HuggingFaceM4/idefics2-8b · Having issue for doing inference after fine tuning idefics2 using LoRA

Hi,
I am trying to do inference using

from transformers import AutoProcessor, BitsAndBytesConfig, Idefics2ForConditionalGeneration
import torch

peft_model_id = "idefics2-finetuned"

processor = AutoProcessor.from_pretrained(peft_model_id)

# Define quantization config
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16
)
# Load the base model with adapters on top
model = Idefics2ForConditionalGeneration.from_pretrained(
    peft_model_id,
    torch_dtype=torch.float16,
    quantization_config=quantization_config,
)
def predict(test_example):
    test_image = test_example["image"]
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Extract JSON."},
                {"type": "image"},
            ]
        },
    ]
    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)

    inputs = processor(text=prompt, images=[test_image], return_tensors="pt").to("cuda")

    # Generate token IDs
    generated_ids = model.generate(**inputs, max_new_tokens=2048, temperature = 0.1,top_p = 0.95, do_sample = True)

    # Decode back into text
    generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
    generated_json = token2json(generated_texts[0])
    return generated_json

But the inference speed is really slow, I tried to do model.merge_and_unload() to reduce the latency issue, to do so, I need to do the following first

if USE_QLORA or USE_LORA:
  lora_config = LoraConfig(
          r=8,
          lora_alpha=8,
          lora_dropout=0.1,
          target_modules=".*(text_model|modality_projection|perceiver_resampler).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$",
          use_dora=False if USE_QLORA else True,
          init_lora_weights="gaussian",
      )


  model = prepare_model_for_kbit_training(model)
  model = get_peft_model(model, lora_config)

But I got error

ValueError: Target module ModuleDict(
  (default): Dropout(p=0.1, inplace=False)
) is not supported. Currently, only the following modules are supported: `torch.nn.Linear`, `torch.nn.Embedding`, `torch.nn.Conv2d`, `transformers.pytorch_utils.Conv1D`.

So I am wondering if you have the same issue before.