dir> -o <out_dir> -w <work_dir> \
  -b 3.5 -hb 16 -mb 4 -cb mcg \
  -cr 250 -cc 2048 \
  -d 0,1,2,3

The layer map is forced with this patch, imported before convert runs (monkey-patches create_q_strategy in exllamav3.conversion.allocation and the reference imported by exllamav3.conversion.convert_model):

# hq16_patch.py
import re as _re
from exllamav3.conversion import allocation as _alloc
from exllamav3.conversion import convert_model as _cm

_orig = _alloc.create_q_strategy
_PAT = (_re.compile(r"\.linear_attn\."), _re.compile(r"\.self_attn\."),
        _re.compile(r"\.shared_expert"))

def _patched(*args, **kwargs):
    f_targets, fb = _orig(*args, **kwargs)
    n = 0
    for k in list(f_targets.keys()):
        if any(p.search(k) for p in _PAT):
            f_targets[k] = 16
            n += 1
    print(f"[hq16] forced {n} modules to 16 bits")
    return f_targets, fb

_alloc.create_q_strategy = _patched
_cm.create_q_strategy = _patched

Expected confirmation in the conversion log: [hq16] forced 257 modules to 16 bits (unpruned) — 90 linear-attention projections + 40 full-attention + 120 shared-expert

• 7 MTP modules.

Conversion note: with triton 3.6.0, GatedDeltaNet kernels can trip a Triton autotuner re-entrancy bug (nested autotuned kernels clobbering nargs); if the conversion crashes inside triton/runtime/autotuner.py, capture nargs into a local before the benchmark() closure.

Runtime environment (tested)

Component	Version
TabbyAPI	git 4a4f9f44820303593844f092d424bb7506008733 (2026-08-24)
exllamav3 (runtime)	1.4.3+cu128.torch2.9.0 prebuilt wheel
Python	3.12.14
torch	2.9.0+cu128

Required runtime patch for TP loading (exllamav3 1.4.3 bug, hit only when GatedDeltaNet projections are stored unquantized):

--- exllamav3/modules/quant/fp16.py
-        id_w = exported["suh"]        # in tp_import_split_n
+        id_w = exported["weight"]

Attribution

• brandonmusic — the GLM-5.2 EXL3 TR3 3bpw recipe (BF16 backbone, 3bpw routed experts) that this layer map follows.
• turboderp / turboderp-org — the EXL3 format and exllamav3, both conversion and runtime.
• CerebrasResearch — REAP expert pruning (pruned variants).
• ornith-ai — the base model. This quant inherits its capabilities, limitations, and MIT license; refer to the base model card before deployment.

If these quants are useful to you, consider supporting the work: donate.sybilsolutions.ai