Instructions to use voidful/bart-eqg-question-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use voidful/bart-eqg-question-generator with Transformers:

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

pipe = pipeline("text-generation", model="voidful/bart-eqg-question-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("voidful/bart-eqg-question-generator")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/bart-eqg-question-generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use voidful/bart-eqg-question-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/voidful/bart-eqg-question-generator

SGLang

How to use voidful/bart-eqg-question-generator 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 "voidful/bart-eqg-question-generator" \
    --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": "voidful/bart-eqg-question-generator",
		"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 "voidful/bart-eqg-question-generator" \
        --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": "voidful/bart-eqg-question-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use voidful/bart-eqg-question-generator with Docker Model Runner:
```
docker model run hf.co/voidful/bart-eqg-question-generator
```

voidful commited on Aug 24, 2021

Commit

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+---
+language: en
+tags:
+- bart
+- question
+- generation
+- seq2seq
+datasets:
+- eqg-race
+metrics:
+- bleu
+- rouge
+pipeline_tag: text2text-generation
+widget:
+- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another special choice : treehouses . In France , treehouses are offered to travelers as a new choice in many places . The price may be a little higher , but you do have a chance to _ your childhood memories . Alain Laurens , one of France ' s top treehouse designers , said , ' Most of the people might have the experience of building a den when they were young . And they like that feeling of freedom when they are children . ' Its fairy - tale style gives travelers a special feeling . It seems as if they are living as a forest king and enjoying the fresh air in the morning . Another kind of treehouse is the ' star cube ' . It gives travelers the chance of looking at the stars shining in the sky when they are going to sleep . Each ' star cube ' not only offers all the comfortable things that a hotel provides for travelers , but also gives them a chance to look for stars by using a telescope . The glass roof allows you to look at the stars from your bed . "
+---
+# voidful/bart-eqg-question-generator
+## Model description
+This model is a sequence-to-sequence question generator with only the context as an input, and generates a  question as an output.
+It is based on a pretrained `bart-base` model, and trained on [EQG-RACE](https://github.com/jemmryx/EQG-RACE) corpus.
+## Intended uses & limitations
+The model is trained to generate examinations-style multiple choice question.
+#### How to use
+The model takes context as an input sequence, and will generate a question as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:
+```
+context
+```
+The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.