---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- legal
- finance
- llama
- pretraining
- small-language-model
datasets:
- HFforLegal/case-law
- PleIAs/SEC
- HuggingFaceFW/fineweb-edu
---

# legal-slm-125m-base

A 125M-parameter Llama-architecture small language model pretrained **from
scratch** on a legal/financial corpus. Base (not instruction-tuned) model.

## Results
- **Validation perplexity: 10.67** (val_loss 2.3674), 1 epoch over 1.97B tokens.

## Model
- 125,847,552 params — 12 layers / 768 hidden / 12 heads, context 1024,
  vocab 16,384 (custom byte-level BPE), tied embeddings, SwiGLU, RoPE.

## Training data (~76% legal)
- `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.

## 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.