meridianal/FinAI

Meridian.AI — Continual-Learning Finance LLM

Meridian.AI is a finance-specialized language model that continuously fine-tunes a Qwen2.5-0.5B backbone every hour on 25+ finance and math datasets, using Elastic Weight Consolidation (EWC) to prevent catastrophic forgetting across training sessions. The entire pipeline runs unattended on free GitHub Actions infrastructure — no GPUs.

• Base model: Qwen/Qwen2.5-0.5B (~494M params, Qwen2 architecture)
• Continual learning: Elastic Weight Consolidation (diagonal Fisher)
• Training cadence: hourly GitHub Actions CI on CPU runners
• Source code & full docs: github.com/MeridianAlgo/FinAI

Usage

The deployed checkpoint is a standard Qwen2 model — trust_remote_code=True is not required.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "meridianal/FinAI"

tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="checkpoint")
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    subfolder="checkpoint",
    torch_dtype=torch.float32,
    low_cpu_mem_usage=True,
)
model.eval()

prompt = """### Instruction:
Explain the difference between a bond's yield to maturity and its coupon rate.

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=200,
        do_sample=True,
        temperature=0.8,
        top_p=0.92,
        repetition_penalty=1.3,
        no_repeat_ngram_size=3,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Inputs are formatted with the ### Instruction: / ### Response: template used during training.

Model details

Specification	Value
Base model	Qwen2.5-0.5B
Architecture	Qwen2ForCausalLM
Parameters	~494M
Context window	32,768 tokens (Qwen2.5 default)
Training dtype	bfloat16
Continual learning	Elastic Weight Consolidation (EWC)

Training data

A weighted streaming mix of 25+ finance and instruction datasets, including gbharti/finance-alpaca, sujet-ai/Sujet-Finance-Instruct-177k, nvidia/OpenMathInstruct-2, HuggingFaceFW/fineweb-edu, yahma/alpaca-cleaned, and the FinanceMTEB suite. See the repository README for the full curriculum and weights.

Limitations & disclaimer

This is an experimental research project on continual learning for financial NLP. Outputs may contain factual errors and are intended for academic and research purposes only. Nothing generated by this model constitutes financial advice. Do not use outputs to make real financial decisions or execute trades.