Instructions to use potsawee/t5-large-generation-race-QuestionAnswer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use potsawee/t5-large-generation-race-QuestionAnswer with Transformers:

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

pipe = pipeline("text-generation", model="potsawee/t5-large-generation-race-QuestionAnswer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer")
model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-QuestionAnswer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use potsawee/t5-large-generation-race-QuestionAnswer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "potsawee/t5-large-generation-race-QuestionAnswer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "potsawee/t5-large-generation-race-QuestionAnswer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/potsawee/t5-large-generation-race-QuestionAnswer

SGLang

How to use potsawee/t5-large-generation-race-QuestionAnswer 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 "potsawee/t5-large-generation-race-QuestionAnswer" \
    --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": "potsawee/t5-large-generation-race-QuestionAnswer",
		"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 "potsawee/t5-large-generation-race-QuestionAnswer" \
        --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": "potsawee/t5-large-generation-race-QuestionAnswer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use potsawee/t5-large-generation-race-QuestionAnswer with Docker Model Runner:
```
docker model run hf.co/potsawee/t5-large-generation-race-QuestionAnswer
```

potsawee commited on Mar 12, 2023

Commit

8e0d096

1 Parent(s): 828f94f

Update README.md

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@@ -11,6 +11,8 @@ pipeline_tag: text2text-generation
 - Input: `context` (e.g. news article)
 - Output: `question <sep> answer`
 ## Model Details
 t5-large model is fine-tuned to the RACE dataset where the input is the context/passage and the output is the question followed by the answer. This is the first component in the question generation pipeline (i.e. `g1`) in our [MQAG paper](https://arxiv.org/abs/2301.12307),
@@ -18,7 +20,7 @@ or please refer to the GitHub repo of this project: https://github.com/potsawee/
 ## How to Use the Model
-Use the code below to get started with the model.
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

 - Input: `context` (e.g. news article)
 - Output: `question <sep> answer`
+This model generates **abstractive** answers following the RACE dataset. If you would like to have **extractive** questions/answers, you can use our model trained on SQuAD: https://huggingface.co/potsawee/t5-large-generation-squad-QuestionAnswer.
 ## Model Details
 t5-large model is fine-tuned to the RACE dataset where the input is the context/passage and the output is the question followed by the answer. This is the first component in the question generation pipeline (i.e. `g1`) in our [MQAG paper](https://arxiv.org/abs/2301.12307),
 ## How to Use the Model
+Use the code below to get started with the model. You can also set do_sample=True in generate() to obtain different question-answer pairs.
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM