FinAI-Next: Liquid-BitNet Architecture

FinAI-Next is a professional-grade, 518M parameter Large Language Model engineered for high-efficiency financial reasoning and long-context processing. The architecture integrates BitNet b1.58 ternary quantization with Liquid Neural Networks, enabling frontier-class performance on standard consumer hardware.

Core Architectural Innovations

1. Liquid-BitNet Sequence Modeling

FinAI-Next utilizes Liquid Neural Network blocks to address the quadratic complexity of traditional Transformer architectures.

• Linear Complexity (O(n)): Computational requirements scale linearly with sequence length, facilitating native support for context windows exceeding 32k tokens.
• Stateful Recurrence: Adaptive dynamical systems evolve the internal hidden state, preserving long-range dependencies without the memory overhead of a KV-cache.

2. Ternary Quantization (BitNet b1.58)

The model employs native ternary weights ({-1, 0, 1}), significantly reducing the computational footprint.

• Multiplication-Free Inference: High-precision operations are replaced with efficient additions and subtractions.
• Hardware Optimization: Primary design focus on CPU execution, ensuring high-speed inference on standard desktop and mobile processors.

3. Adaptive Compute and Multimodal Integration

• Dynamic Depth: Implements token-wise confidence gating to skip layers during low-complexity processing, reducing latency by up to 40%.
• Multimodal Projectors: Unified architectural support for Vision and Audio feature mapping into the core latent space.

Project Structure

fin_ai/
├── model/          # Neural engine implementation including BitNet and LiquidBlock modules
│   ├── configuration_next.py
│   └── modeling_next.py
├── training/       # Specialized TernaryTrainer for progressive training
│   └── next_trainer.py
├── utils/          # Utility functions
└── __init__.py

train.py            # Primary training interface with integrated state persistence
generate.py         # Text generation script
checkpoint/         # Training checkpoints and model weights
scripts/            # Utility scripts for model management
tests/              # Unit tests

Training Pipeline

The training system supports progressive checkpointing with decreasing loss:

1. Initial Training: Model starts with higher loss (e.g., 111.924)
2. Progressive Runs: Each subsequent run loads from the previous checkpoint
3. Loss Convergence: Loss decreases progressively (e.g., 111.924 -> 10 -> 9 -> ...)
4. State Persistence: Dataset position, optimizer state, and scheduler state are saved

Training Configuration

# Configuration in train.py
config = FinAINextConfig(
    vocab_size=151665,
    hidden_size=1536,
    num_layers=24,
    liquid_state_dim=384,
    gradient_checkpointing=True,
    tie_word_embeddings=False,  # Set to false for checkpoint compatibility
)

Running Training

# Set environment variables
export MAX_STEPS=200        # Steps per training run
export TOTAL_STEPS=100000    # Total training lifecycle

# Run training
python train.py

Technical Specifications

Parameter	Value
Parameters	518,137,368
Hidden Dimensions	1536
Network Depth	24 Layers
State Dimension	384
Vocabulary	151,665 (Qwen2.5 optimized)
Precision	1.58-bit (Ternary Weights)
Max Context	32,768 tokens

Performance Monitoring

Comprehensive training metrics, including loss convergence and learning rate schedules, are tracked via Comet ML.

Access the Monitoring Dashboard

Environment Variables

Variable	Description
COMET_API_KEY	API key for Comet ML tracking
HF_TOKEN	Hugging Face authentication token
MAX_STEPS	Steps per training run
TOTAL_STEPS	Total steps across all runs

Dependencies

Key dependencies are listed in requirements.txt:

• torch
• transformers
• datasets
• accelerate
• comet_ml
• safetensors
• tqdm

Development Notes

Code formatted via AI-assisted development workflows for consistency and maintainability.

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