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

# SLM-125M-base

A **125.8M-parameter, Llama-style language model pretrained from scratch** on a
cleaned, deduplicated, and decontaminated legal + financial corpus. Give it the
start of a sentence and it continues in the legal/financial register.

This is a **base completer**, not a chat model. It was trained on next-token
prediction only, so it *continues* text rather than answering questions.

## Model details

| | |
|---|---|
| Parameters | 125,847,552 (~125.8M) |
| Architecture | Llama-style (`LlamaForCausalLM`) |
| Layers | 12 |
| Hidden size | 768 |
| Attention heads | 12 (head dim 64), MHA (12 KV heads) |
| MLP | SwiGLU, inner 3072 |
| Normalization | RMSNorm, pre-norm |
| Positional | RoPE (theta 10000) |
| Context length | 1024 |
| Vocab | 16,384 (byte-level BPE, trained from scratch) |
| Embeddings | tied (input = output) |
| Precision | bf16 |

## Training data

A **~2.04B-token** corpus built from three public, ungated sources, streamed and
filtered through a deterministic cleaning + dedup + decontamination pipeline:

| Source | Content | Share |
|---|---|---|
| [`HFforLegal/case-law`](https://huggingface.co/datasets/HFforLegal/case-law) | US court opinions | ~40% |
| [`PleIAs/SEC`](https://huggingface.co/datasets/PleIAs/SEC) | SEC filings (10-K, etc.) | ~40% |
| [`HuggingFaceFW/fineweb-edu`](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | Educational web text | ~20% |

The corpus was decontaminated against CaseHOLD / LexGLUE (13-gram overlap removed)
and deduplicated (exact + MinHash/LSH near-duplicate removal).

## Training recipe

| Knob | Value |
|---|---|
| Objective | next-token cross-entropy |
| Hardware | 8× H100 (single-node DDP) |
| Epochs | 1 (~2.04B tokens seen) |
| Optimizer | AdamW, betas (0.9, 0.95), weight decay 0.1 |
| LR | 6e-4 → 6e-5, cosine decay, 200M-token warmup |
| Global batch | ~524,288 tokens/step |
| Grad clip | 1.0 |
| Steps | 3,889 |

## Results

**Held-out validation perplexity: 11.01** (val loss 2.42) on a 1% held-out split
(~20.6M tokens). Perplexity was still descending at the end of the single epoch
(step 2000 → 12.54, step 3000 → 11.27, final → 11.01), so additional epochs would
lower it further. Total compute cost to train: **~$11**.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Sudhanshu1985/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("Sudhanshu1985/slm-125m-base")

prompt = "The plaintiff alleges that the defendant"
inputs = tok(prompt, return_tensors="pt")
out = model.generate(
    **inputs,
    max_new_tokens=80,
    min_new_tokens=40,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.3,
)
print(tok.decode(out[0], skip_special_tokens=True))
```

### Example completions

- *"The plaintiff alleges that the defendant"* → "breached its contract of employment... In order to prove fraud, a party must show: (1) The existence of a confidential relationship; (2) the absence or violation by one party of any duty owed..."
- *"Pursuant to the terms of this Agreement,"* → "the parties have agreed as follows: 1. ...all disputes... shall be subject to the jurisdiction of said Court..."
- *"The Company's net revenues for the fiscal year"* → "ended September 30, 1996 were $305.1 million. The operating loss in fiscal 1995 was primarily attributable to..."

## Limitations

- **Base completer, not a chatbot.** It continues text; it does not follow
  instructions or answer questions.
- **Does not know facts.** At 125M parameters a model holds only a small amount of
  usable knowledge; grounded facts would require retrieval (RAG).
- **Domain-biased.** It speaks the legal register fluently; non-legal prompts drift
  toward that register.
- Trained on US legal/financial + educational web text; may reflect biases in those
  sources. Not legal or financial advice.

## Provenance

Pretrained from random weights (nothing fine-tuned). Pipeline: stream 3 datasets →
rule-based cleaning → dedup + decontaminate → 16K byte-level BPE → pack 1024-token
windows → pretrain on 8× H100. Based on the Vizuara AI Labs
[slm-125m-from-scratch](https://github.com/Vizuara-AI-Lab/slm-125m-from-scratch) recipe.