Instructions to use elte-nlp/Racka-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elte-nlp/Racka-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="elte-nlp/Racka-4B-GGUF",
	filename="Racka-4B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf elte-nlp/Racka-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/elte-nlp/Racka-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use elte-nlp/Racka-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "elte-nlp/Racka-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "elte-nlp/Racka-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/elte-nlp/Racka-4B-GGUF:Q4_K_M

Ollama
How to use elte-nlp/Racka-4B-GGUF with Ollama:
```
ollama run hf.co/elte-nlp/Racka-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-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 elte-nlp/Racka-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for elte-nlp/Racka-4B-GGUF to start chatting

How to use elte-nlp/Racka-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elte-nlp/Racka-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "elte-nlp/Racka-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use elte-nlp/Racka-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elte-nlp/Racka-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default elte-nlp/Racka-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use elte-nlp/Racka-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elte-nlp/Racka-4B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "elte-nlp/Racka-4B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use elte-nlp/Racka-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/elte-nlp/Racka-4B-GGUF:Q4_K_M
```

Lemonade

How to use elte-nlp/Racka-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull elte-nlp/Racka-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Racka-4B-GGUF-Q4_K_M

List all available models

lemonade list

Access Request for Research-Only Model

Please provide your professional details and acknowledge the terms of use to request access.

By requesting access, you acknowledge that this model is provided solely for research purposes, is offered 'as-is' without any guarantees, and cannot be utilized for for-profit tasks or commercial applications.

Racka-4B-GGUF Model Card

Racka

Racka (Regionális Adatokon Célzottan Kialakított Alapmodell) is a continually pretrained large language model designed to bridge the resource gap between Hungarian and high-resource languages. It employs parameter-efficient continual pretraining via Low-Rank Adaptation (LoRA) on a Qwen3-4B (reasoning/instruct) backbone.

The model was trained on a mixture of 160B tokens (44% Hungarian, 24% German, 21% English, 11% Code) on the Komondor HPC. To better match the training distribution, Racka uses an adapted tokenizer that achieves substantially improved tokenization fertility for Hungarian while maintaining competitive performance in English and German.

Note: This is a quantized model. The full-precision model is available at elte-nlp/Racka-4B.

To learn more about the details of our project, please check our FAQ.

Model Details

Model: elte-nlp/Racka-4B
Quantization: IQ3_S, Q4_K_M, Q5_K_M, Q8_0
Intended use case: Low-complexity edge tasks, optimized few-shot pipelines, task-specific Hungarian fine-tuning.

Benchmarks

Limitations

The model is capable of both instruction following chat and English reasoning using the original Qwen settings, this is a preserved capability with no direct training targetting this functionality.
The model has not been aligned and is unsafe for use with end-users.
This model is only to be used for research purposes, commercial or for-profit usage is not permitted.

Team

In alphabetical order:

Zsolt Csibi (ELTE-IK, AI Dept.)
Bence Gortka (ELTE-BTK, DH-Lab)
Natabara Gyöngyössy (ELTE-IK, AI Dept.)
Kornél Nagy (ELTE-BTK, DH-Lab)
Dávid Nemeskey (ELTE-BTK, DH-Lab)
Gábor Palkó (ELTE-BTK, DH-Lab)
Martin Sallai (ELTE-BTK, DH-Lab)
András Simonyi (ELTE-IK, AI Dept.)
András Szekeres (ELTE-BTK, DH-Lab)

Acknowledgements

We acknowledge the Digital Government Development and Project Management Ltd. for awarding us access to the Komondor HPC facility based in Hungary.

This research was supported by the EKÖP-24 University Excellence Scholarship Program of the Ministry for Culture and Innovation, funded by the National Research, Development and Innovation Fund.

The authors acknowledge the support of the National Laboratory for Digital Heritage. Project no. 2022-2.1.1-NL-2022-00009 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the 2022-2.1.1-NL funding scheme.

We would like to thank Levente Szabados for the name idea and initial informal discussions.

Citation

@article{racka2026,
  title={Racka: Efficient Hungarian LLM Adaptation on Academic Infrastructure},
  author={Csibi, Zsolt and Gortka, Bence Gy\"orgy and Nagy, Korn\'el and Nemeskey, D\'avid M\'ark and Sallai, Martin and Simonyi, Andr\'as and Szekeres, Andr\'as M\'ark and Palk\'o, G\'abor},
  journal={Proceedings of the XXII. Hungarian Computational Linguistics Conference},
  year={2026}
}

FAQ

Q: Is this model intended for use as a general-purpose chat agent?

A: No. While Racka-4B currently stands as one of the highest-scoring small/edge models on Hungarian benchmarks, it remains a compact, edge-oriented model. Conducting a full language adaptation for larger architectures would demand computing infrastructure beyond the Komondor HPC (AI training is just really expensive on the large scale).

Our team is actively working on adapting larger models; in the meantime, we recommend utilizing models of at least 27B–30B parameters for complex or resource-heavy reasoning tasks.

Q: Why is this a gated-access model with a restricted license?

A: Due to Hungarian and EU regulations, as well as the licensing terms of our source data, certain components of our training corpus cannot be used to generate derivative works for commercial or for-profit activities.

The volume of globally available, high-quality Hungarian text data remains limited. Restricting our training data exclusively to open, unrestricted corpora would not have yielded a sufficient dataset for effective language adaptation. Consequently, we require users to fill in an access agreement confirming the model will be used strictly for non-profit purposes. We believe the academic and non-profit communities will still derive value from this work.

Q: What kind of financial funding did this project receive?

A: As noted in our acknowledgements, our team consists of AI researchers and enthusiasts who volunteered their time. We did not receive dedicated financial funding or grants to build this model. Instead, the project was made possible through institutional and operational support:

Worktime Allocations & Academic Grants: Several ELTE researchers and PhD students were permitted to dedicate official university hours and existing fellowship time to this initiative.
Compute & HPC Access: Our academic project proposal was accepted by the Komondor HPC team (this is a public opportunity available to all Hungarian academics), granting us vital GPU time. The facility's engineering team also provided essential, specialized HPC support.
Voluntary Contribution: Driven by a shared passion for open-source NLP, project members frequently dedicated their personal time to bring this model to completion.

Q: How would you improve on your model?

A: The current model has undergone continual pre-training during language adaptation, but it has not been through any SFT or alignment steps, which are required for highly capable models. Our aim is to translate, create and synthetize higher complexity datasets as part of our next projects. This will allow us to train models that are more capable in everyday use cases.

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GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

8-bit

16-bit

Model tree for elte-nlp/Racka-4B-GGUF

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

elte-nlp/Racka-4B

Quantized

(2)

this model