--- license: mit pipeline_tag: image-text-to-text library_name: transformers base_model: - OpenGVLab/InternViT-6B-448px-V1-2 - NousResearch/Nous-Hermes-2-Yi-34B new_version: OpenGVLab/InternVL2_5-38B base_model_relation: merge language: - multilingual tags: - internvl - custom_code --- # InternVL-Chat-V1-2-Plus [\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
image
## Introduction InternVL-Chat-V1-2-Plus uses the same model architecture as [InternVL-Chat-V1-2](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2), but the difference lies in the SFT dataset. InternVL-Chat-V1-2 only utilizes an SFT dataset with 1.2M samples, while **our plus version employs an SFT dataset with 12M samples**.

image

## Model Details - **Model Type:** multimodal large language model (MLLM) - **Model Stats:** - Architecture: [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) + MLP + [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) - Image size: 448 x 448 (256 tokens) - Params: 40B - **Training Strategy:** - Pre-training Stage - Learnable Component: ViT + MLP - Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data. - Note: In this stage, we first load the pre-trained weights of [InternViT-6B-448px-V1-0](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0) and connect it to Nous-Hermes-2-Yi-34B. After pre-training, the extracted ViT is published as [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2). Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens. - Supervised Fine-tuning Stage - Learnable Component: ViT + MLP + LLM - Data: 12 million SFT samples. ## Performance \* Proprietary Model      † Training Set Observed | name | image size | MMMU
(val) | MMMU
(test) | MathVista
(testmini) | MMB
(test) | MMB−CN
(test) | MMVP | MME | ScienceQA
(image) | POPE | TextVQA
(val) | SEEDv1
(image) | VizWiz
(test) | GQA
(test) | | --------------------------- | ---------- | ------------- | -------------- | ----------------------- | ------------- | ---------------- | ---- | -------- | -------------------- | ---- | ---------------- | ----------------- | ---------------- | ------------- | | GPT-4V\* | unknown | 56.8 | 55.7 | 49.9 | 77.0 | 74.4 | 38.7 | 1409/517 | - | - | 78.0 | 71.6 | - | - | | Gemini Ultra\* | unknown | 59.4 | - | 53.0 | - | - | - | - | - | - | 82.3 | - | - | - | | Gemini Pro\* | unknown | 47.9 | - | 45.2 | 73.6 | 74.3 | 40.7 | 1497/437 | - | - | 74.6 | 70.7 | - | - | | Qwen−VL−Plus\* | unknown | 45.2 | 40.8 | 43.3 | 67.0 | 70.7 | - | 1681/502 | - | - | 78.9 | 65.7 | - | - | | Qwen−VL−Max\* | unknown | 51.4 | 46.8 | 51.0 | 77.6 | 75.7 | - | - | - | - | 79.5 | - | - | - | | | | | | | | | | | | | | | | | | LLaVA−NEXT−34B | 672x672 | 51.1 | 44.7 | 46.5 | 79.3 | 79.0 | - | 1631/397 | 81.8 | 87.7 | 69.5 | 75.9 | 63.8 | 67.1† | | InternVL−Chat
−V1-2 | 448x448 | 51.6 | 46.2 | 47.7 | 82.2 | 81.2 | 56.7 | 1687/489 | 83.3 | 88.0 | 72.5 | 75.6 | 60.0 | 64.0† | | InternVL−Chat
−V1-2−Plus | 448x448 | 50.3 | 45.6 | 59.9 | 83.8 | 82.0 | 58.7 | 1625/553 | 98.1† | 88.7 | 74.1† | 76.4 | - | 66.9† | - MMBench results are collected from the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard). Here, we have conducted only a simple performance comparison. For more detailed performance information and additional evaluation metrics, please refer to our performance summary table. ## Quick Start We provide an example code to run InternVL-Chat-V1-2-Plus using `transformers`. > Please use transformers>=4.37.2 to ensure the model works normally. ### Model Loading #### 16-bit (bf16 / fp16) ```python import torch from transformers import AutoTokenizer, AutoModel path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() ``` #### BNB 8-bit Quantization ```python import torch from transformers import AutoTokenizer, AutoModel path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, load_in_8bit=True, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval() ``` #### BNB 4-bit Quantization > **⚠️ Warning:** Due to significant quantization errors with BNB 4-bit quantization on InternViT-6B, the model may produce nonsensical outputs and fail to understand images. Therefore, please avoid using BNB 4-bit quantization. #### Multiple GPUs The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors. ```python import math import torch from transformers import AutoTokenizer, AutoModel def split_model(model_name): device_map = {} world_size = torch.cuda.device_count() num_layers = {'InternVL-Chat-V1-2': 60, 'InternVL-Chat-V1-2-Plus': 60}[model_name] # Since the first GPU will be used for ViT, treat it as half a GPU. num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) num_layers_per_gpu = [num_layers_per_gpu] * world_size num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) layer_cnt = 0 for i, num_layer in enumerate(num_layers_per_gpu): for j in range(num_layer): device_map[f'language_model.model.layers.