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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
pip install pillow
# Tested on 8x H100 GPUs
accelerate launch
--config_file=examples/accelerate_configs/deepspeed_zero3.yaml \
sft_vlm_smol_vlm.py \
--dataset_name HuggingFaceH4/llava-instruct-mix-vsft \
--model_name_or_path HuggingFaceTB/SmolVLM-Instruct \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 1 \
--output_dir sft-smol-vlm-hf \
--bf16 \
--torch_dtype bfloat16 \
--gradient_checkpointing \
--use_peft \
--lora_target_modules down_proj, o_proj, k_proj, q_proj, gate_proj, up_proj, v_proj
For LLaVA-NeXT, use: (requires transformers>=4.45)
--model_name_or_path llava-hf/llava-v1.6-mistral-7b-hf
For meta-llama/Llama-3.2-11B-Vision-Instruct, use: (requires transformers>=4.45.1)
--model_name_or_path meta-llama/Llama-3.2-11B-Vision-Instruct
"""
import torch
from datasets import load_dataset
from transformers import (
AutoModelForVision2Seq,
AutoProcessor,
Idefics3ForConditionalGeneration,
LlavaForConditionalGeneration,
)
from trl import (
ModelConfig,
ScriptArguments,
SFTConfig,
SFTTrainer,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
training_args.gradient_checkpointing_kwargs = dict(use_reentrant=False)
training_args.remove_unused_columns = False
training_args.dataset_kwargs = {"skip_prepare_dataset": True}
################
# Model, Tokenizer & Processor
################
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
processor = AutoProcessor.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
model = AutoModelForVision2Seq.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
################
# Create a data collator to encode text and image pairs
################
def collate_fn(examples):
# Get the texts and images, and apply the chat template
texts = [processor.apply_chat_template(example["messages"], tokenize=False) for example in examples]
images = [example["images"] for example in examples]
if isinstance(model, LlavaForConditionalGeneration):
# LLava1.5 does not support multiple images
images = [image[0] for image in images]
# Tokenize the texts and process the images
batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
# The labels are the input_ids, and we mask the padding tokens in the loss computation
labels = batch["input_ids"].clone()
labels[labels == processor.tokenizer.pad_token_id] = -100 #
# Ignore the image token index in the loss computation (model specific)
if isinstance(model, Idefics3ForConditionalGeneration):
image_token_id = processor.tokenizer.additional_special_tokens_ids[
processor.tokenizer.additional_special_tokens.index("<image>")
]
else:
image_token_id = processor.tokenizer.convert_tokens_to_ids(processor.image_token)
labels[labels == image_token_id] = -100
batch["labels"] = labels
return batch
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
################
# Training
################
trainer = SFTTrainer(
model=model,
args=training_args,
data_collator=collate_fn,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=processor.tokenizer,
peft_config=get_peft_config(model_args),
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
if trainer.accelerator.is_main_process:
processor.push_to_hub(training_args.hub_model_id)
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