Instructions to use BramVanroy/GEITje-7B-ultra-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BramVanroy/GEITje-7B-ultra-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BramVanroy/GEITje-7B-ultra-GGUF",
	filename="geitje-7b-ultra-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BramVanroy/GEITje-7B-ultra-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BramVanroy/GEITje-7B-ultra-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 BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BramVanroy/GEITje-7B-ultra-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 BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M

Use Docker

docker model run hf.co/BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use BramVanroy/GEITje-7B-ultra-GGUF with Ollama:
```
ollama run hf.co/BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
```

Unsloth Studio

How to use BramVanroy/GEITje-7B-ultra-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 BramVanroy/GEITje-7B-ultra-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 BramVanroy/GEITje-7B-ultra-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BramVanroy/GEITje-7B-ultra-GGUF to start chatting

Docker Model Runner
How to use BramVanroy/GEITje-7B-ultra-GGUF with Docker Model Runner:
```
docker model run hf.co/BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M
```

Lemonade

How to use BramVanroy/GEITje-7B-ultra-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BramVanroy/GEITje-7B-ultra-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GEITje-7B-ultra-GGUF-Q4_K_M

List all available models

lemonade list

GEITje 7B ultra (GGUF version)

A conversational model for Dutch, aligned through AI feedback.

This is a GGUF version of BramVanroy/GEITje-7B-ultra, a powerful Dutch chatbot, which ultimately is Mistral-based model, further pretrained on Dutch and additionally treated with supervised-finetuning and DPO alignment. For more information on the model, data, licensing, usage, see the main model's README.

Citation

If you use GEITje 7B Ultra (SFT) or any of its derivatives or quantizations, place cite the following paper:

@misc{vanroy2024geitje7bultraconversational,
      title={GEITje 7B Ultra: A Conversational Model for Dutch}, 
      author={Bram Vanroy},
      year={2024},
      eprint={2412.04092},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.04092}, 
}

Available quantization types and expected performance differences compared to base f16, higher perplexity=worse (from llama.cpp):

Q3_K_M  :  3.07G, +0.2496 ppl @ LLaMA-v1-7B
Q4_K_M  :  3.80G, +0.0532 ppl @ LLaMA-v1-7B
Q5_K_M  :  4.45G, +0.0122 ppl @ LLaMA-v1-7B
Q6_K    :  5.15G, +0.0008 ppl @ LLaMA-v1-7B
Q8_0    :  6.70G, +0.0004 ppl @ LLaMA-v1-7B
F16     : 13.00G              @ 7B

Also available on ollama.

Quants were made with release b2777 of llama.cpp.

Usage

LM Studio

You can use this model in LM Studio, an easy-to-use interface to locally run optimized models. Simply search for BramVanroy/GEITje-7B-ultra-GGUF, and download the available file.