Instructions to use bryanmildort/gpt-clinical-notes-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bryanmildort/gpt-clinical-notes-summarizer with Transformers:

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

pipe = pipeline("text-generation", model="bryanmildort/gpt-clinical-notes-summarizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bryanmildort/gpt-clinical-notes-summarizer")
model = AutoModelForCausalLM.from_pretrained("bryanmildort/gpt-clinical-notes-summarizer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bryanmildort/gpt-clinical-notes-summarizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bryanmildort/gpt-clinical-notes-summarizer

SGLang

How to use bryanmildort/gpt-clinical-notes-summarizer 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 "bryanmildort/gpt-clinical-notes-summarizer" \
    --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": "bryanmildort/gpt-clinical-notes-summarizer",
		"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 "bryanmildort/gpt-clinical-notes-summarizer" \
        --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": "bryanmildort/gpt-clinical-notes-summarizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bryanmildort/gpt-clinical-notes-summarizer with Docker Model Runner:
```
docker model run hf.co/bryanmildort/gpt-clinical-notes-summarizer
```

How to summarize clinical notes using this model

by krish14388 - opened May 12, 2023

Discussion

krish14388

May 12, 2023

I could only find GPTJforCausalLm in huggingface which can be used to generate text but couldn't figure out how to use this model for text summarization. Could you point me to the code snippet which we can use for the summarization task.

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