Instructions to use rausch/de-t5-sci-transfer-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rausch/de-t5-sci-transfer-init with Transformers:

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

pipe = pipeline("text-generation", model="rausch/de-t5-sci-transfer-init")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("rausch/de-t5-sci-transfer-init")
model = AutoModelForSeq2SeqLM.from_pretrained("rausch/de-t5-sci-transfer-init")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rausch/de-t5-sci-transfer-init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rausch/de-t5-sci-transfer-init"
# 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-sci-transfer-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rausch/de-t5-sci-transfer-init

SGLang

How to use rausch/de-t5-sci-transfer-init 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-sci-transfer-init" \
    --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-sci-transfer-init",
		"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-sci-transfer-init" \
        --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-sci-transfer-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rausch/de-t5-sci-transfer-init with Docker Model Runner:
```
docker model run hf.co/rausch/de-t5-sci-transfer-init
```

DE-T5-Sci-Transfer-Init

WECHSEL-initialized checkpoint: English EN-T5-Sci weights + German tokenizer (GermanT5/t5-efficient-gc4-german-base-nl36) aligned using WECHSEL (static embeddings + bilingual dictionary). No additional German training after transfer. Folder includes transfer_metadata.pt with alignment diagnostics.

Model Details

Embedding init: Orthogonal Procrustes map (fastText n-gram embeddings) + temperature-weighted mixtures (k-nearest neighbors)
Special tokens: <extra_id_0..99> aligned, sentinel behavior preserved
Tokenizer: GermanT5 SentencePiece (files bundled here)

Evaluation (Global-MMLU, zero-shot)

Metric	EN	DE
Overall accuracy	0.2434	0.2463
Humanities	0.2485	0.2559
STEM	0.2391	0.2445
Social Sciences	0.2317	0.2307
Other	0.2517	0.2491

This demonstrates immediate cross-lingual transfer without any German gradient steps.

Intended Use

Starting point for German continued pretraining or fine-tuning where English scientific knowledge should be retained but a German tokenizer is required.

Limitations

No German data exposure beyond embedding alignment; you should run additional continued pretraining (see next model) for best performance.
Still limited to 512-token context.

Downloads last month: 5

Safetensors

Model size

0.2B params

Tensor type

F32

Collection including rausch/de-t5-sci-transfer-init

t5-sci-en2de-continued-pretraining

Collection

6 items • Updated Nov 18, 2025 • 2