# Gemma 4 E2B Vision LoRA
#
# Fine-tuning LM LoRA adapters on multimodal Gemma4 with vision/multimodal modules frozen.
# Uses the base ProcessingStrategy (auto-detects image_token from processor).

base_model: google/gemma-4-E2B-it
processor_type: AutoProcessor
freeze_mm_modules: true

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

# Required for vision/multimodal training
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false

chat_template: gemma4
datasets:
  - path: HuggingFaceH4/llava-instruct-mix-vsft
    type: chat_template
    split: train[:100]

val_set_size: 0
output_dir: ./outputs/gemma4-e2b-vision-lora

adapter: lora
sequence_len: 2048
pad_to_sequence_len: false

lora_r: 16
lora_alpha: 32
lora_dropout: 0
# Target language model only — vision encoder is frozen via freeze_mm_modules
lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
max_steps: 10
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: auto
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1
sdp_attention: true

warmup_ratio: 0.1
weight_decay: 0.0

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model: