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README.md

@@ -11,7 +11,7 @@ pipeline_tag: visual-question-answering
 
 ## Introduction
 
-We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of instruction-tuned models, ranging from 2 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-4B model.
+We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of **instruction-tuned models**, ranging from 2 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-4B model.
 
 Compared to the state-of-the-art open-source multimodal large language models, InternVL 2.0 surpasses most open-source models. It demonstrates competitive performance on par with proprietary commercial models across various capabilities, including document and chart comprehension, infographics QA, scene text understanding and OCR tasks, scientific and mathematical problem solving, as well as cultural understanding and integrated multimodal capabilities.
 
@@ -23,6 +23,8 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 
 ## Performance
 
+### Image Benchmarks
+
 |          Benchmark           | PaliGemma-3B | Phi-3-Vision | Mini-InternVL-4B-1.5 | InternVL2-4B |
 | :--------------------------: | :----------: | :----------: | :------------------: | :----------: |
 |          Model Size          |     2.9B     |     4.2B     |         4.2B         |     4.2B     |
@@ -53,6 +55,10 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 
 Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
 
+### Video Benchmarks
+
+TBD
+
 ## Quick Start
 
 We provide an example code to run InternVL2-4B using `transformers`.
@@ -261,9 +267,10 @@ def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=3
 
 video_path = './examples/red-panda.mp4'
 # pixel_values, num_patches_list = load_video(video_path, num_segments=32, max_num=1)
-pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=2)
+pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
 pixel_values = pixel_values.to(torch.bfloat16).cuda()
-video_prefix = '\n'.join([f'Frame{i+1}: <image>' for i in range(len(num_patches_list))]) + '\n'question = video_prefix + 'What is the red panda doing?'
+video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+question = video_prefix + 'What is the red panda doing?'
 # Frame1: <image>\nFrame2: <image>\n...\nFrame31: <image>\n{question}
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list,
@@ -291,7 +298,7 @@ pip install lmdeploy
 
 You can run batch inference locally with the following python code:
 
-> This model is not yet supported by LMDeploy.
+> Warning: This model is not yet supported by LMDeploy.
 
 ```python
 from lmdeploy.vl import load_image
@@ -332,7 +339,7 @@ If you find this project useful in your research, please consider citing:
 
 ## 简介
 
-我们很高兴宣布 InternVL 2.0 的发布，这是 InternVL 系列多模态大语言模型的最新版本。InternVL 2.0 提供了多种指令微调的模型，参数从 20 亿到 1080 亿不等。此仓库包含经过指令微调的 InternVL2-4B 模型。
+我们很高兴宣布 InternVL 2.0 的发布，这是 InternVL 系列多模态大语言模型的最新版本。InternVL 2.0 提供了多种**指令微调**的模型，参数从 20 亿到 1080 亿不等。此仓库包含经过指令微调的 InternVL2-4B 模型。
 
 与最先进的开源多模态大语言模型相比，InternVL 2.0 超越了大多数开源模型。它在各种能力上表现出与闭源商业模型相媲美的竞争力，包括文档和图表理解、信息图表问答、场景文本理解和 OCR 任务、科学和数学问题解决，以及文化理解和综合多模态能力。
 
@@ -344,6 +351,8 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 
 ## 性能测试
 
+### 图像相关评测
+
 |          评测数据集          | PaliGemma-3B | Phi-3-Vision | Mini-InternVL-4B-1.5 | InternVL2-4B |
 | :--------------------------: | :----------: | :----------: | :------------------: | :----------: |
 |           模型大小           |     2.9B     |     4.2B     |         4.2B         |     4.2B     |