base_model: google/gemma-4-31B

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
  - axolotl.integrations.liger.LigerPlugin
torch_compile: false
liger_layer_norm: true
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_rms_norm_gated: true
strict: false

chat_template: gemma4
datasets:
  - path: mlabonne/FineTome-100k
    type: chat_template
    split: train[:10%]
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
val_set_size: 0.05
output_dir: ./outputs/gemma4-31b-qlora

sequence_len: 2048
sample_packing: true

load_in_4bit: true
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0

# Restrict LoRA to text backbone only (skip vision/audio encoders)
# using regex to match only the text decoder attention projections.
lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'

bnb_config_kwargs:
  bnb_4bit_use_double_quant: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

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

bf16: auto
tf32: true

gradient_checkpointing: true
activation_offloading: true
logging_steps: 1

# FA not supported
sdp_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens: