4月11日，OpenGVLab 开源发布了 InternVL3系列模型，包括从1B 到 78B 共 7 个尺寸，作为一款先进的多模态大型语言模型 (MLLM) ，能够同时处理文字、图片、视频等多种信息，展现出卓越的整体性能。与 InternVL 2.5 相比，InternVL3 展现出卓越的多模态感知和推理能力，同时进一步扩展了其多模态能力，涵盖工具使用、GUI 代理、工业图像分析、3D 视觉感知等。此外，得益于原生多模态预训练，InternVL3 系列的整体文本性能甚至优于 Qwen2.5 系列（后者是 InternVL3 中语言组件的初始化部分）。
一.模型架构
如图所示，InternVL3保留了与InternVL 2.5及其前代产品 InternVL 1.5 和 2.0相同的模型架构，遵循“ViT-MLP-LLM”范式。InternVL3用随机初始化的 MLP projector，将全新增量预训练的 InternViT 与各种预训练的 LLM（包括 InternLM 3 和 Qwen 2.5）集成。
与上一版本一样，InternVL应用了像素反混洗操作，将视觉标记的数量减少到原来的四分之一。此外，InternVL3采用了与 InternVL 1.5 类似的动态分辨率策略，将图像划分为 448×448 像素的图块。从 InternVL 2.0 开始，关键区别在于额外引入了对多图像和视频数据的支持。
值得注意的是，在 InternVL3 中，我们集成了可变视觉位置编码 (V2PE)，它为视觉标记提供了更小、更灵活的位置增量。得益于 V2PE，InternVL3 相比前代产品展现出更出色的长上下文理解能力。
二.模型推理
版本要求：transformers>=4.37.2
import math import numpy as np import torch import torchvision.transforms as T from decord import VideoReader, cpu from PIL import Image from torchvision.transforms.functional import InterpolationMode from modelscope import AutoModel, AutoTokenizer IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD transform = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD) ]) return transform def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (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 i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def load_image(image_file, input_size=448, max_num=12): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values def split_model(model_name): device_map = {} world_size = torch.cuda.device_count() config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) num_layers = config.llm_config.num_hidden_layers # 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.model.rotary_emb'] = 0 device_map['language_model.lm_head'] = 0 device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 return device_map # If you set `load_in_8bit=True`, you will need two 80GB GPUs. # If you set `load_in_8bit=False`, you will need at least three 80GB GPUs. path = 'OpenGVLab/InternVL3-1B' model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, load_in_8bit=False, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True, device_map="auto").eval() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) # set the max number of tiles in `max_num` pixel_values = load_image('./example/image1.jpg', max_num=12).to(torch.bfloat16).cuda() generation_config = dict(max_new_tokens=1024, do_sample=True) # pure-text conversation (纯文本对话) question = 'Hello, who are you?' response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True) print(f'User: {question}\nAssistant: {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}\nAssistant: {response}') # single-image single-round conversation (单图单轮对话) question = '<image>\nPlease describe the image shortly.' response = model.chat(tokenizer, pixel_values, question, generation_config) print(f'User: {question}\nAssistant: {response}') # single-image multi-round conversation (单图多轮对话) question = '<image>\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}\nAssistant: {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}\nAssistant: {response}') # multi-image multi-round conversation, combined images (多图多轮对话，拼接图像) pixel_values1 = load_image('./example/image1.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values2 = load_image('./example/image2.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) question = '<image>\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}\nAssistant: {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}\nAssistant: {response}') # multi-image multi-round conversation, separate images (多图多轮对话，独立图像) pixel_values1 = load_image('./example/image1.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values2 = load_image('./example/image2.jpg', max_num=12).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)] question = 'Image-1: <image>\nImage-2: <image>\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}\nAssistant: {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}\nAssistant: {response}') # batch inference, single image per sample (单图批处理) pixel_values1 = load_image('./example/image1.jpg', max_num=12).to(torch.bfloat16).cuda() pixel_values2 = load_image('./example/image2.jpg', max_num=12).to(torch.bfloat16).cuda() num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)] pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) questions = ['<image>\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}\nAssistant: {response}') # video multi-round conversation (视频多轮对话) 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, input_size=448, max_num=1, 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 = [], [] transform = build_transform(input_size=input_size) 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') img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(tile) for tile in img] pixel_values = torch.stack(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 video_path = './example/red-panda.mp4' 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 = ''.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...\nFrame8: <image>\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}\nAssistant: {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}\nAssistant: {response}')
显存占用：
三.模型部署
环境安装
pip install lmdeploy>=0.7.3
使用LMDeploy's api_server 部署成OpenAI兼容API
modelscope download --model=OpenGVLab/InternVL3-1B --local_dir ./InternVL3-1B lmdeploy serve api_server ./InternVL3-1B --server-port 23333 --tp 1 # 如果lmdeploy<0.7.3, 使用如下命令 # lmdeploy serve api_server ./InternVL3-1B --chat-template internvl2_5 --server-port 23333 --tp 1
模型调用
from openai import OpenAI client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1') model_name = client.models.list().data[0].id response = client.chat.completions.create( model=model_name, messages=[{ 'role': 'user', 'content': [{ 'type': 'text', 'text': 'describe this image', }, { 'type': 'image_url', 'image_url': { 'url': 'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg', }, }], }], temperature=0.8, top_p=0.8) print(response)
四.模型微调
ms-swift已经支持了InternVL3系列模型的微调。ms-swift是魔搭社区官方提供的大模型与多模态大模型训练部署框架。
我们将展示可运行的微调demo，并给出自定义数据集的格式。
在开始微调之前，请确保您的环境已准备妥当。
# pip install git+https://github.com/modelscope/ms-swift.git git clone https://github.com/modelscope/ms-swift.git cd ms-swift pip install -e .
以 InternVL3-8B模型为例，使用OCR图像数据集训练，微调脚本如下：
CUDA_VISIBLE_DEVICES=0 \ swift sft \ --model OpenGVLab/InternVL3-8B \ --dataset 'AI-ModelScope/LaTeX_OCR:human_handwrite#20000' \ --train_type lora \ --torch_dtype bfloat16 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --learning_rate 1e-4 \ --gradient_accumulation_steps 16 \ --eval_steps 200 \ --save_steps 200 \ --save_total_limit 5 \ --logging_steps 5 \ --max_length 2048 \ --output_dir output \ --warmup_ratio 0.05 \ --dataloader_num_workers 4
训练显存占用：
如果要使用自定义数据集进行训练，你可以参考以下格式，并指定`--dataset <dataset_path>`。
{"messages": [{"role": "user", "content": "<image><image>两张图片有什么区别"}, {"role": "assistant", "content": "前一张是小猫，后一张是小狗"}], "images": ["/xxx/x.jpg", "/xxx/x.png"]}
训练完成后，使用以下命令对训练后的权重进行推理，这里的`--adapters`需要替换成训练生成的last checkpoint文件夹。
CUDA_VISIBLE_DEVICES=0 \ swift infer \ --adapters output/vx-xxx/checkpoint-xxx \ --stream false \ --max_batch_size 1 \ --load_data_args true \ --max_new_tokens 2048
推送模型到ModelScope：
CUDA_VISIBLE_DEVICES=0 \ swift export \ --adapters output/vx-xxx/checkpoint-xxx \ --push_to_hub true \ --hub_model_id '<your-model-id>' \ --hub_token '<your-sdk-token>'
