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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- vi |
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- en |
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- zh |
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base_model: |
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- OpenGVLab/InternViT-300M-448px |
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- Qwen/Qwen2.5-3B-Instruct |
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pipeline_tag: visual-question-answering |
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--- |
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<div align="center"> |
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<img src="Vintern3B-logo.jpg" width="700"/> |
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</div> |
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## Vintern-3B-beta 🇻🇳 ❄️ - The LLaVA 🌋 Challenger |
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**What's new in Vintern-3B-beta!** |
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- **We successfully reproduced the training process of InternVL from scratch.** |
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- The model is the result of integrating [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) and [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) through an MLP layer. |
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- Trained with more than 10 Milion Vietnamese QnAs, Descriptions, and 10% English Data from [OpenGVLab/InternVL-Chat-V1-2-SFT-Data](https://huggingface.co/datasets/OpenGVLab/InternVL-Chat-V1-2-SFT-Data). |
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## Model Details |
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| Model Name | Vision Part | Language Part | |
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| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | |
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| Vintern-3B-beta | [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) | [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) | |
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## Bytedance/MTVQA Benchmark |
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We surpassed GPT-4o and are approaching Gemini 1.5 Pro on the MTVQA dataset for Vietnamese. |
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The benchmark result in [MTVQA](https://github.com/bytedance/MTVQA/tree/main) from [open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard). |
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| Rank | Method | Param (B) | Language Model | Vision Model | VI | |
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|:----:|:----------------------------|:---------:|:---------------------------|:---------------------:|:------:| |
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| 1 | Gemini-1.5-Pro | | | | 41.3 | |
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| 2 | **Vintern-3B-beta** | **3** | **Qwen2.5-3B-Instruct** | **InternViT-300M** | **41.289** | |
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| 3 | GPT-4o (0513, detail-h...) | | | | 39.6 | |
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| 4 | GPT-4o (0806, detail-h...) | | | | 38.9 | |
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| 5 | Gemini-1.5-Flash | | | | 38.9 | |
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| 6 | Qwen-VL-Max-0809 | 72 | Qwen2-72B | ViT-600M | 36.9 | |
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| 7 | GPT-4o (0513, detail-lo...) | | | | 26.1 | |
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| 8 | Qwen-VL-Plus-0809 | | | | 27.8 | |
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| 9 | GLM-4v-9B | 9 | GLM-4-9B | EVA-02-5B | 26.6 | |
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| 10 | InternVL2-Llama3-76B | 76 | Llama-3-70B-Instruct | InternViT-6B | 26.7 | |
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| 11 | Step-1.5V | | Step-1.5 | stepencoder | 18.4 | |
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| 12 | InternVL2-40B | 40 | Nous-Hermes-2-Yi-34B | InternViT-6B | 21.2 | |
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| 13 | Pixtral-12B | 13 | Nemo-12B | ViT-400M | 19.7 | |
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## Zalo VMLU Benchmark |
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The Vintern-3B-beta achieved a score of **54.81** on the Zalo VMLU Benchmark. |
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<div align="center"> |
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<img src="vmlu_score.png" width="700"/> |
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</div> |
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``` |
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generation_config = dict(max_new_tokens= 64, do_sample=False, num_beams = 1, repetition_penalty=1.5) |
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question = "Bạn là trợ lý AI giải trắc nghiệm rất chính xác. Bạn biết chắc chắn đáp án đúng nhất. Chỉ đưa ra chữ cái đứng trước câu trả lời đúng của câu hỏi trắc nghiệm sau: Các cơ quan nào sau đây là cơ quan tư pháp? Lựa Chọn:\nA. Viện kiểm sát nhân dân\nB. Tòa án nhân dân\nC. Chính phủ\nD. Cả A và B\nCâu trả lời đúng nhất là:" |
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model.chat(tokenizer, None, question, generation_config) |
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``` |
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## OpenCompass Benchmark |
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<div align="center"> |
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<img src="radar_chart.png" width="400"/> |
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</div> |
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We are creating a pull request for the OpenCompass team to test once more and make the metrics public on the [open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard). |
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The current results are at a quite good level, and we are expanding the training set in English and other languages to approach models within a comparable parameter range. |
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"The table is referenced from the repo [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)." |
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| Benchmark | InternVL2-2B | MiniCPM-V 2.0 | Qwen2-VL-2B | Vintern-3B-beta | |
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|:-----------------|:------------:|:-------------:|:-----------:|:---------------:| |
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| MMMUval | 36.3 | 38.2 | 41.1 | 43.55 | |
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| DocVQAtest | 86.9 | - | 90.1 | 80.47 | |
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| InfoVQAtest | 58.9 | - | 65.5 | 48.28 | |
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| ChartQAtest | 76.2 | - | 73.5 | 68.32 | |
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| TextVQAval | 73.4 | - | 79.7 | 67.09 | |
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| OCRBench | 781 | 605 | 794 | 619 | |
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| MTVQA | 10.9 | 8.8 | 20.0 | 23.58 | |
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| Vi-MTVQA | 9.3 | 8.4 | - | 41.29 | |
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| RealWorldQA | 57.3 | 55.8 | 62.9 | 57.9 | |
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| MMEsum | 1876.8 | 1808.6 | 1872.0 | 1772.9 | |
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| MMBench-ENtest | 73.2 | 69.1 | 74.9 | 70.62 | |
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| MMStar | 49.8 | 39.1 | 48.0 | 47.6 | |
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| HallBenchavg | 38.0 | 36.1 | 41.7 | 43.22 | |
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| MathVistatestmini| 46.0 | 39.8 | 43.0 | 43.9 | |
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## Examples |
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<div align="center"> |
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<img src="ex_1.jpg" width="400"/> |
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</div> |
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``` |
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User: <image> |
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Liệt kê toàn bộ bài thơ có trong ảnh. |
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Assistant: Đi khắp thế gian không ai tốt bằng mẹ Gánh nặng cuộc đời không ai khổ bằng cha |
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``` |
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## Quickstart |
