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.

Training

1 epoch, 3,753 steps, global batch 524,288 tokens, 2x H100 (DDP, bf16, torch.compile). AdamW (0.9/0.95, wd 0.1 on 2D params), grad-clip 1.0.
LR: warmup to 6e-4 over 200M tokens, cosine to 6e-5 annealed over 1 epoch.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = 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_ids
print(tok.decode(model.generate(ids, max_new_tokens=40)[0], skip_special_tokens=True))

Limitations

Base model, English only, 125M params — expect fluent domain-styled text, not reliable facts or instruction-following. Not legal or financial advice.