Instructions to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Use Docker

docker model run hf.co/Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

LM Studio
Jan

vLLM

How to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Myric/ERNIE-4.5-21B-A3B-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Myric/ERNIE-4.5-21B-A3B-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Ollama
How to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with Ollama:
```
ollama run hf.co/Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
```
Unsloth Desktop
Docker Model Runner
How to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/Myric/ERNIE-4.5-21B-A3B-APEX-GGUF
```

Lemonade

How to use Myric/ERNIE-4.5-21B-A3B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Myric/ERNIE-4.5-21B-A3B-APEX-GGUF

Run and chat with the model

lemonade run user.ERNIE-4.5-21B-A3B-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

ERNIE-4.5-21B-A3B — APEX GGUF

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)

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.

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GGUF

Model size

22B params

Architecture

ernie4_5-moe

Hardware compatibility

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