Introduction

This tutorial guides you through the process of performing inference and fine-tuning with the quantized version of the IDEFICS-9B model using an NVIDIA A100 GPU. The IDEFICS model is a visual language model that processes sequences of images and text to generate coherent textual outputs. We will show how to enhance the model’s performance in specific tasks through techniques like LoRA (Low-Rank Adaptation).

Prerequisites for Fine-Tuning IDEFICS 9B on A100

Before you begin, ensure you have the necessary resources:

• Hardware Requirements: An NVIDIA A100 GPU with at least 40GB VRAM.
• Software Setup:
  • Python 3.8+
  • PyTorch with CUDA support
  • Hugging Face Transformers & Datasets libraries
• Dataset: A prepared multimodal dataset with text-image pairs in Hugging Face-compatible format.
• Basic Knowledge: Familiarity with LLM fine-tuning and multimodal architectures.
• Storage & Compute: At least 500GB of storage for model weights and datasets, plus a cloud or local environment for distributed training if necessary.

What is IDEFICS?

IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is a visual language model developed from DeepMind’s Flamingo and is capable of generating text-based responses to images. It can understand and describe visual content, answer questions about images, and perform basic arithmetic operations. This model comes in two sizes: with 9 billion and 80 billion parameters.

What is Fine-Tuning?

Fine-tuning adjusts a pre-trained model for a specific task, utilizing a smaller dataset to slightly modify the model without losing its original knowledge. This process is much more efficient than training a model from scratch and improves the model’s performance for targeted applications.

For this tutorial, we will use the “TheFusion21/PokemonCards” dataset for fine-tuning the model.

Installation

To start, install the required packages in a Jupyter Notebook environment:

!pip install -q datasets!pip install -q git+https://github.com/huggingface/transformers.git!pip install -q bitsandbytes sentencepiece accelerate loralib!pip install -q -U git+https://github.com/huggingface/peft.git!pip install accelerate==0.27.2

After installing the necessary libraries, import them into your notebook:

import torchfrom datasets import load_datasetfrom peft import LoraConfig, get_peft_modelfrom PIL import Imagefrom transformers import IdeficsForVisionText2Text, AutoProcessor, Trainer, TrainingArgumentsimport torchvision.transforms as transforms

Load the Quantized Model

Next, set up your environment to load the quantized model:

device = "cuda" if torch.cuda.is_available() else "cpu"checkpoint = "HuggingFaceM4/idefics-9b"bnb_config = BitsAndBytesConfig(    load_in_4bit=True,    bnb_4bit_use_double_quant=True,    bnb_4bit_quant_type="nf4",    bnb_4bit_compute_dtype=torch.float16,    llm_int8_skip_modules=["lm_head", "embed_tokens"],)processor = AutoProcessor.from_pretrained(checkpoint, use_auth_token=True)model = IdeficsForVisionText2Text.from_pretrained(checkpoint, quantization_config=bnb_config, device_map="auto")

Inference

Once the model is loaded, you can perform inference with it:

def model_inference(model, processor, prompts, max_new_tokens=50):    tokenizer = processor.tokenizer    bad_words = ["<image>", "<fake_token_around_image>"]    if len(bad_words) > 0:        bad_words_ids = tokenizer(bad_words, add_special_tokens=False).input_ids    eos_token = "</s>"    eos_token_id = tokenizer.convert_tokens_to_ids(eos_token)    inputs = processor(prompts, return_tensors="pt").to(device)    generated_ids = model.generate(        **inputs,        eos_token_id=[eos_token_id],        bad_words_ids=bad_words_ids,        max_new_tokens=max_new_tokens,        early_stopping=True,    )    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]    print(generated_text)url = "https://hips.hearstapps.com/hmg-prod/images/dog-puppy-on-garden-royalty-free-image-1586966191.jpg?crop=0.752xw:1.00xh;0.175xw,0&resize=1200:*"prompts = [url, "Question: What's on the picture? Answer:"]model_inference(model, processor, prompts, max_new_tokens=5)

Prepare the Dataset for Fine-Tuning

To fine-tune your model, you will need to prepare your dataset:

def convert_to_rgb(image):    if image.mode == "RGB":        return image    image_rgba = image.convert("RGBA")    background = Image.new("RGBA", image_rgba.size, (255, 255, 255))    alpha_composite = Image.alpha_composite(background, image_rgba)    alpha_composite = alpha_composite.convert("RGB")    return alpha_compositedef ds_transforms(example_batch):    # setup image transformations    image_size = processor.image_processor.image_size    image_mean = processor.image_processor.image_mean    image_std = processor.image_processor.image_std    image_transform = transforms.Compose([        convert_to_rgb,        transforms.RandomResizedCrop((image_size, image_size), scale=(0.9, 1.0)),        transforms.ToTensor(),        transforms.Normalize(mean=image_mean, std=image_std),    ])    prompts = []    for i in range(len(example_batch['caption'])):        caption = example_batch['caption'][i].split(".")[0]        prompts.append([            example_batch['image_url'][i],            f"Question: What's on the picture? Answer: This is {example_batch['name'][i]}. {caption}.",        ])        inputs = processor(prompts, return_tensors="pt").to(device)    inputs["labels"] = inputs["input_ids"]    return inputsds = load_dataset("TheFusion21/PokemonCards")ds = ds["train"].train_test_split(test_size=0.002)train_ds = ds["train"]eval_ds = ds["test"]train_ds.set_transform(ds_transforms)eval_ds.set_transform(ds_transforms)

Fine-Tuning with LoRA

Now apply Low-Rank Adaptation to your model:

config = LoraConfig(    r=16,    lora_alpha=32,    target_modules=["q_proj", "k_proj", "v_proj"],    lora_dropout=0.05,    bias="none",)model = get_peft_model(model, config)

Start Training

With everything set, begin the training process:

training_args = TrainingArguments(    output_dir=f"{checkpoint.split('/')[-1]}-pokemon",    learning_rate=2e-4,    fp16=True,    per_device_train_batch_size=2,    per_device_eval_batch_size=2,    gradient_accumulation_steps=8,    save_total_limit=3,    evaluation_strategy="steps",    save_strategy="steps",    save_steps=40,    eval_steps=20,    logging_steps=20,    max_steps=20,    load_best_model_at_end=True,)trainer = Trainer(    model=model,    args=training_args,    train_dataset=train_ds,    eval_dataset=eval_ds,)trainer.train()

Conclusion

In this tutorial, we successfully fine-tuned the IDEFICS-9B model using the Pokemon dataset, enabling it to perform inference tasks with improved accuracy. Fine-tuning requires balancing computational resources and strategies to achieve optimal performance. The NVIDIA A100’s speed and memory capabilities facilitate effective multimodal model training, providing a robust solution for integrating image and text data for diverse applications.

Feel free to explore further applications using this approach!

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