SLM-125M-Base: A Legal/Financial Small Language Model

A 125M-parameter decoder-only transformer trained from scratch on a curated legal and financial corpus. Built on the LLaMA architecture with a custom 16K byte-level BPE tokenizer.

This is a base model (no instruction tuning or RLHF). It is intended as a foundation for downstream fine-tuning on legal and financial NLP tasks.

GitHub: trehansalil/slm-engineering

Model Details

Architecture	LLaMA (decoder-only transformer)
Parameters	125.8M (tied embeddings)
Layers / Hidden / Heads	12 / 768 / 12 (MHA, head dim 64)
Intermediate (SwiGLU)	3,072
Context length	1,024 tokens
Vocabulary	16,384 (byte-level BPE)
Precision	bfloat16
License	Apache 2.0

Training Data

The model was trained on a legal-first data mix (~40/40/20) totaling 2.04 billion tokens after cleaning and deduplication:

Source	Dataset	Domain	Approx. Share
US Case Law	HFforLegal/case-law (split: us)	Legal	~40%
SEC Filings	PleIAs/SEC	Financial	~40%
FineWeb-Edu	HuggingFaceFW/fineweb-edu (sample-10BT)	General	~20%

Data Processing

All data underwent a rigorous cleaning and deduplication pipeline:

• 6-step deterministic cleaning: line filtering, boilerplate stripping, repetition detection, English language filtering, OCR quality gating, minimum length enforcement
• Exact deduplication via blake2b hashing on normalized text
• Near-deduplication via MinHash LSH (128 permutations, Jaccard threshold 0.7)
• Contamination stripping: 13-gram overlap removal against CaseHOLD and LexGLUE evaluation sets to prevent benchmark leakage

Training Procedure

Hardware	8× NVIDIA H100 GPUs (Modal serverless)
Parallelism	PyTorch DDP (DistributedDataParallel)
Epochs	1
Total steps	3,889
Global batch size	524,288 tokens
Optimizer	AdamW (β₁=0.9, β₂=0.95, weight decay=0.1)
Learning rate	6×10⁻⁴ with cosine decay to 6×10⁻⁵
Warmup	200M tokens (~381 steps)
Gradient clipping	1.0
Wall time	19 minutes
Throughput	~1.96M tokens/sec

Training Loss

Step	Train Loss	Val Loss	Val Perplexity
1,000	2.80	2.81	16.54
2,000	2.55	2.53	12.56
3,000	2.44	2.42	11.29
3,500	2.38	2.41	11.08

Evaluation

Metric	Value
Validation loss	2.4054
Validation perplexity	11.08

Evaluation was conducted on a held-out 1% split (20.6M tokens) of the training corpus.

Intended Use

This model is designed as a base model for fine-tuning on domain-specific legal and financial tasks, including:

• Legal document classification
• Contract analysis and clause extraction
• SEC filing summarization
• Legal question answering
• Financial sentiment analysis

Out-of-Scope Use

• This is a small (125M) model and is not suitable as a general-purpose assistant or chatbot without significant fine-tuning
• The model has not been instruction-tuned or aligned — it may generate harmful, biased, or factually incorrect content
• Not suitable for legal advice, financial decisions, or any application requiring factual accuracy without human review

Limitations

• Model size: At 125M parameters, this model has limited capacity compared to larger LLMs. It is best suited for focused domain tasks rather than broad language understanding.
• Single epoch: The model was trained for 1 epoch over 2.04B tokens. Additional training epochs or more data may improve performance.
• Context length: Limited to 1,024 tokens. Documents longer than this must be chunked.
• English only: The model was trained exclusively on English-language text.
• Temporal cutoff: Training data reflects documents available as of mid-2026. The model has no knowledge of events after this date.
• No safety alignment: The model has no RLHF, constitutional AI, or other safety training.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Saliltrehan7/slm-125m-base")
tokenizer = AutoTokenizer.from_pretrained("Saliltrehan7/slm-125m-base")

prompt = "The court held that the defendant"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Infrastructure

The entire pipeline — data processing, tokenizer training, model pretraining, and deployment — ran on Modal's serverless GPU platform. Total compute cost: $12.36.

Phase	Cost
Data pipeline (clean, dedup, tokenize)	$1.57
Pretraining (8× H100, 19 min)	$10.59
Eval + upload	$0.20
Total	$12.36

Citation

@misc{slm125m2026,
  title={SLM-125M-Base: A Legal/Financial Small Language Model},
  author={Salil Trehan},
  year={2026},
  url={https://huggingface.co/Saliltrehan7/slm-125m-base}
}