exp_name: 'vi-en-fix-v1'

# Training dataset (from Huggingface)
# data_source: "MedCat/MedCAT-SFT-v1"
data_source: "MedCat/MedCAT-SFT-v1.1"

# The base model (from HuggingFace model hub)
# model_name: "Qwen/Qwen2.5-0.5B"
model_name: "MedCat/MedCAT-PT-Qwen2.5-0.5B-v1-stream-data-v1-checkpoint-600000"
# model_name: "MedCat/MedCAT-PT-Apollo-0.5B-v1-stream-data-v1-checkpoint-600000"

# Tokenizer
tokenizer_batch_size: 1_000
max_length: 512

# Checkpoints configuration
output_folder: "./checkpoints/MedCAT-SFT"  # Where to save checkpoints during the training
save_total_limit: 2  # Limit on number of checkpoints to keep
save_strategy: "steps"  # Saving strategy (either 'steps' or 'epoch')
save_steps: 500  # Save model every ... steps

# LoRA
r: 8  # Rank of the low-rank matrices
lora_alpha: 32  # LoRA alpha
lora_dropout: 0.1  # Dropout rate
bias: "none"  # Whether to train biases ("none", "all", or "lora_only")
task_type: "CAUSAL_LM"  # Task type: casual language modeling

# Logging configuration
logging_dir: "./logs/MedCAT-SFT"  # Directory for logs + base_model + data_version
logging_steps: 100  # Frequency of logging

# Training configuration
per_device_train_batch_size: 4  # Training batch size
per_device_eval_batch_size: 4  # Evaluation batch size
num_train_epochs: 2  # Number of epochs
# max_steps: 500  # Total training steps (or use num_train_epochs instead)
eval_steps: 500  # Frequency of evaluation. Should equal to logging_steps (can be different, but should be equal)
evaluation_strategy: "steps"  # Evaluation strategy (either 'steps' or 'epoch')
seed: 3407  # Random seed for reproducibility
gradient_accumulation_steps: 8
learning_rate: 0.00001