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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# Configuration for fine-tuning ministral 3 14b



# To run this recipe:
#   automodel examples/vlm_finetune/mistral/ministral3_14b_medpix.yaml --nproc-per-node 8
# Adjust --nproc-per-node to the number of GPUs available on your machine.

recipe: FinetuneRecipeForVLM

step_scheduler:
  global_batch_size: 8
  local_batch_size: 1
  ckpt_every_steps: 100
  val_every_steps: 100  # will run every x number of gradient steps
  num_epochs: 1

dist_env:
  backend: nccl
  timeout_minutes: 10

rng:
  _target_: nemo_automodel.components.training.rng.StatefulRNG
  seed: 42
  ranked: true

model:
  _target_: nemo_automodel.NeMoAutoModelForImageTextToText.from_pretrained
  pretrained_model_name_or_path: mistralai/Ministral-3-14B-Reasoning-2512
  torch_dtype: torch.bfloat16
  attn_implementation: eager

checkpoint:
  enabled: true
  checkpoint_dir: vlm_checkpoints/
  model_save_format: safetensors
  save_consolidated: True

distributed:
  strategy: fsdp2
  dp_size: none
  tp_size: 1
  cp_size: 1

  sequence_parallel: false

loss_fn:
  _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy

dataset:
  _target_: nemo_automodel.components.datasets.vlm.datasets.make_medpix_dataset
  path_or_dataset: mmoukouba/MedPix-VQA
  split: train[:1000]

dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  num_workers: 0
  collate_fn:
    _target_: nemo_automodel.components.datasets.vlm.collate_fns.default_collate_fn

validation_dataset:
  _target_: nemo_automodel.components.datasets.vlm.datasets.make_medpix_dataset
  path_or_dataset: mmoukouba/MedPix-VQA
  split: validation[:500]

validation_dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  num_workers: 0
  collate_fn:
    _target_: nemo_automodel.components.datasets.vlm.collate_fns.default_collate_fn
  
optimizer:
  _target_: torch.optim.AdamW
  lr: 1e-5
  weight_decay: 0.01
  betas: [0.9, 0.95]

freeze_config:
  freeze_embeddings: true
  freeze_vision_tower: true
  freeze_language_model: false
  
# Uncomment and configure for W&B logging
#  wandb:
#   project:
#   entity:
#   name:
#   save_dir:

ci:
  recipe_owner: akoumpa
