Instructions to use rausch/en-t5-sci-continued-pretraining-487k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rausch/en-t5-sci-continued-pretraining-487k with Transformers:

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

pipe = pipeline("text-generation", model="rausch/en-t5-sci-continued-pretraining-487k")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("rausch/en-t5-sci-continued-pretraining-487k")
model = AutoModelForSeq2SeqLM.from_pretrained("rausch/en-t5-sci-continued-pretraining-487k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rausch/en-t5-sci-continued-pretraining-487k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rausch/en-t5-sci-continued-pretraining-487k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rausch/en-t5-sci-continued-pretraining-487k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rausch/en-t5-sci-continued-pretraining-487k

SGLang

How to use rausch/en-t5-sci-continued-pretraining-487k 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 "rausch/en-t5-sci-continued-pretraining-487k" \
    --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": "rausch/en-t5-sci-continued-pretraining-487k",
		"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 "rausch/en-t5-sci-continued-pretraining-487k" \
        --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": "rausch/en-t5-sci-continued-pretraining-487k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rausch/en-t5-sci-continued-pretraining-487k with Docker Model Runner:
```
docker model run hf.co/rausch/en-t5-sci-continued-pretraining-487k
```

en-t5-sci-continued-pretraining-487k / generation_config.json

root

Initial release with Git LFS

c83a6d9 12 months ago

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142 Bytes

{
  "_from_model_config": true,
  "decoder_start_token_id": 0,
  "eos_token_id": 1,
  "pad_token_id": 0,
  "transformers_version": "4.54.1"
}

	{
	"_from_model_config": true,
	"decoder_start_token_id": 0,
	"eos_token_id": 1,
	"pad_token_id": 0,
	"transformers_version": "4.54.1"
	}