Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+datasets:
+- benchang1110/TaiVision-pretrain-1M-v2.0
+language:
+- zh
+pipeline_tag: image-text-to-text
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
 
+# Model Card for Model ID
 
+![TaivisionLM](TaivisionLM.png)
 ## Model Details
 
-### Model Description
+## English
+# TaiVisionLM: The First of Its Kind! 🚀
 
-<!-- Provide a longer summary of what this model is. -->
+🌟 This is a small (only 1.2B parameters) visual language model on Hugging Face that responds to Traditional Chinese instructions given an image input! 🌟
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+✨ Developed compatible with the Transformers library, TaiVisionLM is quick to load, fine-tune, and use for lightning-fast inferences without needing any external libraries! ⚡️
 
-- **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]
+Ready to experience the Traditional Chinese visual language model? Let's go! 🖼️🤖
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+## 繁體中文
+# 台視: 台灣視覺語言模型!! 🚀
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+🌟 TaiVisionLM 是一個小型的視覺語言模型（僅有 12 億參數），可以根據圖像輸入來回覆繁體中文指令！🌟
 
-## Uses
+✨ TaiVisionLM 可以用 transformers 載入、微調和使用！⚡️
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+準備好體驗"臺視"了嗎？讓我們開始吧！🖼️🤖
 
-### 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]
+### Model Description
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## English
+This model is a multimodal large language model that combines [SigLIP](https://huggingface.co/google/siglip-base-patch16-224) as its vision encoder with [Tinyllama](https://huggingface.co/benchang1110/Taiwan-tinyllama-v1.0-chat) as its language model. The vision projector connects the two modalities together. 
+Its architecture closely resembles [PaliGemma](https://huggingface.co/docs/transformers/v4.44.0/model_doc/paligemma).
 
-[More Information Needed]
+Here's the summary of the development process:
 
-### Out-of-Scope Use
+1) **Unimodal pretraining**
+    - In this stage, instead of pretraining both modalities from scratch, I leverage the image encoder from [google/siglip-base-patch16-224-multilingual](https://huggingface.co/google/siglip-base-patch16-224) and the language model trained by ourselves (https://huggingface.co/benchang1110/Taiwan-tinyllama-v1.0-chat).
+2) **Feature Alignment**
+    - We trained the vision projector and language model using LoRA using 1M image-text pairs to align visual and textual features.  
+    This model is the finetuned version of [benchang1110/TaiVisionLM-base-v1](https://huggingface.co/benchang1110/TaiVisionLM-base-v1). We fintuned the model using 1M image-text pairs. The finetuned model will generate a longer and more detailed description of the image.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+3) **Task Specific Training**
+    - The aligned model undergoes further training for tasks such as short captioning, detailed captioning, and simple visual question answering.
+    We will undergo this stage after the dataset is ready!
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+- **Developed by:** [benchang1110](https://huggingface.co/benchang1110)
+- **Model type:** [Image-Text-to-Text](https://huggingface.co/tasks/image-text-to-text)
+- **Language(s) (NLP):** *Traditional Chinese*
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## 繁體中文
+這個模型是一個多模態的語言模型，結合了 [SigLIP](https://huggingface.co/docs/transformers/en/model_doc/siglip) 作為其視覺編碼器，並使用 [Tinyllama](https://huggingface.co/benchang1110/Taiwan-tinyllama-v1.0-chat) 作為語言模型。視覺投影器將這兩種模態結合在一起。  
+其架構與 [PaliGemma](https://huggingface.co/docs/transformers/v4.44.0/model_doc/paligemma) 非常相似。
 
-[More Information Needed]
+以下是開發過程的摘要：
 
-### Recommendations
+1) **單模態預訓練**
+   - 在這個階段，我���用了 [google/siglip-base-patch16-224-multilingual](https://huggingface.co/google/siglip-base-patch16-224-multilingual) 的圖像編碼器，以及我們自己訓練的語言模型（[Taiwan-tinyllama-v1.0-chat](https://huggingface.co/benchang1110/Taiwan-tinyllama-v1.0-chat)）。
+2) **特徵對齊**
+   - 我們使用了100萬個圖片和文本的配對來訓練圖像投影器 (visual projector)，並使用 LoRA 來微調語言模型的權重。
+    這個模型是 [benchang1110/TaiVisionLM-base-v1](https://huggingface.co/benchang1110/TaiVisionLM-base-v1) 的微調版本。我們使用了100萬個圖片和文本的配對來微調模型。微調後的模型將生成更長、更詳細的圖片描述。
+3) **任務特定訓練**
+   - 對齊後的模型將進行進一步的訓練，針對短描述、詳細描述和簡單視覺問答等任務。我們將在數據集準備好後進行這一階段的訓練！
 
-<!-- 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.
+- **創作者:** [benchang1110](https://huggingface.co/benchang1110)
+- **模型類型:** [Image-Text-to-Text](https://huggingface.co/tasks/image-text-to-text)
+- **語言:** 繁體中文
+  
+---
 
