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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-medium-italian-embeddings")
model = AutoModelForCausalLM.from_pretrained("GroNLP/gpt2-medium-italian-embeddings", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GroNLP/gpt2-medium-italian-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-italian-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-italian-embeddings",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use GroNLP/gpt2-medium-italian-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-italian-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-italian-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-italian-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-italian-embeddings",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

GPT-2 recycled for Italian (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 an Italian 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-italian-embeddings")

from transformers import AutoTokenizer, AutoModel, TFAutoModel

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

Downloads last month: 132

Safetensors

Model size

0.4B params

Tensor type

F32

Paper for GroNLP/gpt2-medium-italian-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