base_model: meta-llama/Llama-3.2-3B
# Automatically upload checkpoint and final model to HF
# 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

datasets:
  - path: yahma/alpaca-cleaned
    type: alpaca
    split: train[:95%]

output_dir: ./outputs/qat_out/
dataset_prepared_path: ./outputs/dataset_prepared

sequence_len: 8192
flash_attention: true

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

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_checkpointing: true
gradient_accumulation_steps: 1
micro_batch_size: 64
num_epochs: 1
optimizer: adamw_torch_fused

cosine_constant_lr_ratio: 0
cosine_min_lr_ratio: 1.0
learning_rate: 2e-5
save_only_model: true
bf16: true

resume_from_checkpoint:
logging_steps: 1

evals_per_epoch: 1
saves_per_epoch: 1

warmup_ratio: 0.1
weight_decay: 0.0

special_tokens:
  pad_token: <|finetune_right_pad_id|>

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