SBOT is a multi-layer LSTM conversational model designed for efficient CPU-based natural language understanding and generation.
SBOT is implemented as a deep recurrent neural network utilizing Long Short-Term Memory (LSTM) cells, structured in four stacked layers to effectively capture temporal dependencies and contextual information over sequential tokens. The model processes input sequences token-by-token, maintaining internal hidden states to preserve conversational context.
The model was trained on high-core-count CPU servers, focusing on efficient batching and parallelization to compensate for the lack of extensive GPU resources. Training leveraged truncated backpropagation through time (TBPTT) to mitigate vanishing gradient issues over long sequences. The Adam optimizer was employed with a decaying learning rate schedule to optimize convergence.
Minimal GPU acceleration was applied selectively for tensor operations where CPU performance was a bottleneck, allowing broad training accessibility on commodity hardware. Gradient clipping and layer normalization were utilized to stabilize training dynamics and reduce exploding gradients.
Training data comprised diverse conversational datasets, including multi-domain chat logs, scripted dialogue corpora, and publicly available dialogue datasets. Data preprocessing included normalization, tokenization using a custom vocabulary with subword units, and filtering to remove low-quality and duplicate samples. The dataset was shuffled and partitioned into training, validation, and test splits to ensure robust evaluation.
SBOT supports real-time inference with token-by-token autoregressive generation, maintaining an internal state buffer to enable coherent multi-turn dialogue. Its lightweight architecture enables deployment on CPU-constrained environments, suitable for edge devices and low-latency applications.
While effective in modeling local context, SBOT's recurrent design limits long-range dependency capture compared to transformer-based architectures. Future iterations plan to integrate attention mechanisms and transformer blocks to improve contextual awareness and scalability.
As SVECTOR's inaugural conversational model, SBOT laid the foundational framework for subsequent development of transformer-based models such as Spec-1-Mini. Its CPU-focused training paradigm underscores SVECTOR's commitment to democratizing AI development beyond GPU-dependent pipelines.
