Instructions to use QuantFactory/YugoGPT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/YugoGPT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/YugoGPT-GGUF",
	filename="YugoGPT.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/YugoGPT-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/YugoGPT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/YugoGPT-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 QuantFactory/YugoGPT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/YugoGPT-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 QuantFactory/YugoGPT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/YugoGPT-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 QuantFactory/YugoGPT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/YugoGPT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/YugoGPT-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/YugoGPT-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/YugoGPT-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/YugoGPT-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 QuantFactory/YugoGPT-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 QuantFactory/YugoGPT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/YugoGPT-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/YugoGPT-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/YugoGPT-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/YugoGPT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/YugoGPT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.YugoGPT-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/YugoGPT-GGUF

This is quantized version of gordicaleksa/YugoGPT created using llama.cpp

Original Model Card

This repo contains YugoGPT - the best open-source base 7B LLM for BCS (Bosnian, Croatian, Serbian) languages developed by Aleksa Gordić.

You can access more powerful iterations of YugoGPT already through the recently announced RunaAI's API platform!

Serbian LLM eval results compared to Mistral 7B, LLaMA 2 7B, and GPT2-orao (also see this LinkedIn post):

Eval was computed using https://github.com/gordicaleksa/serbian-llm-eval

It was trained on tens of billions of BCS tokens and is based off of Mistral 7B.

Notes

YugoGPT is a base model and therefore does not have any moderation mechanisms.
Since it's a base model it won't follow your instructions as it's just a powerful autocomplete engine.
If you want an access to much more powerful BCS LLMs (some of which are powering yugochat) - you can access the models through RunaAI's API

Credits

The data for the project was obtained with the help of Nikola Ljubešić, CLARIN.SI, and CLASSLA. Thank you!

Project Sponsors

A big thank you to the project sponsors!

Platinum sponsors 🌟

Ivan (anon)
Things Solver

Gold sponsors 🟡

Silver sponsors ⚪

psk.rs, OmniStreak, Luka Važić, Miloš Durković, Marjan Radeski, Marjan Stankovic, Nikola Stojiljkovic, Mihailo Tomić, Bojan Jevtic, Jelena Jovanović, Nenad Davidović, Mika Tasich, TRENCH-NS, Nemanja Grujičić, tim011

Also a big thank you to the following individuals:

Slobodan Marković - for spreading the word! :)
Aleksander Segedi - for help around bookkeeping!

Citation

@article{YugoGPT,
  author    = "Gordić Aleksa",
  title     = "YugoGPT - an open-source LLM for Serbian, Bosnian, and Croatian languages",
  year      = "2024"
  howpublished = {\url{https://huggingface.co/gordicaleksa/YugoGPT}},
}

Downloads last month: 733

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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