slm125live-base/README.md

---
library_name: transformers
tags:
  - text-generation
  - legal
  - finance
  - pretraining
  - from-scratch
pipeline_tag: text-generation
---

# SLM125 — a 125M-parameter legal/financial base language model, trained from scratch

**SLM125** is a small (~125.8M parameter), decoder-only transformer pretrained from
scratch on a legal- and finance-heavy corpus. It is a **base** model — it completes
text, it is not instruction-tuned or chat-tuned. Give it a sentence to continue
("The plaintiff argued that ..."), not a question.

🔗 **Try it live:** [SLM125 Playground](https://web-roan-chi-97.vercel.app)

## Model Details

- **Architecture:** Llama-style decoder-only transformer (RoPE, RMSNorm, SwiGLU MLP, tied embeddings)
- **Parameters:** ~125.8M
- **Hidden size:** 768
- **Layers:** 12
- **Attention heads:** 12 (head dim 64, standard multi-head attention — no GQA)
- **Intermediate (MLP) size:** 3,072
- **Context length:** 1,024 tokens
- **Vocabulary:** 16,384 tokens (custom-trained tokenizer)
- **RoPE theta:** 10,000
- **Precision:** trained and served in fp32/bf16 on Modal

## Training Data

Pretrained on a cleaned, deduplicated, decontaminated corpus of **~2.19B tokens**,
mixed "legal-first" from three public, streamed HuggingFace datasets:

| Source | Dataset | Share | What it is |
|---|---|---|---|
| case-law | [`HFforLegal/case-law`](https://huggingface.co/datasets/HFforLegal/case-law) (`us` config) | ~39% (~863M tokens) | US court opinions (scanned; some OCR noise) |
| sec | [`PleIAs/SEC`](https://huggingface.co/datasets/PleIAs/SEC) | ~39% (~861M tokens) | SEC filings (10-K, etc.), born-digital |
| fineweb-edu | [`HuggingFaceFW/fineweb-edu`](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (`sample-10BT`) | ~21% (~465M tokens) | General educational web text, added as fluency filler |

The two legal sources were taken in full (they cap out around 2B tokens combined);
a smaller web slice was added on top, landing at roughly a 40/40/20 split — about
78% legal/financial text overall, not the originally-planned 70/20/10.

## How to Get Started

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "srinivasch87/slm125live-base"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

prompt = "The plaintiff argued that"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Intended Use & Limitations

- This is a **base completion model**, not an instruction-following assistant.
  It will not reliably answer questions, follow chat-style instructions, or
  refuse unsafe requests — prompt it with the start of a sentence or document.
- At 125M parameters and ~2.2B training tokens, factual accuracy, coherence over
  long spans, and reasoning are limited. Treat all output as unverified
  autocomplete, not legal, financial, or professional advice.
- Training data skews heavily toward US case law and SEC filings, so the model's
  style and any biases reflect that domain (and the OCR noise present in the
  scanned case-law source).
- License is not specified for this model; the underlying training datasets each
  carry their own terms — check the dataset cards linked above before
  redistributing outputs.

## Training & Serving Infrastructure

Built end-to-end on [Modal](https://modal.com): data cleaning, deduplication,
tokenizer training, and pretraining all ran as fanned-out CPU/GPU Modal functions
against a shared Modal Volume. Inference is served from a Modal web endpoint with
token-streaming generation, behind a Next.js frontend deployed on Vercel (link
above).