Instructions to use RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits")
model = AutoModelForMultimodalLM.from_pretrained("RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits

SGLang

How to use RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits 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 "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits" \
    --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": "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits",
		"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 "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits" \
        --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": "RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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gpt2-small-italian-embeddings - bnb 4bits

Model creator: https://huggingface.co/GroNLP/
Original model: https://huggingface.co/GroNLP/gpt2-small-italian-embeddings/

Original model description:

language: it tags: - adaption - recycled - gpt2-small pipeline_tag: text-generation

GPT-2 recycled for Italian (small, adapted lexical embeddings)

Wietse de Vries • Malvina Nissim

Model description

This model is based on the small OpenAI GPT-2 (gpt2) 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-small-italian-embeddings")

from transformers import AutoTokenizer, AutoModel, TFAutoModel

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

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Model size

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Tensor type

F32

F16

Paper for RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-4bits

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

Paper • 2012.05628 • Published Dec 10, 2020