Instructions to use BramVanroy/fietje-2-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BramVanroy/fietje-2-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BramVanroy/fietje-2-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BramVanroy/fietje-2-chat")
model = AutoModelForCausalLM.from_pretrained("BramVanroy/fietje-2-chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BramVanroy/fietje-2-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BramVanroy/fietje-2-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BramVanroy/fietje-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BramVanroy/fietje-2-chat

SGLang

How to use BramVanroy/fietje-2-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BramVanroy/fietje-2-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BramVanroy/fietje-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BramVanroy/fietje-2-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BramVanroy/fietje-2-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BramVanroy/fietje-2-chat with Docker Model Runner:
```
docker model run hf.co/BramVanroy/fietje-2-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fietje 2 Chat

An open and efficient LLM for Dutch

👱‍♀️ Base version - 🤖 Instruct version - 💬 Chat version (this one) - 🚀 GGUF of Chat

This is the chat version of Fietje, a DPO-tuned (aligned) continuation on the instruct version. Fietje is an adapated version of microsoft/phi-2, tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like GEITje 7B Ultra.

A thorough description of the creation and evaluation of Fietje as well as usage examples are available in this Github repository.

Citation

If you use Fietje or the CulturaX + Wikipedia filtered subset in your work, please cite to the following paper:

@article{vanroy2024fietje,
    author="Vanroy, Bram",
    title="Fietje: An open, efficient LLM for Dutch",
    url="https://www.clinjournal.org/clinj/article/view/213",
    journal="Computational Linguistics in the Netherlands Journal",
    volume="14",
    year="2025",
    pages="473--504"
}

Intended uses & limitations

The same limitations as phi-2, and LLMs in general, apply here. LLMs hallucinate, make mistakes, and should not be trusted. Use at your own risk!

Training and evaluation data

Fietje 2 Chat was finetuned from the instruct model on the following datasets. Number of training samples per dataset given in brackets, totalling 18,653 samples.

BramVanroy/ultra_feedback_dutch_cleaned subset dpo_hq: a cleaned version of BramVanroy/ultra_feedback_dutch (9186)
BramVanroy/orca_dpo_pairs_dutch_cleaned subset dpo_all: a cleaned version of BramVanroy/orca_dpo_pairs_dutch (9467)

A lot of different learning rates, beta, en batch sizes were investigated in search of a converging combination. You can find them all in the W&B runs.

Training procedure

I am thankful to the Flemish Supercomputer Center (VSC) for providing the computational power to accomplish this project. Accounting for waiting for jobs, training a single run took around nine hours on one A100 80GB.

Training was done with the wonderful alignment-handbook, using DeepSpeed as a back-end. Exact training recipes and SLURM script are given in the Github repository.

Training hyperparameters

The following hyperparameters were used during training:

beta: 0.2
learning_rate: 2e-06
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.2515	1.0	1166	0.2842	-1.1549	-3.6363	0.8867	2.4815	-657.6813	-451.3364	-1.2868	-1.3528

Framework versions

Transformers 4.39.1
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2

License

21/10/2026: the no_robots_dutch dataset was derived from a non-commercially available dataset. Its license was initally incorrectly specified as Apache 2.0, which allowed Fietje to instruct/chat to be released under MIT. However, this has now been rectified: Fietje Instruct and Fietje Chat may not be used for commercial purposes.