MoE-aware, mixed-precision APEX quantizations of baidu/ERNIE-4.5-21B-A3B-PT — Baidu's ernie4_5-moe: 28 layers (dense layer 0 + 27 MoE), 64 routed + 2 shared experts, top-6 routing, ~21B total / ~3B active, Apache-2.0.

Results & which file to pick

Perplexity on wikitext-2-raw (test, 200×512-token windows), llama-perplexity. Both quants are imatrix-guided (Bartowski calibration_datav3).

File	Size	BPW	PPL	Δ vs bf16	Fits (VRAM)
bf16 (reference)	41 GB	16.0	7.362	—	—
APEX-i-quality	15 GB	5.63	7.352	−0.14% (at parity)	16 GB — best quality
APEX-i-compact	11 GB	4.11	7.640	+3.77%	12 GB — smallest usable

i-quality — recommended; effectively lossless — its PPL lands a hair below the bf16 reference (within noise). Q6_K/Q5_K experts on a depth gradient, Q8_0 shared, Q6_K attention. Runs on a 16 GB card.
i-compact — Q4_K_M-class mid experts; a real but usable step (+3.8%) that fits a 12 GB card.

ERNIE's routed-expert intermediate dim is 1536 (256-divisible), so i-quants apply cleanly to every expert tensor — no IQ4_NL fallback needed.

Usage (llama.cpp)

bash

llama-cli    -m ERNIE-4.5-21B-A3B-APEX-i-quality.gguf -ngl 999 -p "Hello"
llama-server -m ERNIE-4.5-21B-A3B-APEX-i-compact.gguf -ngl 999 --host 0.0.0.0 --port 8080

Requires a llama.cpp build supporting the ernie4_5-moe architecture.

Method

APEX = per-tensor-role, per-layer precision over stock llama-quantize --tensor-type-file. ERNIE-4.5-21B is a standard MoE — conventional ffn_*_exps / ffn_*_shexp tensor names, a single dense layer 0 (--dense-layers 1) — so it used the stock APEX config generator, no patching (unlike the Kimi/Granite/Moonlight hybrids in this collection). Routed experts on a layer-depth gradient; shared experts protected at Q8_0/Q6_K; router left high.

Baseline: bartowski's bf16 GGUF. imatrix from Bartowski calibration_datav3. See REPRODUCE.md and configs/.

Attribution & licenses

See LICENSE (Apache-2.0) and NOTICE.

Base: Baidu (@baidu) — ERNIE-4.5-21B-A3B-PT (Apache-2.0)
bf16 GGUF: bartowski (@bartowski) — source
Engine: llama.cpp (@ggml-org) (MIT)
APEX: Ettore Di Giacinto / LocalAI (@mudler) — localai-org/apex-quant (MIT)
Calibration: Bartowski (@bartowski) — calibration_datav3

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

Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

ERNIE-4.5-21B-A3B — APEX GGUF

Results & which file to pick

Usage (llama.cpp)

Method

Attribution & licenses

Myric/
ERNIE-4.5-21B-A3B-APEX-GGUF