Instructions to use TheRensselaerIDEA/gpt2-large-vaccine-tweet-response with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheRensselaerIDEA/gpt2-large-vaccine-tweet-response with Transformers:

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

pipe = pipeline("text-generation", model="TheRensselaerIDEA/gpt2-large-vaccine-tweet-response")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheRensselaerIDEA/gpt2-large-vaccine-tweet-response")
model = AutoModelForCausalLM.from_pretrained("TheRensselaerIDEA/gpt2-large-vaccine-tweet-response", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheRensselaerIDEA/gpt2-large-vaccine-tweet-response with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheRensselaerIDEA/gpt2-large-vaccine-tweet-response

SGLang

How to use TheRensselaerIDEA/gpt2-large-vaccine-tweet-response 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 "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response" \
    --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": "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response",
		"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 "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response" \
        --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": "TheRensselaerIDEA/gpt2-large-vaccine-tweet-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheRensselaerIDEA/gpt2-large-vaccine-tweet-response with Docker Model Runner:
```
docker model run hf.co/TheRensselaerIDEA/gpt2-large-vaccine-tweet-response
```

Base model: gpt2-large

Fine-tuned to generate responses on a dataset of Vaccine public health tweets. For more information about the dataset, task and training, see our paper. This checkpoint corresponds to the lowest validation perplexity (2.82 at 2 epochs) seen during training. See Training metrics for Tensorboard logs.

For input format and usage examples, see our COVID-19 public health tweet response model.

Downloads last month: 10

Paper for TheRensselaerIDEA/gpt2-large-vaccine-tweet-response

Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter

Paper • 2204.04353 • Published Apr 9, 2022