Instructions to use obss/mt5-small-3task-highlight-combined3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use obss/mt5-small-3task-highlight-combined3 with Transformers:

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

pipe = pipeline("text-generation", model="obss/mt5-small-3task-highlight-combined3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("obss/mt5-small-3task-highlight-combined3")
model = AutoModelForSeq2SeqLM.from_pretrained("obss/mt5-small-3task-highlight-combined3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use obss/mt5-small-3task-highlight-combined3 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-highlight-combined3"
# 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-highlight-combined3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/obss/mt5-small-3task-highlight-combined3

SGLang

How to use obss/mt5-small-3task-highlight-combined3 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-highlight-combined3" \
    --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-highlight-combined3",
		"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-highlight-combined3" \
        --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-highlight-combined3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use obss/mt5-small-3task-highlight-combined3 with Docker Model Runner:
```
docker model run hf.co/obss/mt5-small-3task-highlight-combined3
```

Fatih commited on Dec 3, 2021

Commit

4c99115

1 Parent(s): 637d003

Update experiment_config.yaml

Browse files

Files changed (1) hide show

experiment_config.yaml +2 -2

experiment_config.yaml CHANGED Viewed

@@ -53,8 +53,8 @@ max_target_length: 64
 metric_for_best_model: null
 model_name_or_path: google/mt5-small
 mp_parameters: ''
-neptune_api_token: eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJmMDMwZmQzYy01ZmQxLTQzZGMtYjYyOC02ZjUzMmMwYTkxZDgifQ==
-neptune_project: obss-ml/nqg-test
 no_cuda: false
 num_train_epochs: 15
 output_dir: runs/exp10

 metric_for_best_model: null
 model_name_or_path: google/mt5-small
 mp_parameters: ''
+neptune_api_token: null
+neptune_project: null
 no_cuda: false
 num_train_epochs: 15
 output_dir: runs/exp10