{layer_cnt}'] = i layer_cnt += 1 device_map['vision_model'] = 0 device_map['mlp1'] = 0 device_map['language_model.model.tok_embeddings'] = 0 device_map['language_model.model.embed_tokens'] = 0 device_map['language_model.output'] = 0 device_map['language_model.model.norm'] = 0 device_map['language_model.lm_head'] = 0 device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 return device_map path = "OpenGVLab/InternVL-Chat-V1-2-Plus" device_map = split_model('InternVL-Chat-V1-2-Plus') model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True, device_map=device_map).eval() ``` ### Inference with Transformers #### Pure-text conversation ```python from transformers import AutoTokenizer, AutoModel import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) generation_config = dict(max_new_tokens=1024, do_sample=True) question = 'Hello, who are you?' response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') question = 'Can you tell me a story?' response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Single-image single-round conversation ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from PIL import Image import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) image_processor = CLIPImageProcessor.from_pretrained(path) image = Image.open('./examples/image2.jpg').resize((448, 448)) pixel_values = image_processor(images=image, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() generation_config = dict(max_new_tokens=1024, do_sample=True) question = '\nPlease describe the image shortly.' response = model.chat(tokenizer, pixel_values, question, generation_config) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Single-image multi-round conversation ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from PIL import Image import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) image_processor = CLIPImageProcessor.from_pretrained(path) image = Image.open('./examples/image2.jpg').resize((448, 448)) pixel_values = image_processor(images=image, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() generation_config = dict(max_new_tokens=1024, do_sample=True) question = '\nPlease describe the image in detail.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') question = 'Please write a poem according to the image.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Multi-image multi-round conversation, combined images > **⚠️️ Warning:** Please note that for this model, we support multi-image chat in the interface, but the results are not very good due to the lack of training with multi-image data. ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from PIL import Image import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) image_processor = CLIPImageProcessor.from_pretrained(path) image1 = Image.open('./examples/image1.jpg').resize((448, 448)) pixel_values1 = image_processor(images=image1, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() image2 = Image.open('./examples/image2.jpg').resize((448, 448)) pixel_values2 = image_processor(images=image2, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) generation_config = dict(max_new_tokens=1024, do_sample=True) question = '\nDescribe the two images in detail.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') question = 'What are the similarities and differences between these two images.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Multi-image multi-round conversation, separate images > **⚠️️ Warning:** Please note that for this model, we support multi-image chat in the interface, but the results are not very good due to the lack of training with multi-image data. ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from PIL import Image import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) image_processor = CLIPImageProcessor.from_pretrained(path) image1 = Image.open('./examples/image1.jpg').resize((448, 448)) pixel_values1 = image_processor(images=image1, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() image2 = Image.open('./examples/image2.jpg').resize((448, 448)) pixel_values2 = image_processor(images=image2, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] generation_config = dict(max_new_tokens=1024, do_sample=True) question = 'Image-1: \nImage-2: \nDescribe the two images in detail.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=None, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') question = 'What are the similarities and differences between these two images.