FinAI-Next: Frontier Liquid-BitNet Architecture

FinAI-Next is a professional-grade, 331M parameter Large Language Model (LLM) engineered for high-efficiency financial reasoning and long-context processing. The architecture integrates BitNet b1.58 ternary quantization with Liquid Dynamical Systems, enabling frontier-class performance on standard consumer hardware.

Core Architectural Innovations

1. Liquid-BitNet Sequence Modeling

FinAI-Next utilizes Liquid Dynamical 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.
• fin_ai/training/: Specialized TernaryTrainer for managing high-precision master weights and low-bit gradients.
• train.py: Primary training interface with integrated state persistence and checkpointing.
• .github/workflows/: Continuous evolution pipeline for scheduled hourly training.

Automated Training Pipeline

FinAI-Next is designed for continuous parameter evolution. The automated GitHub Actions pipeline performs the following tasks every hour:

1. Retrieval of the most recent model weights from the Hugging Face repository.
2. Iterative training on a specific slice of the Fineweb-Edu dataset.
3. Automated synchronization of updated weights back to Hugging Face.
4. Persistence of dataset iteration state in dataset_state.json.

Technical Specifications

• Parameters: 331,296,816 (Effective)
• Hidden Dimensions: 1536
• Network Depth: 24 Layers
• State Dimension: 384
• Vocabulary: 151,665 (Qwen2.5 optimized)
• Precision: 1.58-bit (Ternary Weights)

Performance Monitoring

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

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