Fin.AI

WORK IN PROGRESS – EXPERIMENTAL RESEARCH PROJECT

A continuously learning transformer language model that trains automatically every ~1.5 hours on diverse datasets using GitHub Actions.

Important Notice
Fin.AI is an experimental research prototype and work in progress.
The model is under continuous training and may produce inaccurate, inappropriate, biased, or nonsensical outputs.
Do NOT use for production applications, critical systems, or high-stakes decisions.
Use at your own risk.

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Overview

Fin.AI is an experimental GPT-style language model that trains 24/7 with a rotating curriculum of 24 different dataset families.

Core characteristics:

• Fully automated hourly training (GitHub Actions)
• 24 diverse dataset categories (news, math, code, dialogue, science, instructions...)
• Focus rotates every ~1.5 hours → targeted capability improvement
• Models automatically pushed to Hugging Face after each run
• Training metrics publicly visible on Weights & Biases
• Designed to run efficiently even on free GitHub runners

This is not a production-ready model. Expect evolving (and sometimes unstable) behavior.

Key Features

Feature	Description
Automated Continuous Training	Trains every ~1.5 hours – completely hands-free
Rotating Curriculum	24 dataset families covering very different capabilities
Hugging Face Integration	Latest checkpoint pushed automatically after every training cycle
Real-time Monitoring	Full metrics, loss curves and samples on Weights & Biases
Flexible Scale	Easily switch between ~15M and ~350M+ parameters
CPU-friendly	Optimized to train efficiently on standard GitHub Actions runners

Training Curriculum (24-cycle daily rotation)

Category	Example Datasets	Cycle Hours	Primary Focus
Encyclopedia	WikiText	0, 6	Broad world knowledge
Creative Writing	TinyStories	1, 18	Storytelling & narrative
News	CNN, AG News, CC News	2,15,17,20	Current events & factual style
Math & Reasoning	GSM8K, CommonsenseQA	3,9,19,23	Problem solving & logic
Open Web Text	OpenWebText, C4	4,11	Diverse internet language
Question Answering	SQuAD	5,22	Reading comprehension
Instruction Following	Alpaca, Dolly	7,14,21	Following user instructions
Reviews & Sentiment	IMDB, Amazon, Yelp	8,10,16	Opinion & sentiment analysis
Scientific / Medical	PubMed	12	Scientific & medical literature
Conversations	UltraChat	13	Natural dialogue

Quick Start

Download Latest Model

from huggingface_hub import hf_hub_download

hf_hub_download("MeridianAlgo/Fin.AI", "model.pt",   local_dir="./model")
hf_hub_download("MeridianAlgo/Fin.AI", "config.json", local_dir="./model")

Basic Inference Example

from fin_ai.model import FinAIModel
import torch

model = FinAIModel.from_pretrained("./model")
tokenizer = model.tokenizer

prompt = "The future of artificial intelligence is"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_length=100, temperature=0.8)
print(tokenizer.decode(outputs[0]))

Warning: Output quality is experimental and may contain factual errors, biases, or inappropriate content.

Model Sizes (V3)

Preset	Parameters	Layers	Heads	Hidden Dim	Recommended Use Case
tiny	~15M	6	4	256	Very fast experiments
small	~40M	8	8	512	Default – good CPU performance
medium	~120M	12	12	768	Noticeably higher quality
large	~350M	24	16	1024	Best results (GPU recommended)

Current Project Status

• Latest checkpoint: huggingface.co/MeridianAlgo/Fin.AI
• Training pipeline runs: GitHub Actions
• Live metrics & samples: Weights & Biases

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Made with passion by the Fin.AI team
⭐ Star on GitHub  🤗 View & download on Hugging Face