sztyberj/IncunabuLM-111M

IncunabuLM

Model Description

IncunabuLM is a decoder-only transformer language model designed for text generation tasks. The model implements a custom architecture with RMSNorm normalization and modern attention mechanisms, optimized for resource-efficient training and inference.

Model Details

Model Type

• Architecture: Decoder-only Transformer
• Language(s): Primarily trained on Polish text (lectures)
• Model size: 111.7M parameters

Source and Base Model

• Base Architecture: Custom implementation inspired by modern transformer architectures
• Training Approach: Trained from scratch on Polish text corpus
• Educational Source: Implementation follows principles from Andrej Karpathy's "Let's build GPT: from scratch, in code, spelled out" tutorial
• Tutorial Reference: https://www.youtube.com/watch?v=kCc8FmEb1nY
• Custom Modifications:
  • RMSNorm instead of LayerNorm for improved stability
  • SiLU activation functions in feed-forward networks
  • Optimized for resource-efficient training and inference

Architecture Details

• Layers: 12 transformer blocks
• Hidden size: 768
• Attention heads: 12
• Head dimension: 64
• Context length: 2048 tokens
• Vocabulary size: 16,384 tokens
• Normalization: RMSNorm (Root Mean Square Layer Normalization)
• Activation: SiLU (Swish) in feed-forward networks
• Attention: Causal self-attention with triangular masking

Key Features

• RMSNorm: More stable and efficient than LayerNorm
• SiLU Activation: Better gradient flow than ReLU
• BPE Tokenization: Byte-level BPE with 16K vocabulary
• Mixed Precision: Support for bfloat16/float16 training
• Generation Controls: Temperature, top-k sampling, repetition penalty

Training Details

Training Data

• Dataset: Custom Polish text corpus
• Preprocessing: Byte-level BPE tokenization
• Split: 90% training, 10% validation

Training Configuration

• Batch size: 8 (with gradient accumulation steps: 8)
• Effective batch size: 64
• Context length: 2048 tokens
• Training steps: 50,000
• Optimizer: AdamW
• Learning rate: 3e-4 (peak)
• Learning rate schedule: Cosine with linear warmup
• Warmup steps: 2,000
• Weight decay: 0.1
• Gradient clipping: 1.0
• Dropout: 0.2

Training Infrastructure

• Hardware: 1x Nvidia A100 80Gb
• Precision: Mixed precision (bfloat16/float16)
• Gradient scaling: Automatic mixed precision with GradScaler

Performance

Model Size and Efficiency

• Parameters: 111.7M (111,718,144 total parameters)
• Context window: 2048 tokens
• Inference speed: Optimized for single-GPU inference

Training Metrics

• Final training loss: [4.5544]
• Final validation loss: [4.7100]
• Training time: [~8h]

Limitations and Biases

Known Limitations

1. Context Length: Limited to 2048 tokens, may struggle with very long documents
2. Language Scope: Primarily designed for Polish text, may have reduced performance on other languages
3. Model Size: At 111M parameters, may have limited knowledge compared to larger models
4. Training Data: Performance heavily dependent on training corpus quality and diversity

Potential Biases

• Language Bias: Optimized for Polish language patterns
• Domain Bias: Reflects the domain distribution of training data
• Temporal Bias: Training data cutoff affects knowledge of recent events
• Cultural Bias: May reflect cultural perspectives present in training data

Technical Specifications

Hardware Recommendations

• Minimum: 4GB GPU memory for inference
• CPU: Compatible but significantly slower

Jakub Sztyber