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"

Myric commited on Jul 20

Commit

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+---
+license: apache-2.0
+base_model: ibm-granite/granite-4.0-h-tiny
+base_model_relation: quantized
+pipeline_tag: text-generation
+library_name: gguf
+tags:
+  - gguf
+  - moe
+  - apex
+  - quantized
+  - granite
+  - mamba
+  - hybrid
+  - llama.cpp
+---
+# Granite-4.0-H-Tiny — APEX GGUF
+MoE-aware, mixed-precision **APEX** quantization of
+[ibm-granite/granite-4.0-h-tiny](https://huggingface.co/ibm-granite/granite-4.0-h-tiny)
+— IBM's **hybrid Mamba-2 / Transformer MoE** (`granitemoehybrid`): 40 layers = 36
+Mamba-2 + 4 attention, a 64-routed + shared-expert MoE FFN on every layer, ~7B
+total / ~1B active, Apache-2.0.
+To my knowledge this is the first APEX quant of a Granite hybrid Mamba-2/MoE.
+APEX assigns precision per tensor role and per layer; here that meant teaching the
+recipe about the Mamba-2 mixer tensors the stock generator doesn't know (see *Method*).
+## Results
+Perplexity on wikitext-2-raw (test, 200×512-token windows), `llama-perplexity`.
+| File | Size | BPW | PPL | Δ vs bf16 |
+|------|------|-----|-----|-----------|
+| bf16 (reference) | 13 GB | 16.0 | 8.868 | — |
+| **APEX i-quality** | **4.4 GB** | 5.40 | **8.901** | **+0.38%** |
+Within **0.38% of full-precision perplexity at ~3× smaller**, and it runs comfortably
+on modest hardware (~14 GB bf16 → 4.4 GB). Coherent, ~110 tok/s on a single GPU.
+Built with a diverse imatrix (Bartowski `calibration_datav3`), full expert coverage.
+## Usage (llama.cpp)
+```bash
+llama-cli   -m granite-4.0-h-tiny-APEX-i-quality.gguf -ngl 999 -p "Hello"
+llama-server -m granite-4.0-h-tiny-APEX-i-quality.gguf -ngl 999 --host 0.0.0.0 --port 8080
+```
+Requires a llama.cpp build supporting the `granitemoehybrid` (a.k.a. `granitehybrid`)
+architecture.
+## Method
+APEX is a bit-allocation recipe over stock `llama-quantize --tensor-type-file`.
+Granite-H needed the **Mamba-2 mixer** tensors added to the map, which the stock
+APEX generator omits:
+- **Mamba-2** (36 layers): `ssm_in`, `ssm_conv1d`, `ssm_out` at mixer precision
+  (Q6_K); 1-D state (`ssm_a`, `ssm_d`, `ssm_dt`, `ssm_norm`) left F32.
+- **Attention** (4 layers): standard `attn_q/k/v/output` (Q6_K).
+- **MoE** (all 40 layers): routed `ffn_*_exps` on a layer-depth precision gradient
+  (Q6_K/Q5_K/IQ4_XS), **shared experts `ffn_*_shexp` at Q8_0** (always active → protect),
+  router left high. Routed intermediate dim 512 is 256-divisible → no IQ4_NL workaround.
+Config generation + patcher: `configs/`, `patch_granite_config.py`, `REPRODUCE.md`.
+Baseline: IBM's own bf16 GGUF.
+### Note: pinning the Mamba-2 recurrence to Q8 doesn't help
+A hand-roll variant pinning `ssm_in/out/conv1d` to Q8_0 scored **worse** at a
+**larger** size (4.5 GB, PPL 8.913) — the SSM/recurrence tensors aren't precision-
+sensitive here. Same null result we saw on Kimi-Linear's KDA. So **Q6_K is the right
+choice**; don't spend bits protecting the linear-recurrence state. Not shipped.
+## Attribution & licenses
+See [`LICENSE`](LICENSE) (Apache-2.0) and [`NOTICE`](NOTICE).
+- Base: **IBM** ([@ibm-granite](https://huggingface.co/ibm-granite)) — [granite-4.0-h-tiny](https://huggingface.co/ibm-granite/granite-4.0-h-tiny) (Apache-2.0)
+- Engine: **llama.cpp** ([@ggml-org](https://huggingface.co/ggml-org)) (MIT)
+- APEX: **Ettore Di Giacinto / LocalAI** ([@mudler](https://huggingface.co/mudler)) — [localai-org/apex-quant](https://github.com/localai-org/apex-quant) (MIT)
+- Calibration: **Bartowski** ([@bartowski](https://huggingface.co/bartowski)) — calibration_datav3
+Unofficial community quantization; not affiliated with or endorsed by IBM.