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VigneshwarKandhaiya
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Model Details
Model Provider
VigneshwarKandhaiya
Model Tree
Input Modalities
Output Modalities
Supported Functionality
HFforLegal/case-law (US court opinions) — ~36%PleIAs/SEC (SEC filings) — ~40%HuggingFaceFW/fineweb-edu (educational web) — ~24%Pipeline: deterministic cleaning (OCR gate, boilerplate/repetition/language filters) → MinHash near-dup + exact-dup removal → 13-gram decontamination against CaseHOLD/LexGLUE (so eval is uncontaminated) → 16K byte-level BPE → packed 1024-token windows.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("VigneshwarKandhaiya/legal-slm-125m-base")model = AutoModelForCausalLM.from_pretrained("VigneshwarKandhaiya/legal-slm-125m-base")ids = tok("The plaintiff shall bear the burden of", return_tensors="pt").input_idsprint(tok.decode(model.generate(ids, max_new_tokens=40)[0], skip_special_tokens=True))
Base model, English only, 125M params — expect fluent domain-styled text, not reliable facts or instruction-following. Not legal or financial advice.