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

Libraries

How to use DeepESP/gpt2-spanish-medium with Transformers:

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

pipe = pipeline("text-generation", model="DeepESP/gpt2-spanish-medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
model = AutoModelForCausalLM.from_pretrained("DeepESP/gpt2-spanish-medium", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeepESP/gpt2-spanish-medium with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DeepESP/gpt2-spanish-medium

SGLang

How to use DeepESP/gpt2-spanish-medium 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 "DeepESP/gpt2-spanish-medium" \
    --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": "DeepESP/gpt2-spanish-medium",
		"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 "DeepESP/gpt2-spanish-medium" \
        --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": "DeepESP/gpt2-spanish-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepESP/gpt2-spanish-medium with Docker Model Runner:
```
docker model run hf.co/DeepESP/gpt2-spanish-medium
```

DeepESP commited on May 17, 2021

Commit

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+# GPT2-Spanish
+GPT2-Spanish is a language generation model trained from scratch with 9GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for this purpose. The parameters used are the same as the medium version of the original OpenAI GPT2 model.
+## Corpus
+This model was trained with a corpus of 9GB of texts corresponding to 3 GB of Wikipedia articles and 6GB of books (narrative, short stories, theater, poetry, essays, and popularization).
+## Tokenizer
+The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for Unicode characters) and a vocabulary size of 50257. The inputs are sequences of 1024 consecutive tokens.
+This tokenizer was trained from scratch with the Spanish corpus, since it was evidenced that the tokenizer of the English models presented limitations to capture the semantic relations of Spanish, due to the morphosyntactic differences between both languages.
+Apart from the special token "<|endoftext|>" for text ending in the OpenAI GPT-2 models, the tokens "<|talk|>", "<|ax1|>", "<|ax2|>" (..)"<|ax9|>" were included so that they can serve as prompts in future training.
+## Training
+The model and tokenizer were trained using the Hugging Face libraries with an Nvidia Tesla V100 GPU with 16GB memory on Google Colab servers.
+## Authors
+The model was trained by Jorge Ortiz Fuentes (Chile) and Alejandro Oñate Latorre (Spain), members of DeepESP, an open-source community on Natural Language Processing in Spanish (https://t.me/joinchat/VoEp1bPrDYEexc6h).
+Thanks to the members of the community who collaborated with funding for the initial tests.
+## Cautions
+The model generates texts according to the patterns learned in the training corpus. These data were not filtered, therefore, the model could generate offensive or discriminatory content.