adaption_personal_finance_advice_qa

Personal Finance Advice — Llama-3.3-70B LoRA

A LoRA adapter for Llama-3.3-70B-Instruct, fine-tuned to answer practical personal finance questions: debt and credit, retirement, investing, budgeting, tax, insurance, savings, and estate planning.

Trained with Adaption Labs' AutoScientist for the AutoScientist Challenge (Personal Finance category).

Result

Head-to-head win rate against the base model:

These are wins in a paired comparison, not accuracy percentages.

The two rows disagree, and that is worth explaining rather than hiding. The training answers are terse extracted action-blocks that read as fragments, so on the narrow in-distribution set a judge often prefers the base model's fluent prose. Across the wider category the adapted model wins decisively — the substance transferred even though the presentation did not.

Usage

The adapter is stored unpacked, so it loads directly.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "meta-llama/Llama-3.3-70B-Instruct"
ADAPTER = "flamiinngo/adaption_personal_finance_advice_qa"

tokenizer = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(
    BASE, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

messages = [{"role": "user", "content":
    "I have $8,000 in credit card debt at 22% APR and $5,000 in savings "
    "earning 4%. Should I pay off the card?"}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

out = model.generate(inputs, max_new_tokens=300, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Hardware: the 70B base needs roughly 140 GB in bf16, or about 40 GB with 4-bit quantisation. The adapter itself is 3.3 GB.

Note on the base model name. adapter_config.json records togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference, which is how the base was served during training. It is the same architecture — load against meta-llama/Llama-3.3-70B-Instruct as shown above.

Training

Trained on 11,639 rows after AutoScientist augmentation — 90% personal-finance by its own domain classification, with the remainder legal and career-adjacent.

Dataset

flamiinngo/personal-finance-advice-qa — 3,805 real questions from r/personalfinance and r/FinancialPlanning, with answers cut to their actionable core (median 77 words). Derived from Akhil-Theerthala/Personal-Finance-Queries (MIT).

Also on Kaggle: model · dataset

Limitations

• Not financial advice. This model produces plausible guidance, not professional advice. Do not use it to make financial decisions.
• US-centric. IRS forms, 401(k) rules, FHA loans, US credit scoring. Little of it transfers to other jurisdictions.
• Time-sensitive. Contribution limits, tax thresholds and rates change; some training content reflects conditions that have since moved.
• The training answers were not written by financial professionals. The questions are real Reddit posts. The upstream dataset describes its contents as "Reddit posts and top comments" but the answer text reads as model-generated; the source does not document which. Either way, no verified professional wrote them.
• It can state figures confidently and be wrong. Treat any number it gives as unverified.
• Win rate is not accuracy. It measures preference against one base model on one evaluation.
• English only.

License

The adapter is a derivative of Llama-3.3-70B-Instruct and is subject to the Llama 3.3 Community License. The training data is MIT.

Acknowledgements

• Adaption Labs — AutoScientist platform and the challenge
• Akhil Theerthala — upstream personal finance dataset
• Meta — Llama 3.3 base model