meridianal/FinAI

Meridian.AI — Continual-Learning Finance LLM

Meridian.AI is a finance-specialized language model that trains itself continuously, every hour, entirely on free GitHub Actions infrastructure. It uses a Sparse Mixture-of-Experts (SMoE) architecture with a Qwen2.5-0.5B backbone fine-tuned via Elastic Weight Consolidation (EWC) to prevent catastrophic forgetting across training sessions.

Model checkpoints: huggingface.co/meridianal/FinAI

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Table of Contents

• Why Meridian.AI
• Key Technical Innovations
• Architecture
• Quick Start
• Inference
• Local Training
• Environment Variables Reference
• CI/CD Training Pipeline
• Dataset Curriculum
• Repository Structure
• Troubleshooting
• Disclaimer

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Why Meridian.AI

Standard LLMs have a static knowledge cutoff. For finance — where earnings reports, Fed decisions, and market conditions change daily — this is a critical limitation.

Meridian.AI solves this with an automated continuous training pipeline: every hour, GitHub Actions pulls the latest checkpoint from HuggingFace, trains on fresh financial data, and pushes the updated checkpoint back. No GPUs. No cloud bills. No manual intervention.

Key constraints this design respects:

• 16 GB RAM ceiling of free GitHub Actions ubuntu-latest runners
• 2000 CI minutes/month free tier (public repos: unlimited)
• No persistent storage between runs — all state is round-tripped through HuggingFace Hub

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Key Technical Innovations

1. Sparse Mixture-of-Experts (SMoE)

Each MoE layer contains 8 expert feed-forward networks. For each input token, a learned router selects the top-2 most relevant experts. Only those 2 run — the other 6 are skipped. This gives a large total parameter count (479M) with a much smaller active compute cost (283M parameters per forward pass).

A load-balancing auxiliary loss (Switch Transformer style) ensures experts are used evenly and no single expert dominates routing.

2. Elastic Weight Consolidation (EWC)

After each hourly training run, the model computes the diagonal Fisher Information Matrix — a measure of which parameters were most important for tasks learned so far. The next run adds a regularization penalty for changing those parameters. This prevents the model from "forgetting" financial knowledge from earlier training sessions while absorbing new data.

3. Financial Numeracy Encoding

Standard tokenizers treat numbers as arbitrary tokens. Meridian.AI adds a 64-dimensional learned embedding specifically allocated to encode numeric magnitude signals, giving the model dedicated capacity to reason about quantities like prices, percentages, and financial ratios.

4. Memory-Safe CPU Training

Every component is designed around the 16 GB RAM constraint:

• AdaFactor optimizer: eliminates the 2×-parameter-size optimizer state of Adam
• Gradient checkpointing: trades compute for activation memory
• Soft RAM throttle: dynamically truncates sequence length when memory pressure rises
• Hard RAM guard: emergency checkpoint + clean exit before OOM
• Fisher threshold pruning: only stores EWC state for parameters with significant Fisher values

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Architecture

Specification	Value
Base Model	Qwen2.5-0.5B (continually fine-tuned)
Custom Arch Module	Sparse MoE Transformer (meridian/)
Layers	14 (alternating Dense ↔ MoE)
Attention	Grouped Query Attention: 12 Q heads, 4 KV heads
Position Encoding	RoPE (theta=500,000)
Feed-Forward	SwiGLU
Normalization	RMSNorm
MoE Experts	8 per layer, top-2 active per token
Vocabulary	151,665 tokens (Qwen2.5 tokenizer)
Context Window	2,048 tokens
Total Parameters	~479M (tied) / ~283M unique
Active per Token	~283M
Continual Learning	Elastic Weight Consolidation (EWC)

Layer alternation pattern (0-indexed):

Layer 0:  Dense FFN
Layer 1:  Sparse MoE (8 experts)
Layer 2:  Dense FFN
Layer 3:  Sparse MoE (8 experts)
... (alternates for all 14 layers)

For full architectural detail, see docs/architecture.md.

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Quick Start

Prerequisites

• Python 3.10+
• Git
• ~2 GB disk (for model weights)

Installation

git clone https://github.com/MeridianAlgo/FinAI.git
cd FinAI
pip install -r requirements.txt

Verify Installation (Smoke Test)

Runs a tiny in-memory model to confirm the architecture works without downloading anything:

SMOKE_TEST=1 FAST_MODE=1 python train.py

Expected output: [OK] Smoke test passed!

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Inference

From HuggingFace Hub

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",
    trust_remote_code=True,
    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))

From Local Checkpoint

If you have trained locally and have a ./checkpoint directory:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("./checkpoint")
model = AutoModelForCausalLM.from_pretrained("./checkpoint")

See docs/examples/01_inference.py for a complete, annotated script.