 ## 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]
-
+## English
+
+In Transformers, you can load the model and do inference as follows:
+
+**IMPORTANT NOTE:** TaiVisionLM model is not yet integrated natively into the Transformers library. So you need to set ```trust_remote_code=True``` when loading the model. It will download the ```configuration_taivisionlm.py```, ```modeling_taivisionlm.py``` and ```processing_taivisionlm.py``` files from the repo. You can check out the content of these files under the *Files and Versions* tab and pin the specific versions if you have any concerns regarding malicious code.
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM, AutoConfig
+from PIL import Image
+import requests
+import torch
+config = AutoConfig.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True,torch_dtype=torch.float16,attn_implementation="sdpa").to('cuda')
+model.eval()
+url = "https://media.wired.com/photos/598e35fb99d76447c4eb1f28/master/pass/phonepicutres-TA.jpg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+text = "描述圖片"
+inputs = processor(text=text,images=image, return_tensors="pt",padding=False).to('cuda')
+outputs = processor.tokenizer.decode(model.generate(**inputs,max_length=512)[0])
+print(outputs)
+```
+
+## 中文
+利用 transformers，可以用下面程式碼進行推論:
+
+**重要通知:** 台視 (TaiVisionLM) 還沒被整合進transformers，因此在下載模型時要使用 ```trust_remote_code=True```，下載模型將會使用```configuration_taivisionlm.py```、 ```modeling_taivisionlm.py``` 和 ```processing_taivisionlm.py``` 這三個檔案，若擔心有惡意程式碼，請先點選右方 *Files and Versions* 來查看程式碼內容。
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM, AutoConfig
+from PIL import Image
+import requests
+import torch
+config = AutoConfig.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("benchang1110/TaiVisionLM-base-v2",trust_remote_code=True,torch_dtype=torch.float16,attn_implementation="sdpa").to('cuda')
+model.eval()
+url = "https://media.wired.com/photos/598e35fb99d76447c4eb1f28/master/pass/phonepicutres-TA.jpg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+text = "描述圖片"
+inputs = processor(text=text,images=image, return_tensors="pt",padding=False).to('cuda')
+outputs = processor.tokenizer.decode(model.generate(**inputs,max_length=512)[0])
+print(outputs)
+```
+
+
+### Comparision with prior model ([benchang1110/TaiVisionLM-base-v1](https://huggingface.co/benchang1110/TaiVisionLM-base-v1))
+
+- **Example 1**
+![smile](https://www.slidecow.com/wp-content/uploads/2018/04/Setting-Up-The-Slide-Text-1000x563.jpg)
+* TaiVisionLM-base-v1:  
+卡通插圖描繪掛在家門口的標誌，上下方以卡通插圖的方式呈現。
+* TaiVisionLM-base-v2:  
+這張圖描繪了一個單詞「SMILE」經典的卡通字體。該字表面��黑白的主要色彩調色板。詞以貫穿其身體的光滑線條字體書寫。該字具有模糊的質感，與單詞形成平滑而簡約的視覺效果。  
+字母「「SMILE」」自豪地表示。顯眼的文字是圖片的焦點，吸引觀眾的注意力到其具有簡潔性的方式。該字在白色背景上顯眼地展示，與黑色字體形成鮮明對比。  
+圖片中沒有其他物品或文字。字和底部的文字並沒有提供有關詞「「SMILE」具體含義的任何其他背景信息。然而，詞「「SMILE」」的整體設計使其成為這張影像中的焦點，吸引了注意力到其獨特形狀。圖片中沒有其他物品或文字。
+
+
+- **Example 2**
+![paris](https://static.toiimg.com/thumb/msid-53891743,width-748,height-499,resizemode=4,imgsize-152022/.jpg)
+* TaiVisionLM-base-v1:  
+這是一幅攝影作品，展示了巴黎的鐵塔被水景所環繞
+* TaiVisionLM-base-v2:
+這張照片捕捉了巴黎，法國標誌性的塔樓和人行道景觀的令人驚嘆的景象。塔樓高聳在清澈的藍天沿著舊有大路的背景之上。它是一座高聳入雲的圓頂金屬圖案，高度被分數精確錯量。塔樓由金屬和石頭結構組成，其統一的形狀證明了其歷史意義。  
+塔樓東面延伸的人行道向遠處延伸，邀請路人探索它所有的美麗。這條人行道上排列著樹木，它們翠綠的葉片與藍天形成鮮明的對比。它們的存在為場景增添了一抹綠意，為都市景觀增添了一抹自然元素。  
+背景中可以看到巴黎城市景觀。各種大小和設計的建築物可以看到，它們矗立在背景中，它們的建築藝術被塔樓和人行道的視野所突顯。天空是一個清澈的藍色，它延伸到遠方，沒有任何雲彩的陰影。
+
+這張照片是巴黎豐富歷史和現代性的一個見證。塔樓和人行道標誌著這座經典都市的地標，高聳主權人偶的高度及其證據這座城市獨特的信仰。橫跨整張照片的人行道禮貌地介紹了城市的繁忙路線。
 ### 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. -->
+Since we don't have enough resources to train the model on the whole dataset, we only use 250k image-text pairs for training. The following training hyperparameters are used in feature alignment and task specific training stages respectively:
 
-[More Information Needed]
 
-### Results
+- **Feature Alignment**
 
-[More Information Needed]
+| Data size    | Global Batch Size | Learning Rate | Epochs | Max Length | Weight Decay |
+|--------------|-------------------|---------------|--------|------------|--------------|
+| 250k        | 2               | 5e-5          | 1      | 2048       | 1e-5            |
 
-#### 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]
+We use full-parameter finetuning for the projector and apply LoRA to the language model.
 
+We will update the training procedure once we have more resources to train the model on the whole dataset.
+![metric](metrics.png)
 ### 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]
-
-<!-- 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]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- **Feature Alignment**
+  1xV100(32GB), took approximately 12 GPU hours.