Instructions to use microsoft/BioGPT-Large-PubMedQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/BioGPT-Large-PubMedQA with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/BioGPT-Large-PubMedQA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large-PubMedQA")
model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large-PubMedQA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/BioGPT-Large-PubMedQA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/BioGPT-Large-PubMedQA

SGLang

How to use microsoft/BioGPT-Large-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 "microsoft/BioGPT-Large-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": "microsoft/BioGPT-Large-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 "microsoft/BioGPT-Large-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": "microsoft/BioGPT-Large-PubMedQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/BioGPT-Large-PubMedQA with Docker Model Runner:
```
docker model run hf.co/microsoft/BioGPT-Large-PubMedQA
```

kamalkraj commited on Feb 4, 2023

Commit

39c04fb

1 Parent(s): 15596c8

Update README.md

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@@ -14,7 +14,7 @@ widget:
 - text: "question: Can 'high-risk' human papillomaviruses (HPVs) be detected in human breast milk? context: Using polymerase chain reaction techniques, we evaluated the presence of HPV infection in human breast milk collected from 21 HPV-positive and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no 'high-risk' HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer: This preliminary case-control study indicates the absence of mucosal 'high-risk' HPV types in human breast milk."
 inference:
   parameters:
-    max_new_tokens: 1024
     do_sample: False
 ---

 - text: "question: Can 'high-risk' human papillomaviruses (HPVs) be detected in human breast milk? context: Using polymerase chain reaction techniques, we evaluated the presence of HPV infection in human breast milk collected from 21 HPV-positive and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no 'high-risk' HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer: This preliminary case-control study indicates the absence of mucosal 'high-risk' HPV types in human breast milk."
 inference:
   parameters:
+    max_new_tokens: 250
     do_sample: False
 ---