base_model: google/gemma-3-12b-it
# Math finetuning configuration for Gemma3-12B
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
seed: 42
chat_template: gemma3
datasets:
  - path: AI-MO/NuminaMath-CoT
    type: chat_template

output_dir: ./outputs/qat_out_math_gemma/

sequence_len: 4096
sample_packing: true
flash_attention: true

qat:
  activation_dtype: nvfp4
  weight_dtype: nvfp4
  group_size: 16 # only group_size of 16 is supported with nvfp4

wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 8

num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 3e-5

bf16: true
tf32: true

resume_from_checkpoint:
logging_steps: 1

# evals_per_epoch: 1
saves_per_epoch: 1

warmup_ratio: 0.1
weight_decay: 0.0
fsdp_version: 2

fsdp_config:
  offload_params: false
  cpu_ram_efficient_loading: true
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: Gemma3DecoderLayer
  state_dict_type: FULL_STATE_DICT
  sharding_strategy: FULL_SHARD
  reshard_after_forward: true
  activation_checkpointing: true

special_tokens:

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config