---

Local Training

Full Training Run

export HF_TOKEN=your_huggingface_token
python train.py

The script will:

1. Pull the latest checkpoint from HuggingFace (if HF_TOKEN is set)
2. Load the Qwen2.5-0.5B base model (or resume from checkpoint)
3. Stream finance datasets and train for MAX_STEPS steps
4. Save checkpoint locally (and upload if token is present)

Fast Debugging Mode

Runs with minimal settings (no dataset streaming, 5 steps, tiny sequences):

FAST_MODE=1 python train.py

Custom Step Count

MAX_STEPS=300 BATCH_SIZE=1 python train.py

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Environment Variables Reference

All variables are optional. CI defaults are shown in train.yml.

Core Training

Variable	CI Default	Description
MAX_STEPS	150	Gradient update steps per run
TOTAL_STEPS	100000	Cumulative steps across all runs (for LR schedule)
BATCH_SIZE	1	Samples per micro-step
GRAD_ACCUM	8	Micro-steps before each optimizer update
LEARNING_RATE	5e-5	Peak learning rate
BLOCK_SIZE	256	Token sequence length
DTYPE	bfloat16	Model dtype (bfloat16 or float32)
OPTIMIZER	adafactor	Optimizer (adafactor or adamw)

Memory Management

Variable	CI Default	Description
HARD_RAM_GUARD	1	Enable emergency save + stop at RAM ceiling
MAX_RAM_GB	14.5	Hard RAM limit in GB
SOFT_RAM_GB	12.5	Soft limit — begins sequence truncation
SOFT_RAM_PCT	80	Soft limit as % of total RAM
MIN_THROTTLE_SEQ_LEN	64	Minimum sequence length during throttle
GRADIENT_CHECKPOINTING	1	Trade compute for activation memory
SKIP_OPTIMIZER_SAVE	1	Omit 2GB+ optimizer state from checkpoint

Dataset

Variable	CI Default	Description
MAX_BYTES	15728640 (15 MB)	Max training data per run
USE_LIGHT_DATASETS	0	Restrict to small/fast datasets only

EWC (Continual Learning)

Variable	CI Default	Description
USE_EWC	1	Enable Elastic Weight Consolidation
EWC_LAMBDA	500.0	EWC regularization strength
EWC_SAMPLES	5	Batches used to estimate Fisher matrix
SKIP_FISHER	0	Skip Fisher computation (disables EWC next run)
FREE_OPTIMIZER_BEFORE_FISHER	1	Free optimizer RAM before Fisher computation
FISHER_SEQ_LEN	64	Sequence length used during Fisher estimation
FISHER_THRESHOLD	1e-6	Drop Fisher entries below this value

Paths & Misc

Variable	Default	Description
CHECKPOINT_PATH	./checkpoint	Local checkpoint directory
TOKENIZER_ID	Qwen/Qwen2.5-0.5B	HuggingFace tokenizer ID
FAST_MODE	0	Minimal config for quick local debugging
SMOKE_TEST	0	Run tiny in-memory architecture test
GC_EVERY_STEPS	5	Python GC frequency (steps)
DEBUG_STEPS	0	Print verbose per-step debug info
COMET_API_KEY	(unset)	Comet ML experiment tracking key

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CI/CD Training Pipeline

Every hour (GitHub Actions cron: '0 * * * *')
│
├── Pull checkpoint from HuggingFace Hub
│     meridianal/FinAI  →  ./checkpoint/
│
├── Train (timeout: 90 minutes)
│     • Load Qwen2.5-0.5B (or resume checkpoint)
│     • Stream finance datasets (weighted curriculum mix)
│     • 150 AdaFactor steps with gradient checkpointing
│     • EWC regularization (prevent forgetting)
│     • Auto-throttle sequence length if RAM > 12.5 GB
│     • Emergency save + exit if RAM > 14.5 GB
│
├── Upload checkpoint to HuggingFace Hub
│     ./checkpoint/  →  meridianal/FinAI/checkpoint/
│
└── Sync dataset state to git
      dataset_state.json  →  main branch

Failure Handling

If training encounters >50 [ERROR] lines or any fatal pattern (OOM, NaN explosion), the CI workflow automatically opens a GitHub Issue with the error details and a diagnostic checklist.

Triggering a Manual Run

From the GitHub Actions tab, click Meridian.AI Train → Run workflow. You can override MAX_STEPS at dispatch time.

Force Reset (Nuke & Seed)

To wipe the HuggingFace checkpoint and restart training from a fresh Qwen2.5-0.5B:

Run workflow with force_seed: true. This runs scripts/seed_hf_repo.py before training.

