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--- |
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library_name: transformers |
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datasets: |
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- benchang1110/TaiVision-pretrain-1M |
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language: |
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- zh |
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pipeline_tag: image-text-to-text |
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--- |
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# Model Card for Model ID |
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## Model Details |
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## English |
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# TaiVisionLM: The First of Its Kind! 🚀 |
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🌟 This is a small (only 1.2B parameters) visual language model on Hugging Face that responds to Traditional Chinese instructions given an image input! 🌟 |
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✨ Developed compatible with the Transformers library, TaiVisionLM is quick to load, fine-tune, and use for lightning-fast inferences without needing any external libraries! ⚡️ |
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Ready to experience the Traditional Chinese visual language model? Let's go! 🖼️🤖 |
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## 繁體中文 |
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# 台視: 台灣視覺語言模型!! 🚀 |
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🌟 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！🌟 |
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✨ TaiVisionLM 可以用 transformers 載入、微調和使用！⚡️ |
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準備好體驗"臺視"了嗎？讓我們開始吧！🖼️🤖 |
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--- |
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### Model Description |
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## English |
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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. |
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Its architecture closely resembles [PaliGemma](https://huggingface.co/docs/transformers/v4.44.0/model_doc/paligemma). |
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Here's the summary of the development process: |
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1) **Unimodal pretraining** |
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- 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). |
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2) **Feature Alignment** |
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- I train the vision projector and language model using LoRA using 1B image-text pairs to align visual and textual features. |
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3) **Task Specific Training** |
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- The aligned model undergoes further training for tasks such as short captioning, detailed captioning, and simple visual question answering. |
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We will undergo this stage after the dataset is ready! |
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- **Developed by:** [benchang1110](https://huggingface.co/benchang1110) |
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- **Model type:** [Image-Text-to-Text](https://huggingface.co/tasks/image-text-to-text) |
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- **Language(s) (NLP):** *Traditional Chinese* |
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## 繁體中文 |
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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。 |
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其架構與 [PaliGemma](https://huggingface.co/docs/transformers/v4.44.0/model_doc/paligemma) 非常相似。 |
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以下是開發過程的摘要： |
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1) **單模態預訓練** |
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- 在這個階段，我利用了 [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)）。 |
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2) **特徵對齊** |
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- 我使用了10億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。 |
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3) **任務特定訓練** |
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- 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！ |
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- **創作者:** [benchang1110](https://huggingface.co/benchang1110) |
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- **模型類型:** [Image-Text-to-Text](https://huggingface.co/tasks/image-text-to-text) |
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- **語言:** *Traditional Chinese* |
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--- |
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## How to Get Started with the Model |
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## English |
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In Transformers, you can load the model and do inference as follows: |
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**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. |
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```python |
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from transformers import AutoProcessor, AutoModelForCausalLM, AutoConfig |
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from PIL import Image |
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import requests |
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import torch |
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config = AutoConfig.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True,torch_dtype=torch.float16,attn_implementation="sdpa").to('cuda') |
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model.eval() |
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url = "https://media.wired.com/photos/598e35fb99d76447c4eb1f28/master/pass/phonepicutres-TA.jpg" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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text = "描述圖片" |
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inputs = processor(text=text,images=image, return_tensors="pt",padding=False).to('cuda') |
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outputs = processor.tokenizer.decode(model.generate(**inputs,max_length=512)[0]) |
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print(outputs) |
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``` |
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## 中文 |
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利用 transformers，可以用下面程式碼進行推論: |
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**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。 |
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```python |
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from transformers import AutoProcessor, AutoModelForCausalLM, AutoConfig |
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from PIL import Image |
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import requests |
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import torch |
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config = AutoConfig.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("benchang1110/TaiVisionLM-base",trust_remote_code=True,torch_dtype=torch.float16,attn_implementation="sdpa").to('cuda') |
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model.eval() |
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url = "https://media.wired.com/photos/598e35fb99d76447c4eb1f28/master/pass/phonepicutres-TA.jpg" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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text = "描述圖片" |
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inputs = processor(text=text,images=image, return_tensors="pt",padding=False).to('cuda') |
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outputs = processor.tokenizer.decode(model.generate(**inputs,max_length=512)[0]) |
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print(outputs) |
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``` |
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### Training Procedure |
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The following training hyperparameters are used in feature alignment and task specific training stages respectively: |
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- **Feature Alignment** |
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| Data size | Global Batch Size | Learning Rate | Epochs | Max Length | Weight Decay | |
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|--------------|-------------------|---------------|--------|------------|--------------| |
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| 1M | 16 | 5e-5 | 1 | 2048 | 1e-5 | |
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We use full-parameter finetuning for the projector and apply LoRA to the language model. |
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### Compute Infrastructure |
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- **Feature Alignment** |
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1xV100(32GB), took approximately 16 GPU hours. |
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