Text Generation
Transformers
PyTorch
German
mt5
text2text-generation
text simplification
plain language
easy-to-read language
sentence simplification
Instructions to use DEplain/mt5-DEplain-APA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DEplain/mt5-DEplain-APA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DEplain/mt5-DEplain-APA")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DEplain/mt5-DEplain-APA") model = AutoModelForSeq2SeqLM.from_pretrained("DEplain/mt5-DEplain-APA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DEplain/mt5-DEplain-APA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DEplain/mt5-DEplain-APA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DEplain/mt5-DEplain-APA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DEplain/mt5-DEplain-APA
- SGLang
How to use DEplain/mt5-DEplain-APA 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 "DEplain/mt5-DEplain-APA" \ --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": "DEplain/mt5-DEplain-APA", "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 "DEplain/mt5-DEplain-APA" \ --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": "DEplain/mt5-DEplain-APA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DEplain/mt5-DEplain-APA with Docker Model Runner:
docker model run hf.co/DEplain/mt5-DEplain-APA
Download pytorch_model.bin from DEplain/mt5-DEplain-APA: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/DEplain/mt5-DEplain-APA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DEplain/mt5-DEplain-APA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DEplain/mt5-DEplain-APA/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 277a5534df1db34642caca8f0e904e82aea9d8ac4af3a8541ccacf05be4594d9
- Size of remote file:
- 2.33 GB
- SHA256:
- c182c4e6f0169c90bfd65a7195c1081e2b9129c6e2c67ec696821e91e4077061
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