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Dataset Curriculum

Training data is a weighted mix of finance-focused HuggingFace datasets, streamed in real-time (no full downloads):

Dataset	Weight	Focus
gbharti/finance-alpaca	30%	Financial Q&A instructions
nvidia/OpenMathInstruct-2	25%	Math reasoning (critical for quantitative finance)
HuggingFaceFW/fineweb-edu	20%	General knowledge foundation
FinanceMTEB/financial_phrasebank	1%	Sentiment classification
FinanceMTEB/FinQA	1%	Financial QA pairs
FinanceMTEB/TATQA	1%	Table-and-text QA
FinanceMTEB/FOMC	0.8%	FOMC meeting transcripts
Various FinanceMTEB	~20%	Sentiment, ESG, fraud, FLS, events, and more

All text is formatted into the ### Instruction: / ### Response: template before tokenization.

See docs/training_pipeline.md for full dataset details.

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Repository Structure

FinAI/
├── meridian/
│   ├── model/
│   │   ├── configuration.py   # MeridianConfig (HF PretrainedConfig)
│   │   └── modeling.py        # Full model: RMSNorm, RoPE, GQA, SwiGLU, SMoE
│   ├── data/
│   │   └── pipeline.py        # Streaming dataset curriculum + DataLoader
│   └── training/
│       ├── trainer.py          # MeridianTrainer (AdaFactor, EWC, RAM guards)
│       └── ewc.py              # Elastic Weight Consolidation
│
├── scripts/
│   ├── seed_hf_repo.py         # Nuke & reseed HuggingFace repo
│   ├── evaluate_model.py       # Evaluation utilities
│   ├── hf_download_and_test.py # Download checkpoint and run test generation
│   └── count_params.py         # Parameter counting utility
│
├── docs/
│   ├── architecture.md         # Detailed architecture specification
│   ├── training_pipeline.md    # Pipeline, env vars, memory management
│   ├── setup_and_usage.md      # Setup guide and inference examples
│   └── examples/
│       ├── 01_inference.py     # HuggingFace inference example
│       ├── 02_dataset_pipeline.py  # Dataset streaming walkthrough
│       └── 03_model_config.py  # Direct architecture instantiation
│
├── tests/
│   ├── test_model.py           # Architecture unit tests
│   └── test_training.py        # Trainer and EWC tests
│
├── .github/
│   └── workflows/
│       ├── train.yml           # Hourly training CI
│       ├── lint.yml            # Ruff + Black linting
│       └── dependency-cache.yml
│
├── train.py                    # Main training entry point
├── requirements.txt            # Python dependencies
└── pyproject.toml              # Ruff + Black + mypy config

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Troubleshooting

trust_remote_code=True warning

Meridian.AI uses a custom model architecture registered with HuggingFace. Passing trust_remote_code=True is required when loading with AutoModelForCausalLM. This is safe — it executes code from the repo you explicitly specify.

OOM / RuntimeError: [enforce fail] during training

Reduce memory usage:

BATCH_SIZE=1 GRAD_ACCUM=8 BLOCK_SIZE=128 SOFT_RAM_GB=10.0 python train.py

Checkpoint architecture mismatch warning

If you see [WARN] Checkpoint architecture mismatch (old model), the saved config.json has model_type that doesn't match Qwen2/Llama. The checkpoint will be discarded and training restarts from the base model. This is expected when switching base architectures.

EWC shape mismatch warning

[WARN] EWC: Dropped N params due to shape/name mismatch appears when the model architecture changed between runs. EWC state for mismatched layers is safely dropped; valid parameters are kept. Training continues normally.

NaN loss

Usually caused by extreme learning rates or corrupted data batches. The trainer automatically skips batches with NaN loss or NaN gradients. If persistent, try:

LEARNING_RATE=1e-5 USE_EWC=0 python train.py

Slow training on CPU

Expected — these are CPU-only GitHub runners. With BATCH_SIZE=1 BLOCK_SIZE=256 MAX_STEPS=150, expect ~30–60 minutes per run. This fits the 90-minute CI timeout.

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Contributing

1. Run the test suite before submitting: pytest tests/ -v
2. Format with black: black .
3. Lint with ruff: ruff check . --fix
4. Keep all code pure Python — no unnecessary system dependencies
5. Submit PRs against main with clear commit messages following conventional commits format

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Disclaimer

Meridian.AI is an experimental research project on continual learning for financial NLP. All model outputs are strictly for academic and research purposes. Nothing generated by this model constitutes financial advice. Do not use outputs to make real financial decisions or execute trades.