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README.md

@@ -15,7 +15,7 @@ We released [🤗 InternVL-Chat-V1-1](https://huggingface.co/OpenGVLab/InternVL-
 As shown in the figure below, we connected our InternViT-6B to LLaMA2-13B through a simple MLP projector. Note that the LLaMA2-13B used here is not the original model but an internal chat version obtained by incrementally pre-training and fine-tuning the LLaMA2-13B base model for Chinese language tasks. Overall, our model has a total of 19 billion parameters.
 
 <p align="center">
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/HD29tU-g0An9FpQn1yK8X.png" style="width: 75%;">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/HD29tU-g0An9FpQn1yK8X.png" style="width: 75%;">
 </p>
 
 In this version, we explored increasing the resolution to 448 × 448, enhancing OCR capabilities, and improving support for Chinese conversations. Since the 448 × 448 input image generates 1024 visual tokens after passing through the ViT, leading to a significant computational burden, we use a pixel shuffle operation to reduce the 1024 tokens to 256 tokens.
@@ -122,7 +122,7 @@ The reason for writing the code this way is to avoid errors that occur during mu
 ```python
 import math
 import torch
-from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor
+from transformers import AutoTokenizer, AutoModel
 
 def split_model(model_name):
     device_map = {}

conversation.py

@@ -2,7 +2,7 @@
 Conversation prompt templates.
 
 We kindly request that you import fastchat instead of copying this file if you wish to use it.
-If you have any changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
+If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
 """
 
 import dataclasses