Update README.md

README.md

@@ -1,199 +1,269 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- vi
+- en
+- zh
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+- OpenGVLab/InternViT-300M-448px
+pipeline_tag: visual-question-answering
 ---
+## Vintern-3B-beta ❄️ - The LLaVA 🌋 Challenger
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+**What's new in Vintern-3B-beta!**
+- **We successfully reproduced the training process of InternVL from scratch.**
+- 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.
+- 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).
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+|      Model Name      |                                     Vision Part                                     |                                        Language Part                                         |                  
+| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: |
+|      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)            |  
+
+
+## Bytedance/MTVQA Benchmark
+
+We surpassed GPT-4o and are approaching Gemini 1.5 Pro on the MTVQA dataset for Vietnamese.
+The benchmark result in [MTVQA](https://github.com/bytedance/MTVQA/tree/main) from [open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard).
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+| Rank | Method                      | Param (B) | Language Model             | Vision Model         | VI     |
+|:----:|:----------------------------|:---------:|:---------------------------|:---------------------:|:------:|
+|  1   | Gemini-1.5-Pro               |           |                             |                       | 41.3   |
+|  2   | **Vintern-3B-beta**          | **3**    | **Qwen2.5-3B-Instruct**     | **InternViT-300M**    | **41.289** |
+|  2   | GPT-4o (0513, detail-h...)   |           |                             |                       | 39.6   |
+|  3   | GPT-4o (0806, detail-h...)   |           |                             |                       | 38.9   |
+|  4   | Gemini-1.5-Flash             |           |                             |                       | 38.9   |
+|  5   | Qwen-VL-Max-0809             | 72        | Qwen2-72B                   | ViT-600M              | 36.9   |
+|  6   | GPT-4o (0513, detail-lo...)  |           |                             |                       | 26.1   |
+|  7   | Qwen-VL-Plus-0809            |           |                             |                       | 27.8   |
+|  8   | GLM-4v-9B                   | 9         | GLM-4-9B                    | EVA-02-5B             | 26.6   |
+|  9   | InternVL2-Llama3-76B         | 76        | Llama-3-70B-Instruct        | InternViT-6B          | 26.7   |
+|  10  | Step-1.5V                   |           | Step-1.5                    | stepencoder           | 18.4   |
+|  11  | InternVL2-40B               | 40        | Nous-Hermes-2-Yi-34B        | InternViT-6B          | 21.2   |
+|  12  | Pixtral-12B                 | 13        | Nemo-12B                    | ViT-400M              | 19.7   |
 
-[More Information Needed]
 
-## More Information [optional]
+## Zalo VMLU Benchmark
+The Vintern-3B-beta achieved a score of **52.98** on the Zalo VMLU Benchmark.
+<div align="center">
+  <img src="vmlu_score.png" width="700"/>
+</div>
 
-[More Information Needed]
+```
+generation_config = dict(max_new_tokens= 64, do_sample=False, num_beams = 1, repetition_penalty=3.5)
+question = "Bạn là thầy giáo 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: Một doanh nghiệp có vốn đầu tư nước ngoài có trụ sở chính ở Việt Nam, thì: Lựa Chọn: A. Được ĐKDN và HĐKD theo pháp luật Việt Nam B. Được ĐKDN và HĐKD theo pháp luật nước ngoài C. Được ĐKDN và HĐKD theo pháp luật Việt Nam và pháp luật nước ngoài tùy theo từng vấn đề cụ thể D. Cả A, B và C đều sai"
+model.chat(tokenizer, None, question, generation_config)
+```
 
-## Model Card Authors [optional]
+## open_vlm_leaderboard Benchmark
 
-[More Information Needed]
+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).. 
+The current metrics are at an acceptable level, and we are expanding the training set in English and other languages to approach global models within a comparable parameter range.
 
-## Model Card Contact
+"The table is referenced from the repo [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)."
 
