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
```

rausch commited on Nov 17, 2025

Commit

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+---
+language:
+- de
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- t5
+- german
+- wechsel
+- cross-lingual
+datasets:
+- unpaywall-scientific
+---
+# 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.