Instructions to use GeorgiaTech/scibert-generative-pubmedqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GeorgiaTech/scibert-generative-pubmedqa with Transformers:

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

pipe = pipeline("text-generation", model="GeorgiaTech/scibert-generative-pubmedqa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("GeorgiaTech/scibert-generative-pubmedqa")
model = AutoModelForSeq2SeqLM.from_pretrained("GeorgiaTech/scibert-generative-pubmedqa", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GeorgiaTech/scibert-generative-pubmedqa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GeorgiaTech/scibert-generative-pubmedqa

SGLang

How to use GeorgiaTech/scibert-generative-pubmedqa 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 "GeorgiaTech/scibert-generative-pubmedqa" \
    --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": "GeorgiaTech/scibert-generative-pubmedqa",
		"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 "GeorgiaTech/scibert-generative-pubmedqa" \
        --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": "GeorgiaTech/scibert-generative-pubmedqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GeorgiaTech/scibert-generative-pubmedqa with Docker Model Runner:
```
docker model run hf.co/GeorgiaTech/scibert-generative-pubmedqa
```

scibert-generative-pubmedqa / README.md

hbeadles

Update README.md

cf9a941 verified over 2 years ago

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	---
	license: mit
	datasets:
	- qiaojin/PubMedQA
	language:
	- en
	pipeline_tag: text2text-generation
	tags:
	- medical
	---

	Author - Hayden Beadles

	This model is meant to evaluate the results of creating an Encoder / Decoder generative model using SciBERT. The model is then finetuned on 30000 samples of the PubMedQA dataset. Instead of being finetuned
	on the columns question and final_answer, where final_answer is a set of yes / no answers, we instead fine tune on the more challenging long_answer column, which gives a short answer
	to the question.

	The model was fine-tuned over 3 epochs, using the Adam learning rate scheduler, with a max length of 128 tokens.

	The results are to help gauge SciBERT's abilities to answer (generate an answer) directly to a question, with no context provided. It is meant to evaluate the overall models training and attention towards
	a more focused topic, to see if SciBERTs base training gives it any advantages.