Text Generation
Transformers
PyTorch
German
mt5
text2text-generation
text simplification
plain language
easy-to-read language
sentence simplification
Instructions to use DEplain/mt5-simple-german-corpus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DEplain/mt5-simple-german-corpus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DEplain/mt5-simple-german-corpus")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("DEplain/mt5-simple-german-corpus") model = AutoModelForSeq2SeqLM.from_pretrained("DEplain/mt5-simple-german-corpus") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DEplain/mt5-simple-german-corpus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DEplain/mt5-simple-german-corpus" # 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-simple-german-corpus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DEplain/mt5-simple-german-corpus
- SGLang
How to use DEplain/mt5-simple-german-corpus 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-simple-german-corpus" \ --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-simple-german-corpus", "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-simple-german-corpus" \ --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-simple-german-corpus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DEplain/mt5-simple-german-corpus with Docker Model Runner:
docker model run hf.co/DEplain/mt5-simple-german-corpus
- Xet hash:
- 5f8890b0266578ce7fffc34e5b17d56d5d5fc768932a1bd2b5c60843cee76139
- Size of remote file:
- 2.33 GB
- SHA256:
- 3c7e79bfcab7090f188489bfe6f8b4e56dc186f1ca861a89929005a9b4bf58d9
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