Instructions to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ibm-granite/granite-4.0-tiny-base-preview-GGUF", dtype="auto", device_map="auto")

llama-cpp-python

How to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ibm-granite/granite-4.0-tiny-base-preview-GGUF",
	filename="granite-4.0-tiny-base-preview-Q2_K.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 ibm-granite/granite-4.0-tiny-base-preview-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 ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-4.0-tiny-base-preview-GGUF: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 ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ibm-granite/granite-4.0-tiny-base-preview-GGUF: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 ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with Ollama:
```
ollama run hf.co/ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
```

Unsloth Studio

How to use ibm-granite/granite-4.0-tiny-base-preview-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 ibm-granite/granite-4.0-tiny-base-preview-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 ibm-granite/granite-4.0-tiny-base-preview-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ibm-granite/granite-4.0-tiny-base-preview-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M
```

Lemonade

How to use ibm-granite/granite-4.0-tiny-base-preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ibm-granite/granite-4.0-tiny-base-preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.0-tiny-base-preview-GGUF-Q4_K_M

List all available models

lemonade list

This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model.

Please reference the base model's full model card here: https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview

Granite-4.0-Tiny-Base-Preview

Model Summary:

Granite-4.0-Tiny-Base-Preview is a 7B-parameter hybrid mixture-of-experts (MoE) language model featuring a 128k token context window. The architecture leverages Mamba-2, superimposed with a softmax attention for enhanced expressiveness, with no positional encoding for better length generalization.

Developers: Granite Team, IBM
Website: Granite Docs
Release Date: May 2nd, 2025
License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these 12 languages.

Intended Use: Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and other long-context tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.

Downloads last month: 329

GGUF

Model size

7B params

Architecture

granitehybrid

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Collection including ibm-granite/granite-4.0-tiny-base-preview-GGUF

Granite Quantized Models

Collection

Quantized versions of IBM Granite models. • 50 items • Updated 20 days ago • 36