File size: 3,002 Bytes
442752a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import os
import torch
from PIL import Image
import matplotlib.pyplot as plt
from datasets import load_dataset
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration, BitsAndBytesConfig
from peft import PeftModel
# =========================
# CONFIG
# =========================
MODEL_NAME = "Qwen/Qwen2-VL-2B-Instruct"
CHECKPOINT_PATH = "./qlora-vlm" # your saved model
JSONL_PATH = "0508_clean.jsonl"
NUM_SAMPLES = 3 # show 3 samples
IMAGE_SIZE = 512
# =========================
# LOAD MODEL (IMPORTANT)
# =========================
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
processor = AutoProcessor.from_pretrained(MODEL_NAME)
base_model = Qwen2VLForConditionalGeneration.from_pretrained(
MODEL_NAME,
quantization_config=bnb_config,
device_map="auto",
)
model = PeftModel.from_pretrained(base_model, CHECKPOINT_PATH)
model.eval()
# =========================
# LOAD DATASET
# =========================
dataset = load_dataset("json", data_files=JSONL_PATH)
# =========================
# GENERATION FUNCTION
# =========================
def generate_markdown(image_path):
image = Image.open(image_path).convert("RGB")
image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
prompt = "Convert this document image into structured Markdown."
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": prompt}
]
}
]
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = processor(
text=text,
images=image,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=512,
do_sample=False
)
result = processor.decode(output[0], skip_special_tokens=True)
return result
# =========================
# VISUALIZATION FUNCTION
# =========================
def visualize_sample(sample, title="Sample"):
image_path = sample["image"]
gt_markdown = sample["markdown"]
pred_markdown = generate_markdown(image_path)
image = Image.open(image_path).convert("RGB")
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis("off")
plt.title(title)
plt.show()
print("\n" + "="*80)
print("GROUND TRUTH:\n")
print(gt_markdown[:2000])
print("\n" + "-"*80)
print("GENERATED:\n")
print(pred_markdown[:2000])
print("="*80)
# =========================
# RUN VISUALIZATION
# =========================
print("\n===== TRAIN SAMPLES =====\n")
for i in range(NUM_SAMPLES):
visualize_sample(dataset[i], title=f"Train Sample {i+1}") |