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
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.
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docker model run hf.co/ppanja/slm-125m-base