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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

mt5-small-3task-prepend-tquad2

Commit History

Jul 18, 2023

10:47
Adding `safetensors` variant of this model⁠

SFconvertbot

fa4bd60

Dec 14, 2022

08:34
update citation

fcakyon

d2ff2a0

Dec 3, 2021

23:55
initial commit

fcakyon

82ba154
23:45
initial commit

system HF Staff

38ab4ea

Commit History

Jul 18, 2023

Adding `safetensors` variant of this model⁠

Dec 14, 2022

update citation

Dec 3, 2021

initial commit

initial commit