Instructions to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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": "kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF to start chatting

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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": "kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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 kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

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 "kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP" \
  --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"

kingjones777 commited on about 4 hours ago

Commit

84a69e6

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1 Parent(s): b516df7

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README.md CHANGED Viewed

@@ -95,3 +95,19 @@ per my build discipline.
 Converted and quantized from `ibm-granite/granite-4.2-3b` (Apache 2.0). This quantized build
 is released under the same Apache 2.0 license. The ROCmFPX runtime is a third-party fork;
 its own terms apply to the runtime, not to these weights.

 Converted and quantized from `ibm-granite/granite-4.2-3b` (Apache 2.0). This quantized build
 is released under the same Apache 2.0 license. The ROCmFPX runtime is a third-party fork;
 its own terms apply to the runtime, not to these weights.
+<!-- VARIANTS:START -->
+## All my quants of Granite-4.2-3B
+| build | what it is | size | tok/s (full GPU offload) |
+|---|---|---:|---:|
+| [`STRIX_LEAN`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFP4-STRIX_LEAN-GGUF) | my leaner 4-bit tier, Q6_K head — smallest of my 4-bit builds, the one most people want | 1.92 GiB | 60.69 |
+| [`COHERENT`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFP4-COHERENT-GGUF) | my 4-bit ROCmFP4 tier with the Q6_K-protected head — the balance I run day to day | 2.04 GiB | 71.55 |
+| [`Q8_0`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFPX-Q8_0-GGUF) | straight 8-bit ROCmFPX — highest fidelity I publish | 3.52 GiB | 46.36 |
+| [`Q8_0-AGENT`](https://huggingface.co/kingjones777/Granite-4.2-3B-ROCmFPX-Q8_0-AGENT-GGUF) | 8-bit ROCmFPX with the agent-tuned tensor set — for tool-calling work where precision matters | 3.59 GiB | 50.47 |
+All measured by me on a Ryzen AI MAX+ 395 (Strix Halo, gfx1151, ROCm 7.2.4) with the whole model on GPU (`-ngl 999`), 128-token greedy generation. A dash means I haven't measured that one yet — I won't put a number in a card I didn't measure.
+Base model: [ibm-granite/granite-4.2-3b](https://huggingface.co/ibm-granite/granite-4.2-3b)
+<!-- VARIANTS:END -->