SauerkrautLM-GLiNEREL-72m

Joint named-entity recognition, PII detection and relation extraction in one encoder pass. It turns a 33-page PDF into a knowledge graph in 0.34 s on a GPU, where a 4B generative model needs 17.6 s for a single paragraph. Labels are free text, chosen at inference time. English, German, French, Italian, Spanish. Try it in the demo →

Try it: SauerkrautLM-GLiNEREL Interactive Knowledge Graph: upload a PDF, explore the graph and export it as JSON, CSV, GraphML or Cypher (Neo4j).

Quickstart: glinerel-fast

glinerel-fast is our new inference library for SauerkrautLM-GLiNEREL, written for CPUs: about 1,600 lines of plain PyTorch, no transformers; also runs on NVIDIA and Apple GPUs.

pip install -U glinerel-fast     # private repo: hf auth login

from glinerel_fast import GLiNERel

model = GLiNERel.from_pretrained("VAGOsolutions/SauerkrautLM-GLiNEREL-72m")   # device="cuda", precision="bf16" on GPUs
result = model.extract(
    "Marie Curie was born in Warsaw. She married Pierre Curie, a professor at the University of Paris.",
    entities=["person", "location", "organization"],
    relations=["born in", "spouse", "works for"],
)
# {"entities": [{label, text, start, end, score}, ...], "relations": [{label, head, tail, score}, ...]}

model.redact("Call Laura Bianchi at +39 347 555 0192.", ["first name", "last name", "phone number"])
# 'Call [FIRST_NAME] [LAST_NAME] at [PHONE_NUMBER].'

What makes it fast

• Packed batches: the tokens of all texts are concatenated without padding; only attention sees a padded view.
• Fused matrices: q/k/v and the SwiGLU gate/up projections are one matrix product each; the LFM2 short convolutions run as three shifted multiply-adds that never cross text boundaries.
• No 4-D intermediate in span scoring: the FiLM layer is rewritten algebraically, so the texts × labels × spans × width tensor is never built.
• Cache-sized CPU batches: texts sorted by length, at most ~2,048 tokens per batch so activations stay in the CPU cache; tokenized words and label prompts are cached.
• Inference-only weights: training heads are dropped at load time, no transformers model is built.
• GPU (0.1.1): span selection on the GPU, an asynchronous pipeline and CUDA graphs for small batches: about 3 ms per text on an H100.
• Same predictions: in fp32 the output matches the reference implementation exactly (agreement F1 1.000).

Pruning

Structured width pruning of SauerkrautLM-GLiNEREL-314m (narrower residual stream and feed-forward layers, factorized token embedding; all 14 layers and all attention heads kept), followed by knowledge distillation from SauerkrautLM-GLiNEREL-314m.

Speed

One German document (1,065 characters), against gliner2.5 and 4B generative models.

Results

Development splits, zero-shot, against gliner2.5, gliner-relex, GPT-6 Luna and Qwen3.8-27B.

German relations (RED^FM test split).

German entities (RED^FM and MobIE test splits).

Tokens, latency and cost of the LLM baselines.

LLM baselines with reasoning and four prompts (600-record subset).

Thresholds are chosen per benchmark; LLMs get the same label sets with one fixed instruction at temperature 0.

Citation

@misc{sauerkrautlm_glinerel_2026,
  title={SauerkrautLM-GLiNEREL: Joint Entity, PII and Relation Extraction in One Encoder Pass},
  author={Michele Montebovi and Hakim Bedhief},
  organization={VAGO Solutions},
  url={https://huggingface.co/VAGOsolutions/SauerkrautLM-GLiNEREL-72m},
  year={2026}
}

Family: SauerkrautLM-GLiNEREL-314m · SauerkrautLM-GLiNEREL-128m · SauerkrautLM-GLiNEREL-72m