Instructions to use obss/mt5-small-3task-both-tquad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use obss/mt5-small-3task-both-tquad2 with Transformers:

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

pipe = pipeline("text-generation", model="obss/mt5-small-3task-both-tquad2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("obss/mt5-small-3task-both-tquad2")
model = AutoModelForSeq2SeqLM.from_pretrained("obss/mt5-small-3task-both-tquad2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use obss/mt5-small-3task-both-tquad2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "obss/mt5-small-3task-both-tquad2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "obss/mt5-small-3task-both-tquad2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/obss/mt5-small-3task-both-tquad2

SGLang

How to use obss/mt5-small-3task-both-tquad2 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 "obss/mt5-small-3task-both-tquad2" \
    --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": "obss/mt5-small-3task-both-tquad2",
		"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 "obss/mt5-small-3task-both-tquad2" \
        --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": "obss/mt5-small-3task-both-tquad2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use obss/mt5-small-3task-both-tquad2 with Docker Model Runner:
```
docker model run hf.co/obss/mt5-small-3task-both-tquad2
```

mt5-small-3task-both-tquad2 / pytorch_model.bin

fcakyon

initial commit

d3e3c87 over 4 years ago

Download

History Blame Contribute Delete

1.2 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9d6bc6328227c1450d3d048bbf3613913ff219b105128512018c1825d9ecc922
Size of remote file:: 1.2 GB
SHA256:: e1dc3930dc8c4e4375682bccc6229f2dfc8d27b4942b14e5b45b2e19a05427a4

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.