Instructions to use intelia-lab-uah/mT5-base_AE_SS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use intelia-lab-uah/mT5-base_AE_SS with Transformers:

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

pipe = pipeline("text-generation", model="intelia-lab-uah/mT5-base_AE_SS")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("intelia-lab-uah/mT5-base_AE_SS")
model = AutoModelForSeq2SeqLM.from_pretrained("intelia-lab-uah/mT5-base_AE_SS", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use intelia-lab-uah/mT5-base_AE_SS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "intelia-lab-uah/mT5-base_AE_SS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "intelia-lab-uah/mT5-base_AE_SS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/intelia-lab-uah/mT5-base_AE_SS

SGLang

How to use intelia-lab-uah/mT5-base_AE_SS 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 "intelia-lab-uah/mT5-base_AE_SS" \
    --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": "intelia-lab-uah/mT5-base_AE_SS",
		"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 "intelia-lab-uah/mT5-base_AE_SS" \
        --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": "intelia-lab-uah/mT5-base_AE_SS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use intelia-lab-uah/mT5-base_AE_SS with Docker Model Runner:
```
docker model run hf.co/intelia-lab-uah/mT5-base_AE_SS
```

mT5-base_AE_SS / tokenizer_config.json

intelia-lab

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	{
	"additional_special_tokens": null,
	"eos_token": "</s>",
	"extra_ids": 0,
	"name_or_path": "google/mt5-base",
	"pad_token": "<pad>",
	"sp_model_kwargs": {},
	"special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276",
	"tokenizer_class": "T5Tokenizer",
	"unk_token": "<unk>"
	}