Instructions to use research-backup/bart-large-squad-qg-default with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/bart-large-squad-qg-default with Transformers:

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

pipe = pipeline("text-generation", model="research-backup/bart-large-squad-qg-default")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("research-backup/bart-large-squad-qg-default")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/bart-large-squad-qg-default")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use research-backup/bart-large-squad-qg-default with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/research-backup/bart-large-squad-qg-default

SGLang

How to use research-backup/bart-large-squad-qg-default 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 "research-backup/bart-large-squad-qg-default" \
    --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": "research-backup/bart-large-squad-qg-default",
		"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 "research-backup/bart-large-squad-qg-default" \
        --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": "research-backup/bart-large-squad-qg-default",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/bart-large-squad-qg-default with Docker Model Runner:
```
docker model run hf.co/research-backup/bart-large-squad-qg-default
```

asahi417 commited on Jan 31, 2022

Commit

e3cd2b5

1 Parent(s): 2f782ad

Create README.md

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+---
+language:
+  - en
+tags:
+- question generation
+- question answer generation
+license: mit
+datasets:
+- squad
+metrics:
+- bleu
+- meteor
+- rouge
+widget:
+- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
+  example_title: "Example 1"
+- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records."
+  example_title: "Example 2"
+- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,  <hl> Cadillac Records  <hl> ."
+  example_title: "Example 3"
+---
+# T5 finetuned on Question Generation
+T5 model for question generation. Please visit [our repository](https://github.com/asahi417/t5-question-generation) for more detail.