How to use finetuned lora adapter in a huggingface-like pipeline

Hi, thanks for this amazing project. I was trying to finetune the lora model for Llama3.2 Vision which works fine and saved a adapter_0.pt; Then I wanted to use this adapter checkpoint for inference in a huggingface pipeline where i see some issues. I tried two ways as following and thanks in advance!

Weight format conversion: there is a file to convert meta (meta_model_0.pt) to tune, then to HF as in this _convert_weights.py. I tried to use it to convert a full model (non-lora), and it works for inference, but it does not work the adapter checkpoint as the parameters names are different.

Then I tried to set the _component_ parameter to torchtune.training.FullModelHFCheckpointer hoping to get a huggingface compatible model directly. Then I got the error: *** KeyError: 'num_attention_heads'.

File ".../lora_finetune_distributed.py", line 725, in save_checkpoint
self._checkpointer.save_checkpoint(
File ".../lib/python3.10/site-packages/torchtune/training/checkpointing/_checkpointer.py", line 639, in save_checkpoint
num_heads=self._config["num_attention_heads"],
KeyError: 'num_attention_heads'

What would be ideal is to have a way to use the finetuned model in a way similar to the following? Note the following code does not work as there is no adapter_config.json under the checkpointer/output_dir path.

model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"

# Replace the original model loading code with this:
# with torch.inference_mode():
model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    low_cpu_mem_usage=True,
)

from peft import PeftModel
peft_model = PeftModel.from_pretrained(model, "checkpointer/output_dir")

To reproduce what I observed:

I am using the following command to run, with slightly modified config file:

tune run --nnodes 1 --nproc_per_node 1 lora_finetune_distributed --config ./finetune-llama32/11B _lora_debug.yaml

Main modification:

from

_component_: torchtune.training.FullModelMetaCheckpointer
originally: checkpoint_files: [consolidated.pth]

  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /data/home/xxx/models/Llama-3.2-11B-Vision-Instruct/
  checkpoint_files: [model-00001-of-00005.safetensors,
                      model-00002-of-00005.safetensors,
                      model-00003-of-00005.safetensors,
                      model-00004-of-00005.safetensors,
                      model-00005-of-00005.safetensors ]

Full config file:

# Config for multi-device LoRA finetuning in lora_finetune_distributed.py
# using a Llama3.2 11B Vision Instruct model
#
# This config assumes that you've run the following command before launching:
#   tune download meta-llama/Llama-3.2-11B-Vision-Instruct --output-dir /tmp/Llama-3.2-11B-Vision-Instruct
#
# To launch on 2 devices, run the following command from root:
#   tune run --nproc_per_node 2 lora_finetune_distributed --config llama3_2_vision/11B_lora
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training:
#   tune run --nproc_per_node 2 lora_finetune_distributed --config llama3_2_vision/11B_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works best when the model is being fine-tuned on 2+ GPUs.
# For single device LoRA finetuning please use 11B_lora_single_device.yaml
# or 11B_qlora_single_device.yaml

# Model arguments
model:
  _component_: torchtune.models.llama3_2_vision.lora_llama3_2_vision_11b
  decoder_trainable: "frozen"
  encoder_trainable: "lora"
  fusion_trainable: "lora"
  lora_attn_modules: ['q_proj', 'v_proj']
  apply_lora_to_mlp: False
  apply_lora_to_output: False
  lora_rank: 8
  lora_alpha: 16
  lora_dropout: 0.0
  image_size: 560 # Make sure this matches the image_size in tokenizer

# Transform
tokenizer:
  _component_: torchtune.models.llama3_2_vision.llama3_2_vision_transform
  path: /data/home/xxx/models/Llama-3.2-11B-Vision-Instruct/original/tokenizer.model
  image_size: 560

# Checkpointer
checkpointer:
  # _component_: torchtune.training.FullModelMetaCheckpointer
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /data/home/xxx/models/Llama-3.2-11B-Vision-Instruct/
  checkpoint_files: [model-00001-of-00005.safetensors,
                      model-00002-of-00005.safetensors,
                      model-00003-of-00005.safetensors,
                      model-00004-of-00005.safetensors,
                      model-00005-of-00005.safetensors ]
  # originally: checkpoint_files: [consolidated.pth]
  recipe_checkpoint: null
  output_dir: /tmp/Llama-3.2-11B-Vision-Instruct/
  model_type: LLAMA3_VISION
resume_from_checkpoint: False

# Dataset
dataset:
  _component_: torchtune.datasets.multimodal.the_cauldron_dataset
  subset: ocrvqa
seed: null
shuffle: True
collate_fn: torchtune.data.padded_collate_tiled_images_and_mask

# Fine-tuning arguments
epochs: 1
max_steps_per_epoch: null
batch_size: 2
gradient_accumulation_steps: 4
optimizer:
  _component_: torch.optim.AdamW
  fused: True
  weight_decay: 0.01
  lr: 2e-5
lr_scheduler:
  _component_: torchtune.modules.get_cosine_schedule_with_warmup
  num_warmup_steps: 100
loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
clip_grad_norm: 1.0
compile: False # set it to True for better memory and performance

# Training env
device: cuda

# Memory management
enable_activation_checkpointing: True
enable_activation_offloading: False
dtype: bf16

# Logging
output_dir: /tmp/full-llama3.2-vision-finetune
log_peak_memory_stats: False
metric_logger:
  _component_: torchtune.training.metric_logging.WandBLogger
  project: llama3.2_lora_project
log_every_n_steps: 1

pytorch / torchtune

How to use finetuned lora adapter in a huggingface-like pipeline #1779