Update README.md

README.md

@@ -1,199 +1,224 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- vi
+- en
+- zh
+base_model:
+- OpenGVLab/InternVL2_5-1B
+pipeline_tag: image-text-to-text
 ---
 
-# Model Card for Model ID
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/AxRFDUt8uft6HVxBWuXgJ.png)
 
-<!-- Provide a quick summary of what the model is/does. -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6336b5c831efcb5647f00170/DrUCZuXuMz47uVU4zqnJ4.png)
 
 
+## Vintern-1B-v2 ❄️ (Viet-InternVL2-1B-v2) - The LLaVA 🌋 Challenger
 
-## 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.
+We are excited to introduce  **Vintern-1B-v2** the Vietnamese 🇻🇳 multimodal model that combines the advanced Vietnamese language model [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)[1] with the latest visual model, [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)[2], CVPR 2024. This model excels in tasks such as OCR-VQA, Doc-VQA, and Chart-VQA,... With only 1 billion parameters, it is **4096 context length** finetuned from the [Viet-InternVL2-1B](https://huggingface.co/5CD-AI/Viet-InternVL2-1B) model on over 3 million specialized image-question-answer pairs for optical character recognition 🔍, text recognition 🔤, document extraction 📑, and general VQA. The model can be integrated into various on-device applications 📱, demonstrating its versatility and robust capabilities.
 
-[More Information Needed]
+[**\[🤗 HF Demo\]**](https://huggingface.co/spaces/khang119966/Vintern-v2-Demo)
 
-## Training Details
+The special thing is that our model can be easily finetuned with a T4 GPU on Google Colab by following the instructions provided at the end of this section.
 
-### 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]
+## Model Details
 
-#### Summary
+|      Model Name      |                                     Vision Part                                     |                                        Language Part                                         |                  
+| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: |
+|      Vintern-1B-v2      |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |            [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)            |  
 
 
+Vintern-1B-v2 is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. Vintern-1B-v2 consists of [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px), an MLP projector, and [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
 
-## Model Examination [optional]
+## Training details 📚
 
-<!-- Relevant interpretability work for the model goes here -->
+The fine-tuning dataset was meticulously sampled in part from the following datasets:  
+[Viet-OCR-VQA 📚](https://huggingface.co/datasets/5CD-AI/Viet-OCR-VQA), [Viet-Doc-VQA 📄](https://huggingface.co/datasets/5CD-AI/Viet-Doc-VQA), [Viet-Doc-VQA-II 📑](https://huggingface.co/datasets/5CD-AI/Viet-Doc-VQA-II), [Vista 🖼️](https://huggingface.co/datasets/Vi-VLM/Vista), [Viet-Receipt-VQA 🧾](https://huggingface.co/datasets/5CD-AI/Viet-Receipt-VQA), [Viet-Sketches-VQA ✏️](https://huggingface.co/datasets/5CD-AI/Viet-Sketches-VQA), [Viet-Geometry-VQA 📐](https://huggingface.co/datasets/5CD-AI/Viet-Geometry-VQA), [Viet-Wiki-Handwriting ✍️](https://huggingface.co/datasets/5CD-AI/Viet-Wiki-Handwriting), [Viet-ComputerScience-VQA 💻](https://huggingface.co/datasets/5CD-AI/Viet-ComputerScience-VQA), [Viet-Handwriting-gemini-VQA 🖋️](https://huggingface.co/datasets/5CD-AI/Viet-Handwriting-gemini-VQA), [Viet-Menu-gemini-VQA 🍽️](https://huggingface.co/datasets/5CD-AI/Viet-Menu-gemini-VQA), [Viet-Vintext-gemini-VQA 📜](https://huggingface.co/datasets/5CD-AI/Viet-Vintext-gemini-VQA), [Viet-OpenViVQA-gemini-VQA 🧠](https://huggingface.co/datasets/5CD-AI/Viet-OpenViVQA-gemini-VQA), [Viet-Resume-VQA 📃](https://huggingface.co/datasets/5CD-AI/Viet-Resume-VQA), [Viet-ViTextVQA-gemini-VQA 📑](https://huggingface.co/datasets/5CD-AI/Viet-ViTextVQA-gemini-VQA)
 
-[More Information Needed]
+## Benchmarks 📈
 
-## 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).
+## Examples
 
-- **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]
+<div align="center">
+  <img src="ex_images/1.png" width="500"/>
+</div>
 
-## Technical Specifications [optional]
+```
 
-### Model Architecture and Objective
+```
 
-[More Information Needed]
+<div align="center">
+  <img src="ex_images/4.jpg" width="500"/>
+</div>
 
-### Compute Infrastructure
+```
 
-[More Information Needed]
+```
 
-#### Hardware
+<div align="center">
+  <img src="ex_images/2.jpg" width="500"/>
+</div>
 
-[More Information Needed]
+```
 
-#### Software
+```
 
-[More Information Needed]
+<div align="center">
+  <img src="ex_images/3.png" width="400"/>
+</div>
 
-## 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:**
+<div align="center">
+  <img src="ex_images/5.jpg" width="400"/>
+</div>
 
-[More Information Needed]
+```
 
-**APA:**
+```
 
-[More Information Needed]
+<div align="center">
+  <img src="ex_images/6.png" width="400"/>
+</div>
 
-## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+```
 
-[More Information Needed]
+```
 
-## More Information [optional]
+## Quickstart
 
-[More Information Needed]
+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
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZD1oB56PF0lF66RCuTVJYLTEV0tM3CFf?usp=sharing)
 
-## Model Card Authors [optional]
+```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
 
-[More Information Needed]
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
 
-## Model Card Contact
+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
 
-[More Information Needed]
+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-1B-v2",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-1B-v2", trust_remote_code=True, use_fast=False)
+
+test_image = 'test-image.jpg'
+
+pixel_values = load_image(test_image, max_num=12).to(torch.bfloat16).cuda()
+generation_config = dict(max_new_tokens= 1024, do_sample=False, num_beams = 3, repetition_penalty=2.5)
+
+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}')
+```
+
+## Finetune on your Data
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bK6fpWfResjv9UxWoKHDStXQ8bop3a6Z?usp=sharing)
+
+
+## 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}, 
+}
+```