Reduces the default LoRA rank in training configuration templates from 16 to 8.
This change aims to optimize resource usage and potentially accelerate training.
Increases LoRA rank from 4 to 16 in example and default configurations.
This aims to improve model fine-tuning effectiveness.
Adjusts per-device batch size and gradient accumulation steps. This
maintains the same effective batch size while potentially reducing
memory usage.
Standardizes LoRA dropout to 0.25 across configurations.
Updates READMEs to clarify model performance expectations.
Adds support for processing stickers in Telegram chat data.
- Includes 'sticker' as a valid DataModality.
- Allows the option to include sticker emojis as message content.
xming521