Instructions to use GroNLP/gpt2-medium-dutch-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GroNLP/gpt2-medium-dutch-embeddings with Transformers:

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

pipe = pipeline("text-generation", model="GroNLP/gpt2-medium-dutch-embeddings")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")
model = AutoModelForMultimodalLM.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GroNLP/gpt2-medium-dutch-embeddings with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GroNLP/gpt2-medium-dutch-embeddings"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GroNLP/gpt2-medium-dutch-embeddings",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GroNLP/gpt2-medium-dutch-embeddings

SGLang

How to use GroNLP/gpt2-medium-dutch-embeddings 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 "GroNLP/gpt2-medium-dutch-embeddings" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GroNLP/gpt2-medium-dutch-embeddings",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "GroNLP/gpt2-medium-dutch-embeddings" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GroNLP/gpt2-medium-dutch-embeddings",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GroNLP/gpt2-medium-dutch-embeddings with Docker Model Runner:
```
docker model run hf.co/GroNLP/gpt2-medium-dutch-embeddings
```

GPT-2 recycled for Dutch (medium, adapted lexical embeddings)

Wietse de Vries • Malvina Nissim

Model description

This model is based on the medium OpenAI GPT-2 (gpt2-medium) model.

The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for a Dutch vocabulary.

For details, check out our paper on arXiv and the code on Github.

Related models

Dutch

gpt2-small-dutch-embeddings: Small model size with only retrained lexical embeddings.
gpt2-small-dutch: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)
gpt2-medium-dutch-embeddings: Medium model size with only retrained lexical embeddings.

Italian

gpt2-small-italian-embeddings: Small model size with only retrained lexical embeddings.
gpt2-small-italian: Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended)
gpt2-medium-italian-embeddings: Medium model size with only retrained lexical embeddings.

How to use

from transformers import pipeline

pipe = pipeline("text-generation", model="GroNLP/gpt2-medium-dutch-embeddings")

from transformers import AutoTokenizer, AutoModel, TFAutoModel

tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")
model = AutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")  # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-medium-dutch-embeddings")  # Tensorflow

BibTeX entry

@misc{devries2020good,
      title={As good as new. How to successfully recycle English GPT-2 to make models for other languages}, 
      author={Wietse de Vries and Malvina Nissim},
      year={2020},
      eprint={2012.05628},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Safetensors

Model size

0.4B params

Tensor type

F32

Paper for GroNLP/gpt2-medium-dutch-embeddings

As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages

Paper • 2012.05628 • Published Dec 10, 2020