---
language: en
license: apache-2.0
tags:
  - causal-lm
  - llama
  - legal
  - financial
  - from-scratch
library_name: transformers
---

# slm-125m

125M-parameter causal language model trained from scratch on a legal/financial corpus.

## Model

| | |
|---|---|
| Architecture | Llama-style (SwiGLU, RoPE, RMSNorm) |
| Parameters | ~125.8M |
| Layers / dim / heads | 12 / 768 / 12 |
| Context | 1024 tokens |
| Vocab | 16384 (byte-level BPE) |

## Training data

| Source | Role |
|--------|------|
| HFforLegal/case-law | US case law |
| PleIAs/SEC | SEC filings |
| HuggingFaceFW/fineweb-edu | General educational web text |

Packed train tokens: **unknown** | val tokens: **unknown**

Eval benchmarks (LexGLUE, CaseHOLD) were held out during corpus construction.

## Training run

| Hyperparameter | Value |
|----------------|-------|
| Global batch (tokens) | 524,288 |
| LR / min LR | 0.0006 / 6e-05 |
| Warmup tokens | 200M |
| Weight decay | 0.1 |

Last logged train loss: **n/a** | val loss: **n/a**

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("ppanja/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("ppanja/slm-125m-base")
inputs = tok("The plaintiff shall bear the burden of proof", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=64)
print(tok.decode(out[0]))
```

Chat special tokens (`<|user|>`, `<|assistant|>`, `<|system|>`) are in the vocabulary for future instruction tuning.