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

Libraries

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

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

fcakyon commited on Dec 3, 2021

Commit

08aaeb0

1 Parent(s): a80d4fd

update trained data on readme

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -25,7 +25,7 @@ generation_api = GenerationAPI(model_url_or_path='mt5-base-3task-highlight-combi
 **Language model:** mt5-base
 **Language:** Turkish
 **Downstream-task:** Extractive QA/QG, Answer Extraction
-**Training data:** TQuADv2-train
 **Code:**  https://github.com/obss/turkish-question-generation
 **Paper:**  https://arxiv.org/abs/2111.06476

 **Language model:** mt5-base
 **Language:** Turkish
 **Downstream-task:** Extractive QA/QG, Answer Extraction
+**Training data:** TQuADv2-train, TQuADv2-val, XQuAD.tr
 **Code:**  https://github.com/obss/turkish-question-generation
 **Paper:**  https://arxiv.org/abs/2111.06476