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")

# pip install -U transformers accelerate
# 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
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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.

Downloads last month: 23

Safetensors

Model size

0.2B params

Tensor type

F32

GeorgiaTech
/

scibert-generative-pubmedqa

Dataset used to train GeorgiaTech/scibert-generative-pubmedqa