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=history, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Batch inference, single image per sample ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from PIL import Image import torch path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) image_processor = CLIPImageProcessor.from_pretrained(path) image1 = Image.open('./examples/image1.jpg').resize((448, 448)) pixel_values1 = image_processor(images=image1, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() image2 = Image.open('./examples/image2.jpg').resize((448, 448)) pixel_values2 = image_processor(images=image2, return_tensors='pt').pixel_values.to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] generation_config = dict(max_new_tokens=1024, do_sample=True) questions = ['\nDescribe the image in detail.'] * len(num_patches_list) responses = model.batch_chat(tokenizer, pixel_values, num_patches_list=num_patches_list, questions=questions, generation_config=generation_config) for question, response in zip(questions, responses): print(f'User: {question}') print(f'Assistant: {response}') ``` #### Video multi-round conversation > **⚠️️ Warning:** Please note that for this model, we support video chat in the interface, but the results are not very good due to the lack of training with video data. ```python from transformers import AutoTokenizer, AutoModel, CLIPImageProcessor from decord import VideoReader, cpu from PIL import Image import numpy as np import torch def get_index(bound, fps, max_frame, first_idx=0, num_segments=32): if bound: start, end = bound[0], bound[1] else: start, end = -100000, 100000 start_idx = max(first_idx, round(start * fps)) end_idx = min(round(end * fps), max_frame) seg_size = float(end_idx - start_idx) / num_segments frame_indices = np.array([ int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) for idx in range(num_segments) ]) return frame_indices def load_video(video_path, bound=None, num_segments=32): vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) max_frame = len(vr) - 1 fps = float(vr.get_avg_fps()) pixel_values_list, num_patches_list = [], [] image_processor = CLIPImageProcessor.from_pretrained(path) frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments) for frame_index in frame_indices: img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB').resize((448, 448)) pixel_values = image_processor(images=img, return_tensors='pt').pixel_values num_patches_list.append(pixel_values.shape[0]) pixel_values_list.append(pixel_values) pixel_values = torch.cat(pixel_values_list) return pixel_values, num_patches_list path = "OpenGVLab/InternVL-Chat-V1-2-Plus" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) generation_config = dict(max_new_tokens=1024, do_sample=True) video_path = './examples/red-panda.mp4' pixel_values, num_patches_list = load_video(video_path, num_segments=8) pixel_values = pixel_values.to(torch.bfloat16).cuda() video_prefix = ''.join([f'Frame{i+1}: \n' for i in range(len(num_patches_list))]) question = video_prefix + 'What is the red panda doing?' # Frame1: \nFrame2: \n...\nFrame8: \n{question} response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=None, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') question = 'Describe this video in detail.' response, history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=history, return_history=True) print(f'User: {question}') print(f'Assistant: {response}') ``` #### Streaming Output Besides this method, you can also use the following code to get streamed output. ```python from transformers import TextIteratorStreamer from threading import Thread # Initialize the streamer streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10) # Define the generation configuration generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer) # Start the model chat in a separate thread thread = Thread(target=model.chat, kwargs=dict( tokenizer=tokenizer, pixel_values=pixel_values, question=question, history=None, return_history=False, generation_config=generation_config, )) thread.start() # Initialize an empty string to store the generated text generated_text = '' # Loop through the streamer to get the new text as it is generated for new_text in streamer: if new_text == model.conv_template.sep: break generated_text += new_text print(new_text, end='', flush=True) # Print each new chunk of generated text on the same line ``` ## License This project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses. ## Citation If you find this project useful in your research, please consider citing: ```BibTeX @article{chen2024expanding, title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling}, author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others}, journal={arXiv preprint arXiv:2412.05271}, year={2024} } @article{gao2024mini, title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance}, author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others}, journal={arXiv preprint arXiv:2410.16261}, year={2024} } @article{chen2024far, title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, 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}, journal={arXiv preprint arXiv:2404.16821}, year={2024} } @inproceedings{chen2024internvl, title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks}, 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 others}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={24185--24198}, year={2024} } ```