curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git-lfs clone https://huggingface.co/chio4696/Ovis-2.5-SFT-2733

python

import torch
import requests
from PIL import Image
from transformers import AutoModelForCausalLM
import pandas as pd
import os

IMAGE_PATH = 'AI-Chellenge-Ovis2_5/data/image/'

model = AutoModelForCausalLM.from_pretrained(
    "Ovis-2.5-SFT-2733/converted/default",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
).cuda()

def inference_on_data(row):
    image_path = ""
    task = row['task']
    question = row['question']
    if task == 'captioning':
        text = 'Generate a single, detailed, and objective descriptive paragraph for the given image. Each description must begin with the phrase "The image is..." or "The image shows...", followed by a structured analysis that moves from the main subject to its details, and then to the background elements. You must use positional language, such as "on the left" or "at the top of the cover" to clearly orient the reader. If any text is visible in the image, transcribe it exactly and describe its visual characteristics like color and style. Conclude the entire description with a sentence that summarizes the overall atmosphere of the image, using a phrase like "The overall mood of the image is...". Throughout the paragraph, maintain a strictly factual, declarative tone with specific, descriptive vocabulary, avoiding any personal opinions or interpretations.'
        image_path = os.path.join(IMAGE_PATH, row['input'])
    elif task == 'vqa':
        text = f'Given a document image and a question, extract the precise answer. Your response must be only the literal text found in the image, with no extra words or explanation.\n\nQuestion: {question}'
        image_path = os.path.join(IMAGE_PATH, row['input'])
    elif task == 'summarization':
        text = f"Generate a summary of the following legislative text. Start with the bill's official title, then state its primary purpose and key provisions. Use formal, objective language and focus on the actions the bill takes, such as what it amends, requires, prohibits, or establishes.\n\nText: {row['input']}"
    elif task == 'text_qa':
        text = f"Given a context and a question, extract the most concise, direct answer from the text. Your answer should be a short phrase, not a complete sentence.\n\nContext: {row['input']}\n\nQuestion: {question}"
    elif task == 'math_reasoning':
        text = f"Given a math word problem, solve the question by generating a step-by-step reasoning process. After detailing all the steps in your reasoning, you must conclude your response by placing the final numerical answer on its own separate line, prefixed with #### .\n\nQuestion: {row['input']}"
    messages = [{
        "role": "user",
        "content": [
            {"type": "image", "image": Image.open(image_path) if image_path else None},
            {"type": "text", "text": text},
        ],
    }]
    
    input_ids, pixel_values, grid_thws = model.preprocess_inputs(
        messages=messages,
        add_generation_prompt=True,
        enable_thinking=True
    )
    input_ids = input_ids.cuda()
    pixel_values = pixel_values.cuda() if pixel_values is not None else None
    grid_thws = grid_thws.cuda() if grid_thws is not None else None
    
    outputs = model.generate(
        inputs=input_ids,
        pixel_values=pixel_values,
        grid_thws=grid_thws,
        enable_thinking=True,
        enable_thinking_budget=True,
        max_new_tokens=8192,
        thinking_budget=4096,
    )
    
    return model.text_tokenizer.decode(outputs[0], skip_special_tokens=True), text

test_df = pd.read_parquet("AI-Chellenge-Ovis2_5/data/converted/deep_chal_multitask_dataset_test_path_converted.parquet") # Should be able to feed the function with local image path
test_df = test_df.groupby('task').head(5) # Simple task-stratified samples

inference_result = dict()
for _, row in test_df.iterrows():
    y, x = inference_on_data(row)
    inference_result[x] = y
    
inference_df = pd.DataFrame(inference_result.items(), columns=['In', 'Out'])

Compute Infrastructure

Hardware

OS: Ubuntu 22.04.4 LTS
Processor: 16 vCore
Memory: 128 GiB
Accelerator: 1x NVIDIA A100 80GB PCIe

Environmental Impact

Hardware Type: 1x NVIDIA A100 80GB PCIe
Hours used: 8.2 hours
Carbon Emitted: Based on the hardware and training time, an estimated carbon emission is 1.15 kg of CO₂eq, which is equivalent to: 4.65 km driven by an average ICE car, 0.58 kgs of coal burned, and 0.02 tree seedlings sequesting carbon for 10 years. Machine Learning Impact calculator

Software

Python 3.10.14
Training was managed using the MS-Swift framework.