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Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents. |
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To run inference using the model, follow the steps outlined in our Colab inference notebook |
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```python |
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import numpy as np |
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import torch |
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import torchvision.transforms as T |
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# from decord import VideoReader, cpu |
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from PIL import Image |
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from torchvision.transforms.functional import InterpolationMode |
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from transformers import AutoModel, AutoTokenizer |
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IMAGENET_MEAN = (0.485, 0.456, 0.406) |
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IMAGENET_STD = (0.229, 0.224, 0.225) |
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def build_transform(input_size): |
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD |
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transform = T.Compose([ |
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), |
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), |
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T.ToTensor(), |
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T.Normalize(mean=MEAN, std=STD) |
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]) |
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return transform |
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): |
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best_ratio_diff = float('inf') |
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best_ratio = (1, 1) |
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area = width * height |
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for ratio in target_ratios: |
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target_aspect_ratio = ratio[0] / ratio[1] |
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ratio_diff = abs(aspect_ratio - target_aspect_ratio) |
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if ratio_diff < best_ratio_diff: |
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best_ratio_diff = ratio_diff |
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best_ratio = ratio |
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elif ratio_diff == best_ratio_diff: |
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: |
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best_ratio = ratio |
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return best_ratio |
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def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): |
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orig_width, orig_height = image.size |
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aspect_ratio = orig_width / orig_height |
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# calculate the existing image aspect ratio |
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target_ratios = set( |
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if |
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i * j <= max_num and i * j >= min_num) |
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) |
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# find the closest aspect ratio to the target |
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target_aspect_ratio = find_closest_aspect_ratio( |
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aspect_ratio, target_ratios, orig_width, orig_height, image_size) |
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# calculate the target width and height |
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target_width = image_size * target_aspect_ratio[0] |
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target_height = image_size * target_aspect_ratio[1] |
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1] |
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# resize the image |
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resized_img = image.resize((target_width, target_height)) |
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processed_images = [] |
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for i in range(blocks): |
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box = ( |
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(i % (target_width // image_size)) * image_size, |
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(i // (target_width // image_size)) * image_size, |
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((i % (target_width // image_size)) + 1) * image_size, |
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((i // (target_width // image_size)) + 1) * image_size |
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) |
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# split the image |
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split_img = resized_img.crop(box) |
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processed_images.append(split_img) |
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assert len(processed_images) == blocks |
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if use_thumbnail and len(processed_images) != 1: |
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thumbnail_img = image.resize((image_size, image_size)) |
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processed_images.append(thumbnail_img) |
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return processed_images |
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def load_image(image_file, input_size=448, max_num=12): |
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image = Image.open(image_file).convert('RGB') |
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transform = build_transform(input_size=input_size) |
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images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) |
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pixel_values = [transform(image) for image in images] |
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pixel_values = torch.stack(pixel_values) |
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return pixel_values |
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model = AutoModel.from_pretrained( |
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"5CD-AI/Vintern-3B-beta", |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True, |
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).eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-3B-beta", trust_remote_code=True, use_fast=False) |
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test_image = 'test-image.jpg' |
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pixel_values = load_image(test_image, max_num=6).to(torch.bfloat16).cuda() |
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generation_config = dict(max_new_tokens= 512, do_sample=False, num_beams = 3, repetition_penalty=3.5) |
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question = '<image>\nMô tả hình ảnh một cách chi tiết.' |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
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print(f'User: {question}\nAssistant: {response}') |
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#question = "Câu hỏi khác ......" |
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#response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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#print(f'User: {question}\nAssistant: {response}') |
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``` |
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## Citation |
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``` |
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@misc{doan2024vintern1befficientmultimodallarge, |
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title={Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese}, |
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author={Khang T. Doan and Bao G. Huynh and Dung T. Hoang and Thuc D. Pham and Nhat H. Pham and Quan T. M. Nguyen and Bang Q. Vo and Suong N. Hoang}, |
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year={2024}, |
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eprint={2408.12480}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2408.12480}, |
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} |
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``` |
