Instructions to use Myric/granite-4.0-h-small-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Myric/granite-4.0-h-small-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Myric/granite-4.0-h-small-APEX-GGUF",
	filename="granite-4.0-h-small-APEX-handroll.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Myric/granite-4.0-h-small-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/granite-4.0-h-small-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/granite-4.0-h-small-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Myric/granite-4.0-h-small-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/granite-4.0-h-small-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/granite-4.0-h-small-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Myric/granite-4.0-h-small-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/granite-4.0-h-small-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/granite-4.0-h-small-APEX-GGUF

Use Docker

docker model run hf.co/Myric/granite-4.0-h-small-APEX-GGUF

LM Studio
Jan

vLLM

How to use Myric/granite-4.0-h-small-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Myric/granite-4.0-h-small-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/granite-4.0-h-small-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Myric/granite-4.0-h-small-APEX-GGUF

Ollama
How to use Myric/granite-4.0-h-small-APEX-GGUF with Ollama:
```
ollama run hf.co/Myric/granite-4.0-h-small-APEX-GGUF
```

Unsloth Studio

How to use Myric/granite-4.0-h-small-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Myric/granite-4.0-h-small-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Myric/granite-4.0-h-small-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Myric/granite-4.0-h-small-APEX-GGUF to start chatting

How to use Myric/granite-4.0-h-small-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-4.0-h-small-APEX-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Myric/granite-4.0-h-small-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Myric/granite-4.0-h-small-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-4.0-h-small-APEX-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Myric/granite-4.0-h-small-APEX-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Myric/granite-4.0-h-small-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Myric/granite-4.0-h-small-APEX-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Myric/granite-4.0-h-small-APEX-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Myric/granite-4.0-h-small-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/Myric/granite-4.0-h-small-APEX-GGUF
```

Lemonade

How to use Myric/granite-4.0-h-small-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Myric/granite-4.0-h-small-APEX-GGUF

Run and chat with the model

lemonade run user.granite-4.0-h-small-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Granite-4.0-H-Small — APEX GGUF

MoE-aware, mixed-precision APEX quantizations of ibm-granite/granite-4.0-h-small — IBM's hybrid Mamba-2 / Transformer MoE (granitemoehybrid): 40 layers (36 Mamba-2 + 4 attention), 64-routed + shared-expert MoE, ~32B total / ~9B active, Apache-2.0. To my knowledge the first APEX quant of a 32B Granite hybrid.

Results & which file to pick

Perplexity on wikitext-2-raw (test, 200×512-token windows), llama-perplexity. All quants are imatrix-guided (diverse calibration).

File	Size	BPW	PPL	Δ vs bf16	Fits (VRAM)
bf16 (reference)	61 GB	16.0	5.752	—	—
APEX-i-quality	21 GB	5.44	5.781	+0.50%	24 GB — best quality
APEX-i-compact	16 GB	4.13	5.998	+4.28%	24 GB
APEX-i-mini	13 GB	3.34	6.471	+12.49%	16 GB — smallest, quality trade

i-quality — recommended; near-lossless (+0.5%). Q6_K/Q5_K experts, Q8_0 shared, Q6_K attn/SSM.
i-compact — Q3_K mid experts; a real but usable quality step (+4.3%) for tighter fits.
i-mini — IQ2_S mid experts; smallest that stays coherent (16 GB cards), but a clear quality drop (+12.5%) — pick it only when you must fit and can tolerate the hit.

Note on minimum size: Granite's routed-expert dim is 640 (not 256-divisible), so i-quants can't apply to the ffn_down_exps — they fall back to ~Q4_K. That floors the practical minimum around ~13 GB (i-mini) and, as a bonus, protects the down projections' quality. Going below that (nano/IQ1) buys little here.

Usage (llama.cpp)

llama-cli   -m granite-4.0-h-small-APEX-i-quality.gguf -ngl 999 -p "Hello"
llama-server -m granite-4.0-h-small-APEX-i-compact.gguf -ngl 999 --host 0.0.0.0 --port 8080

Requires a llama.cpp build supporting the granitemoehybrid architecture.

Method

APEX = per-tensor-role, per-layer precision over stock llama-quantize --tensor-type-file. Granite-H needed its Mamba-2 mixer tensors added to the map (the stock generator omits them):

Mamba-2 (36 layers): ssm_in, ssm_conv1d, ssm_out at mixer precision; 1-D state (ssm_a/d/dt/norm) left F32.
Attention (4 layers): attn_q/k/v/output.
MoE (all 40): routed ffn_*_exps on a depth gradient; shared ffn_*_shexp at Q8_0/Q6_K.

Baseline: IBM's own f16 GGUF. imatrix from Bartowski calibration_datav3. Config gen + patcher: configs/, patch_granite_config.py, REPRODUCE.md.

Null result: protecting the Mamba-2 recurrence doesn't help

A hand-roll pinning ssm_in/out/conv1d to Q8_0 (22 GB, PPL 5.779) was within noise of i-quality — the SSM/recurrence tensors aren't precision-sensitive. Consistent with the same finding on Kimi-Linear's KDA and Granite-4.0-H-Tiny.

It is shipped here as granite-4.0-h-small-APEX-handroll.gguf for transparency and reproduction, but there's no reason to use it — it's slightly larger than i-quality (22 GB vs 21 GB) for no measurable quality gain (PPL 5.779 vs 5.781). Use i-quality.

Attribution & licenses

See LICENSE (Apache-2.0) and NOTICE.

Base: IBM (@ibm-granite) — granite-4.0-h-small (Apache-2.0)
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 IBM.

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GGUF

Model size

32B params

Architecture

granitehybrid

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Myric/granite-4.0-h-small-APEX-GGUF

Base model

ibm-granite/granite-4.0-h-small

Quantized

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