Instructions to use rausch/de-t5-base-continued-15k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rausch/de-t5-base-continued-15k with Transformers:

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

pipe = pipeline("text-generation", model="rausch/de-t5-base-continued-15k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("rausch/de-t5-base-continued-15k")
model = AutoModelForSeq2SeqLM.from_pretrained("rausch/de-t5-base-continued-15k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rausch/de-t5-base-continued-15k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rausch/de-t5-base-continued-15k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rausch/de-t5-base-continued-15k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rausch/de-t5-base-continued-15k

SGLang

How to use rausch/de-t5-base-continued-15k 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/de-t5-base-continued-15k" \
    --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/de-t5-base-continued-15k",
		"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/de-t5-base-continued-15k" \
        --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/de-t5-base-continued-15k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rausch/de-t5-base-continued-15k with Docker Model Runner:
```
docker model run hf.co/rausch/de-t5-base-continued-15k
```

DE-T5-Base-15k

GermanT5/t5-efficient-gc4-german-base-nl36 continued for 15 000 steps on the German portion of the scientific corpus (same preprocessing as EN). Checkpoint: cross_lingual_transfer/logs/native_baseline/.../step-step=015000.ckpt.

Model Details

Base: GermanT5 (same architecture as T5-base, German tokenizer)
Optimizer: Adafactor, lr=1e-3, inverse sqrt schedule, warmup=1.5k, grad clip=1.0
Effective batch: 48 (per-GPU 48, no accumulation)
Objective: Span corruption (15 % masking, mean span length 3)

Training Data

German split of the Unpaywall-derived corpus (continued-pretraining windows of 512 tokens, 50 % overlap).

Evaluation (Global-MMLU, zero-shot)

Metric	EN	DE
Overall accuracy	0.2295	0.2295
Humanities	0.2421	0.2421
STEM	0.2125	0.2125
Social Sciences	0.2171	0.2171
Other	0.2398	0.2398

Intended Use

German scientific NLP baseline; compare against WECHSEL-based models or continue fine-tuning on German datasets.

Limitations

Only 15k steps, so improvements over base GermanT5 are modest.
Uses German SentencePiece vocab; incompatible with English tokenizer out of the box.

Downloads last month: 1

Safetensors

Model size

0.6B params

Tensor type

F32

Collection including rausch/de-t5-base-continued-15k

t5-sci-en2de-continued-pretraining

Collection

6 items • Updated Nov 18, 2025 • 2