Instructions to use BramVanroy/GEITje-7B-ultra-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BramVanroy/GEITje-7B-ultra-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 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 serve -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 Desktop
- 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
- Atomic Chat
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.
Ollama
The model is available on ollama.
- Downloads last month
- 562
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
Model tree for BramVanroy/GEITje-7B-ultra-GGUF
Base model
mistralai/Mistral-7B-v0.1