Instructions to use librepowerai/Granite-4.1-8B-Power with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use librepowerai/Granite-4.1-8B-Power with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="librepowerai/Granite-4.1-8B-Power",
	filename="Granite-4.1-8B-Q4_K_M-be.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use librepowerai/Granite-4.1-8B-Power with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M

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 librepowerai/Granite-4.1-8B-Power:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M

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 librepowerai/Granite-4.1-8B-Power:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf librepowerai/Granite-4.1-8B-Power:Q4_K_M

Use Docker

docker model run hf.co/librepowerai/Granite-4.1-8B-Power:Q4_K_M

LM Studio
Jan
Ollama
How to use librepowerai/Granite-4.1-8B-Power with Ollama:
```
ollama run hf.co/librepowerai/Granite-4.1-8B-Power:Q4_K_M
```

Unsloth Studio

How to use librepowerai/Granite-4.1-8B-Power 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 librepowerai/Granite-4.1-8B-Power 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 librepowerai/Granite-4.1-8B-Power to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for librepowerai/Granite-4.1-8B-Power to start chatting

Atomic Chat new
Docker Model Runner
How to use librepowerai/Granite-4.1-8B-Power with Docker Model Runner:
```
docker model run hf.co/librepowerai/Granite-4.1-8B-Power:Q4_K_M
```

Lemonade

How to use librepowerai/Granite-4.1-8B-Power with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull librepowerai/Granite-4.1-8B-Power:Q4_K_M

Run and chat with the model

lemonade run user.Granite-4.1-8B-Power-Q4_K_M

List all available models

lemonade list

Granite-4.1-8B — Q4_K_M for IBM Power (Linux ppc64le + AIX)

Granite-4.1-8B quantized to Q4_K_M with a Q6_K output head for fast CPU inference on IBM Power — POWER9 (VSX) and POWER10/11 (MMA-accelerated) via LibrePower. No GPU required. Size: 5.0G.

Run it

Ubuntu / Debian ppc64le:

curl -fsSL https://linux.librepower.org/install.sh | sudo sh
sudo apt install librepower-llama
wget https://huggingface.co/librepowerai/Granite-4.1-8B-Power/resolve/main/Granite-4.1-8B-Q4_K_M.gguf
lp-llama-completion -m Granite-4.1-8B-Q4_K_M.gguf -p "Hello!" -n 64 -t $(nproc)

IBM AIX 7.3 (big-endian):

dnf install llama-aix
wget https://huggingface.co/librepowerai/Granite-4.1-8B-Power/resolve/main/Granite-4.1-8B-Q4_K_M-be.gguf
lp-llama-completion -m Granite-4.1-8B-Q4_K_M-be.gguf -p "Hello!" -n 64 -t $(nproc)

Files

Granite-4.1-8B-Q4_K_M.gguf — little-endian (Ubuntu/Linux ppc64le)
Granite-4.1-8B-Q4_K_M-be.gguf — big-endian (IBM AIX)

Good for

IBM 8B dense, signed, ISO-42001: enterprise RAG, tool-calling, structured JSON, code

Credits

Base model by its original authors (Apache-2.0). Quantization & Power packaging: LibrePower.

Downloads last month: 43

GGUF

Model size

9B params

Architecture

granite

Hardware compatibility

4-bit

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Model tree for librepowerai/Granite-4.1-8B-Power

Base model

ibm-granite/granite-4.1-8b

Quantized

(47)

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