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--- |
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license: mit |
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datasets: |
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- laion/laion2B-en |
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- laion/laion-coco |
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- laion/laion2B-multi |
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- kakaobrain/coyo-700m |
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- conceptual_captions |
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- wanng/wukong100m |
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pipeline_tag: visual-question-answering |
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--- |
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# Model Card for Mini-InternVL-Chat-4B-V1-5 |
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<center> |
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<p><img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/pvfKc16O-ej91632FHaIK.png" style="width:80%;" alt="image/png"></p> |
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</center> |
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[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) |
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[\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#model-usage) [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376) |
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You can run multimodal large models using a 1080Ti now. |
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We are delighted to introduce the Mini-InternVL-Chat series. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) down to 300M and used [InternLM2-Chat-1.8B](https://huggingface.co/internlm/internlm2-chat-1_8b) or [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) as our language model. This resulted in a small multimodal model with excellent performance. |
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As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B / Phi-3-mini-128k-instruct. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training. |
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## Model Details |
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- **Model Type:** multimodal large language model (MLLM) |
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- **Model Stats:** |
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- Architecture: [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) + MLP + [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) |
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- Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). |
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- Params: 4.2B |
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- **Training Strategy:** |
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- Learnable component in the pretraining stage: MLP |
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- Learnable component in the finetuning stage: ViT + MLP + LLM |
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- For more details on training hyperparameters, take a look at our code: [pretrain](<>) | [finetune](<>) |
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## Released Models |
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| Model | Vision Foundation Model | Release Date | Note | |
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| :----------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | :----------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| InternVL-Chat-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5)) | InternViT-6B-448px-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5)) | 2024.04.18 | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new) | |
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| InternVL-Chat-V1-2-Plus(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) ) | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) | 2024.02.21 | more SFT data and stronger | |
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| InternVL-Chat-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) ) | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) | 2024.02.11 | scaling up LLM to 34B | |
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| InternVL-Chat-V1-1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1)) | InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0)) | 2024.01.24 | support Chinese and stronger OCR | |
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## Performance |
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## Model Usage |
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We provide an example code to run Mini-InternVL-Chat-4B-V1-5 using `transformers`. |
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You can also use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model. |
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> Please use transformers==4.37.2 to ensure the model works normally. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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import torchvision.transforms as T |
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from PIL import Image |
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from torchvision.transforms.functional import InterpolationMode |
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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=6, 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=6): |
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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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path = "OpenGVLab/Mini-InternVL-Chat-4B-V1-5" |
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model = AutoModel.from_pretrained( |
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path, |
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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).eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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# set the max number of tiles in `max_num` |
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pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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generation_config = dict( |
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num_beams=1, |
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max_new_tokens=512, |
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do_sample=False, |
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) |
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# single-round single-image conversation |
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question = "请详细描述图片" # Please describe the picture in detail |
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response = model.chat(tokenizer, pixel_values, question, generation_config) |
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print(question, response) |
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# multi-round single-image conversation |
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question = "请详细描述图片" # Please describe the picture in detail |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
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print(question, response) |
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question = "请根据图片写一首诗" # Please write a poem according to the picture |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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print(question, response) |
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# multi-round multi-image conversation |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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question = "详细描述这两张图片" # Describe the two pictures in detail |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) |
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print(question, response) |
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question = "这两张图片的相同点和区别分别是什么" # What are the similarities and differences between these two pictures |
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response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) |
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print(question, response) |
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# batch inference (single image per sample) |
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pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() |
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pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() |
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image_counts = [pixel_values1.size(0), pixel_values2.size(0)] |
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pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) |
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questions = ["Describe the image in detail."] * len(image_counts) |
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responses = model.batch_chat(tokenizer, pixel_values, |
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image_counts=image_counts, |
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questions=questions, |
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generation_config=generation_config) |
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for question, response in zip(questions, responses): |
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print(question) |
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print(response) |
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``` |
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## Citation |
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If you find this project useful in your research, please consider citing: |
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```BibTeX |
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@article{chen2023internvl, |
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title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks}, |
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author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng}, |
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journal={arXiv preprint arXiv:2312.14238}, |
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year={2023} |
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} |
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@article{chen2024far, |
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title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, |
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author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others}, |
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journal={arXiv preprint arXiv:2404.16821}, |
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year={2024} |
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} |
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``` |
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## License |
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This project is released under the MIT license. |
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## Acknowledgement |
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InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work! |
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