Instructions to use Myric/granite-4.0-h-tiny-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/granite-4.0-h-tiny-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-tiny-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/granite-4.0-h-tiny-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-tiny-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf Myric/granite-4.0-h-tiny-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-tiny-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Myric/granite-4.0-h-tiny-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-tiny-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Myric/granite-4.0-h-tiny-APEX-GGUF

Use Docker

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

LM Studio
Jan

vLLM

How to use Myric/granite-4.0-h-tiny-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-tiny-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-tiny-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

How to use Myric/granite-4.0-h-tiny-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-tiny-APEX-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-tiny-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use Myric/granite-4.0-h-tiny-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-tiny-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-tiny-APEX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Myric/granite-4.0-h-tiny-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-tiny-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-tiny-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"

granite-4.0-h-tiny-APEX-GGUF

File size: 1,335 Bytes

3e77dba

# Reproducing this APEX quant

Full imatrix pipeline; all runs on ~14 GB, GPU-friendly. Components MIT/Apache (see NOTICE).

## Pinned
- **llama.cpp** supporting `granitemoehybrid` + `--tensor-type-file` (e.g. commit `bbf4a8a`/b8833+).
- **apex-quant** commit `a445a12` for `generate_config.sh` (bundled).

## Baseline + calibration
```bash
hf download ibm-granite/granite-4.0-h-tiny-GGUF granite-4.0-h-tiny-bf16.gguf --local-dir .
# calibration_datav3: https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8
```

## Config (regenerate)
```bash
bash generate_config.sh --profile i-quality --layers 40 --dense-layers 0 -o granite_iq.base.txt
python patch_granite_config.py granite_iq.base.txt configs/granite_i-quality.txt   # adds Mamba-2 ssm_in/conv1d
```

## imatrix -> quantize -> eval
```bash
llama-imatrix -m granite-4.0-h-tiny-bf16.gguf -f calibration_datav3.txt \
  -o granite-4.0-h-tiny.imatrix -ngl 999
llama-quantize --tensor-type-file configs/granite_i-quality.txt \
  --imatrix granite-4.0-h-tiny.imatrix \
  granite-4.0-h-tiny-bf16.gguf granite-4.0-h-tiny-APEX-i-quality.gguf Q6_K
# eval (PPL prints to STDERR -> 2>&1):
llama-perplexity -m granite-4.0-h-tiny-APEX-i-quality.gguf -f wiki.test.raw -ngl 999 --chunks 200 2>&1 \
  | grep -oP 'Final estimate: PPL = \K[0-9.]+'
```
Expected: ~8.90 (bf16 ~8.87).