One of 13 models in a controlled study of how far a small language model can be pushed on US legal and financial text. Every version was trained on the same data, evaluated on the same frozen held-out set, and scored by the same blind LLM judge, so the stages are directly comparable. Compare them side by side in the SLM Arena.

Trained and aligned by Harman Sandhu (Vizuara AI Labs).

At a glance


Base model	`Ace-2504/slm-125m-e4`
Method	full fine-tune
Parameters	125.8M
Trainable parameters	125.8M (all)
Training data	10,000 retrieval-augmented examples (golden passage + distractors)
Schedule	3 epochs · 1,875
Compute	19.4 min on L4 (Modal)
Cost	$58.65 total lineage
Judge score	0.81 / 10

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "Ace-2504/slm-125m-raft"
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16).eval()
tok = AutoTokenizer.from_pretrained(MODEL)

SYS = ('You are a precise legal and financial assistant. Answer clearly using the '
       'provided context; do not invent facts.')
user = 'Context:\n<passage>\n\nQuestion: <your question>'

# Custom chat scheme. NOTE: never prepend <|bos|> — it was left untrained.
prompt = f'<|system|>\n{SYS}<|eos|>\n<|user|>\n{user}<|eos|>\n<|assistant|>\n'
enc = tok(prompt, return_tensors='pt', add_special_tokens=False).to(model.device)
eos = tok.convert_tokens_to_ids('<|eos|>')
out = model.generate(**enc, max_new_tokens=160, eos_token_id=eos,
                     pad_token_id=eos)   # pad and eos share an id here
print(tok.decode(out[0, enc['input_ids'].shape[1]:], skip_special_tokens=True))

Evaluation

Scored on 500 decontaminated held-out questions (chunk-level dedup against public legal benchmarks, so no evaluation passage was trained on). Every model in the study answered the identical questions with deterministic greedy decoding, and a Gemini judge blind to the model graded each answer with the gold answer in hand.

Metric	Value
Judge score (0–10 rubric)	0.81
Judged correctness (0–1)	0.049
Groundedness	10.8%
Fabrication ↓	0.2%
Token-F1	0.078
n	500

By source:

US case law	SEC filings	Educational web
0.98	0.55	0.98

The 0–10 figure is a four-dimension rubric (correctness 0–5 + completeness 0–2 + groundedness 0–2 + clarity 0–1). The 0–1 figure is the stricter correctness-only scale used in the experiment reports. Same questions, same answers, same judge — different scale, so the two numbers differ.

Token-F1 is reported for completeness and should not be read as quality: it punishes correct paraphrase heavily, which is exactly why the judge carries the headline.

Architecture


Class	LlamaForCausalLM
Layers	12
Hidden size	768
Attention	12 heads · head dim 64 · full MHA
Feed-forward	SwiGLU · inner 3,072
Positional	RoPE · θ 10,000
Norm	RMSNorm · ε 1e-5
Context	1,024 tokens
Vocabulary	16,384 · byte-level BPE
Embeddings	tied input/output

Training

Initialised from Ace-2504/slm-125m-e4
Method — full fine-tune
Data — 10,000 retrieval-augmented examples (golden passage + distractors), generated from a legal/financial corpus and gated by an LLM judge for faithfulness
Schedule — 3 epochs · 1,875, 19.4 min on L4
Loss is masked to the answer tokens only, so the model is never trained to reproduce the prompt

Cost — $58.65. This line was pretrained from scratch, so the total includes the base: $57.79 of A100 time across four pretraining legs (v1 → extension → e2 → e4, 27.5 h at $2.10/h) plus $0.86 for this fine-tune. Models built on someone else's base do not carry that cost.

Limitations and intended use

Not legal or financial advice. This is a research artefact for studying small-model training, not a professional tool. Do not rely on its output for real decisions.
At 125.8M it holds very little world knowledge. It is built to read an answer out of a passage you supply, not to recall facts. Used closed-book it will produce fluent, confident and wrong text.
This checkpoint scores 0.81/10 — it is published for comparison across training stages, not because it is good. See the arena for why.
Emits its evidence wrapped in ##begin_quote## … ##end_quote## markers, and the sampler mangles these fairly often — strip or parse them before display.
The judge behind these scores has not been calibrated against human labels, so treat small differences between models cautiously.
English only; the corpus is US case law, SEC filings and educational web text.

The rest of the family

Size	Base	QA SFT	RAFT	DPO	RLAIF
125M	`slm-125m-e4`	`slm-125m-sft`	`slm-125m-raft`	`slm-125m-sft-dpo`	`slm-125m-sft-rlaif`
500M	`slm-500m-base`	`slm-500m-sft`	`slm-500m-raft`	`slm-500m-sft-dpo`	`slm-500m-sft-rlaif`
Gemma 2B	`gemma-2-2b-it`	`gemma-2-2b-sft`	`gemma-2-2b-raft`	`gemma-2-2b-sft-dpo`	`gemma-2-2b-sft-rlaif`

All under Ace-2504 except the two imported bases.

This model has its own write-up — training details, cost breakdown and live demo — at https://slm-125m-raft-harman.vercel.app.

Citation

bibtex

@misc{sandhu2026slm,
  title  = {Small Language Models for Legal and Financial Text: a controlled study of
            pretraining, instruction tuning, retrieval augmentation and alignment},
  author = {Harman Sandhu},
  year   = {2026},
  note   = {https://slm-arena-harman.vercel.app}
}