---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - llama
  - legal
  - finance
  - small-language-model
---

# shivamfet/slm-125m-base

A ~125M-parameter Llama-architecture language model pretrained from scratch on a
legal- and finance-heavy corpus (U.S. case law, SEC filings, and a slice of
FineWeb-Edu). Built as a compact domain SLM; not instruction-tuned.

**🌐 Live demo:** https://slm-125m-shivamk.vercel.app

## Model details

| | |
|---|---|
| Parameters | ~125.8M (tied embeddings) |
| Architecture | Llama (12 layers, 768 hidden, 12 heads) |
| Vocab | 16,384 (byte-level BPE, trained on-corpus) |
| Context length | 1024 |
| Precision | bf16 autocast (fp32 master weights) |

## Training

- **Corpus:** ~2.4B unique tokens (case-law / SEC / FineWeb-Edu), deduplicated
  (MinHash LSH) and decontaminated against CaseHOLD / LexGLUE eval sets.
- **Schedule:** 4 epochs (~8.15B tokens seen) on 8xH100, cosine LR (6e-4 -> 6e-5),
  token-based warmup, fused AdamW.
- **Result:** validation perplexity 16.0 -> 8.35, monotonic, no divergence.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

ids = tok("The plaintiff shall bear the burden of proving",
          return_tensors="pt", return_token_type_ids=False)
out = model.generate(**ids, max_new_tokens=80, do_sample=True,
                     top_k=50, temperature=0.8, pad_token_id=tok.pad_token_id)
print(tok.decode(out[0], skip_special_tokens=True))
```

## Limitations

Base model only — no instruction tuning or RLHF. It can produce plausible-sounding
but incorrect legal/financial text and must not be relied on for legal or financial
advice. Reflects biases in its public training sources.