-[More Information Needed]
+| Benchmark        | InternVL2-2B | MiniCPM-V 2.0 | Qwen2-VL-2B | Vintern-3B-beta |
+|:-----------------|:------------:|:-------------:|:-----------:|:---------------:|
+| MMMUval          | 36.3         | 38.2          | 41.1        | 43.55           |
+| DocVQAtest       | 86.9         | -             | 90.1        | 80.47           |
+| InfoVQAtest      | 58.9         | -             | 65.5        | 48.28           |
+| ChartQAtest      | 76.2         | -             | 73.5        | 68.32           |
+| TextVQAval       | 73.4         | -             | 79.7        | 67.09           |
+| OCRBench         | 781          | 605           | 794         | 619             |
+| MTVQA            | -            | -             | 20.0        | 23.58           |
+| RealWorldQA      | 57.3         | 55.8          | 62.9        | 57.9            |
+| MMEsum           | 1876.8       | 1808.6        | 1872.0      | 1772.9          |
+| MMBench-ENtest   | 73.2         | 69.1          | 74.9        | 70.62           |
+| MMStar           | 49.8         | 39.1          | 48.0        | 43.2            |
+| HallBenchavg     | 38.0         | 36.1          | 41.7        | 64.98           |
+| MathVistatestmini| 46.0         | 39.8          | 43.0        | 43.9            |
+
+
+<!-- ## VLSP2023: ViVRC Challenge Benchmark
+
+| **Name**               | **F1**      |
+|:----------------------:|:-----------:|
+| ICNLP                  | 3.6384      |
+| **Vintern-4B-v1**      | 3.5514      |
+| **Vintern-3B-beta**    | **3.5266**  |
+| **Vintern-1B-v2**      | 3.4616      |
+| linh                   | 3.4293      |
+| DS@ViVRC               | 3.4121      |
+| DS@UIT Dynasty         | 3.3172      |
+| NTQ Solution           | 3.2926      |
+| I, Me & Myself         | 3.2396      |
+| AVQA_AIO               | 2.9018      |
+| **Vintern-1B-v1**      | 2.7256      |
+| NguyenLe               | 2.7053      |
+| nowj2                  | 1.6808      | -->
+
+
+<!-- ## Examples
+
+<div align="center">
+  <img src="https://drscdn.500px.org/photo/1100852428/q%3D90_m%3D2048/v2?sig=7a6df43806315966517e2506394d71561f113321e0a4efc7d442e7303b5e97c7" width="400"/>
+</div>
+
+```
+
+```
+
+<div align="center">
+  <img src="https://drscdn.500px.org/photo/1100852641/q%3D90_m%3D2048/v2?sig=aba53dbde6a7e50d6c3d45289d47145c1a2c5c6708e3fb4b6fad721d4fc8a195" width="400"/>
+</div>
+
+```
+
+```
+
+<div align="center">
+  <img src="https://drscdn.500px.org/photo/1100852792/q%3D90_m%3D2048/v2?sig=d88c04be7beee1eebca7081251c11d0daeafa558bee0aa8a6fd3103b1556c5f5" width="400"/>
+</div>
+
+```
+
+```
+
+<div align="center">
+  <img src="https://drscdn.500px.org/photo/1100854004/q%3D90_m%3D2048/v2?sig=98a4d4f1fbbaec8994c71daed7a72d14d771bdbce481a91583b5955336bc08dd" width="400"/>
+</div>
+
+```
+
+```
+
+<div align="center">
+  <img src="https://drscdn.500px.org/photo/1100854109/q%3D90_m%3D2048/v2?sig=192a484e7207aafd7b827b1b42ceb24fdb740e2f6aab15cec21bd64ce0268d15" width="400"/>
+</div>
+
+```
+
+``` -->
+
+## Quickstart
+
+Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents.
+To run inference using the model, follow the steps outlined in our Colab inference notebook
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+# from decord import VideoReader, cpu
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (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
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+model = AutoModel.from_pretrained(
+    "5CD-AI/Vintern-3B-beta",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-3B-beta", trust_remote_code=True, use_fast=False)
+
+test_image = 'test-image.jpg'
+
+pixel_values = load_image(test_image, max_num=6).to(torch.bfloat16).cuda()
+generation_config = dict(max_new_tokens= 512, do_sample=False, num_beams = 3, repetition_penalty=3.5)
+
+question = '<image>\nMô tả hình ảnh một cách chi tiết.'
+
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+#question = "Câu hỏi khác ......"
+#response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
+#print(f'User: {question}\nAssistant: {response}')
+```
+
+## Citation 
+
+```
+@misc{doan2024vintern1befficientmultimodallarge,
+      title={Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese}, 
+      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},
+      year={2024},
+      eprint={2408.12480},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2408.12480}, 
+}
+```