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narendarcodes/
adaption-sec-financial-arithmetic-109b

Name: adaption-sec-financial-arithmetic-109b
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Adaption AutoScientist — SEC Financial Arithmetic (109B LoRA)

Powered by Adaptive Data — Adaption Labs

🎯 Core Problem Solved

Given a financial table from an SEC filing (10-K, 10-Q, earnings report), this model extracts the correct numeric values, identifies the right formula, executes multi-step arithmetic, and produces a verified numeric answer — never hallucinating numbers.

Specifically, this model performs:

Table-Grounded Variable Extraction — "Given this SEC filing table with 20+ rows and 5+ columns, identify exactly which cells contain the values needed for this calculation"
Multi-Step Financial Arithmetic — "Compute the year-over-year revenue growth rate by extracting 2021 and 2022 revenue, then applying: ((2022 - 2021) / 2021) × 100"
Formula-Verified Calculations — "Every answer is cross-checked against a gold reasoning program (e.g., subtract(8.5, 7.2) → divide(#0, 7.2) → multiply(#1, 100)) to ensure mathematical correctness"
Compound Financial Metrics — "Compute LTI payout percentages, operating margins, debt-to-equity ratios, and other multi-variable derived values from dense financial context"

Why This Matters

Financial analysts extract numbers from dense corporate filings to compute key metrics. Current LLMs:

Hallucinate numbers that aren't in the table
Skip intermediate steps (jumping to a final answer without showing work)
Apply wrong formulas (e.g., computing growth rate with the wrong denominator)

This model is trained on real SEC filing data with gold reasoning programs that verify every arithmetic step, teaching it to show complete <think> traces before outputting a verified <answer>.

Model Details

Field	Value
Trained Model Name	`adaption_finqa_financial_reasoning`
Base Model	`meta-llama/Llama-4-Scout-17B-16E-Instruct` (109B MoE)
Training Method	Supervised Fine-Tuning (SFT) with LoRA
Training Platform	Adaption Labs AutoScientist
Language	English (en)
License	MIT

AutoScientist Platform Integration

The model was adapted using the automated training features of the Adaption Labs AutoScientist platform:

Adaptive Data Pipeline: Validated the financial dataset structure, maintaining a stable Grade B (8.9 score) across multi-step calculation patterns.
Prompt Deduplication: Removed redundant financial questions, focusing training on unique computation flows.
Prompt Rephrase Constraint: Prompt rephrasing was disabled to protect the integrity of financial tables, column headers, and tabular structures from corruption.
Reasoning Traces & Blueprint Constraints: Enforced structured arithmetic thinking. The model is trained to show complete working in a <think> trace verified against gold calculation programs before outputting the final answer. The blueprint constraints mandate:
1. Extracting the exact cell coordinates and values from the input SEC table.
2. Stating the mathematical formula explicitly.
3. Executing arithmetic steps sequentially.
4. Outputting a clear, verified final numeric value.
Hyperparameter Optimization: Managed the training run using LoRA SFT targeting the attention and feed-forward layers of meta-llama/Llama-4-Scout-17B-16E-Instruct.
Evaluation: Compared the adapted model performance against the base model on held-out financial prompts using automated preference scoring.

📊 Dataset

Dataset: narendarcodes/adaption-sec-financial-arithmetic-dataset
Size: 1,055 rows
License: MIT

Source Data & Attribution:

Source	Rows	What It Teaches	License
czyssrs/FinQA	~550	Financial QA from real SEC filings with gold reasoning programs	MIT
cerebras/TAT-QA-Arithmetic-CoT	~550	Table-and-text financial QA with chain-of-thought arithmetic	Apache-2.0

Metric	Before	After	Change
Grade	B	B	—
Score	9.0	8.9	-1.1%
Percentile	—	28.9%	—

⚙️ Training Configuration

Hyperparameter	Value
Finetune Job ID	`5213d7c9-d937-4f5b-b4f8-c7316c0a18b3`
Training Experiment ID	`4fa65b4d-3ed8-4189-9e8f-98a920c92a4d`
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.1
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `shared_expert.`, `feed_forward.`
Epochs	1
Batch Size	max
Learning Rate	2e-5
LR Scheduler	Cosine (0.5 cycles)
Warmup Ratio	0.05
Weight Decay	0.05
Max Grad Norm	1.0

📈 Evaluation Results

Evaluation Set	Base Model	Adapted Model	Winner
On Your Dataset	44%	58%	✅ Adapted
Across Category (Held-out)	40%	61%	✅ Adapted

How to Use

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")
model = PeftModel.from_pretrained(base_model, "narendarcodes/adaption-sec-financial-arithmetic-109b")
tokenizer = AutoTokenizer.from_pretrained("narendarcodes/adaption-sec-financial-arithmetic-109b")

Citation

@misc{golla2026secfinancial,
  title={SEC Financial Arithmetic — Table-Grounded Multi-Step Calculation from Corporate Filings},
  author={Golla Narendar},
  year={2026},
  note={Trained using Adaption Labs AutoScientist platform. Powered by Adaptive Data.}
}

Powered by Adaptive Data — Adaption Labs

Built for the 2026 Adaption AutoScientist Challenge — Finance Category

Framework Versions

PEFT 0.15.1