Internvl3 78B Pretrained

🚀 InternVL3-78B预训练模型

InternVL3-78B预训练模型是一款先进的多模态大语言模型，在多模态感知、推理等能力上表现卓越，还拓展了工具使用、GUI代理等多模态能力，且文本性能也十分出色。

🚀 快速开始

我们提供了使用 transformers 运行 InternVL3-78B 的示例代码。

⚠️ 重要提示

请使用 transformers>=4.37.2 以确保模型正常工作。

模型加载

16位（bf16 / fp16）

import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3-78B"
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位量化

import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3-78B"
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()

多GPU

以下代码写法是为了避免多GPU推理时因张量不在同一设备而出现的错误。通过确保大语言模型（LLM）的第一层和最后一层在同一设备上，可防止此类错误。

import math
import torch
from transformers import AutoTokenizer, AutoModel

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

path = "OpenGVLab/InternVL3-78B"
device_map = split_model('InternVL3-78B')
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()

使用Transformers进行推理

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 transformers 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/InternVL3-78B'
device_map = split_model('InternVL3-78B')
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=device_map).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('./examples/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('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/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('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/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('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/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 = './examples/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}')

流式输出

除上述方法外，你还可以使用以下代码获取流式输出。

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

✨ 主要特性

• 卓越的多模态能力：与InternVL 2.5相比，InternVL3展现出更出色的多模态感知和推理能力，还将多模态能力拓展到工具使用、GUI代理、工业图像分析、3D视觉感知等领域。
• 统一的预训练方法：提出原生多模态预训练方法，将语言和视觉学习整合到单个预训练阶段，使模型能同时学习语言和多模态表示，提升处理视觉 - 语言任务的能力。
• 更好的长上下文理解：集成可变视觉位置编码（V2PE），使用更小、更灵活的位置增量处理视觉标记，使InternVL3在长上下文理解方面表现更优。
• 优异的文本性能：得益于原生多模态预训练，InternVL3系列在整体文本性能上优于Qwen2.5系列。

📚 详细文档

模型架构

InternVL3 沿用了 InternVL 2.5 及其前身InternVL 1.5和2.0的模型架构，遵循“ViT - MLP - LLM”范式。在新版本中，我们使用随机初始化的MLP投影器，将新的增量预训练的InternViT与包括InternLM 3和Qwen 2.5在内的各种预训练LLM集成在一起。

与之前版本一样，我们应用了像素重排操作，将视觉标记数量减少到原来的四分之一。此外，我们采用了与InternVL 1.5类似的动态分辨率策略，将图像分割成448×448像素的图块。从InternVL 2.0开始，关键的区别在于我们还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），它为视觉标记使用更小、更灵活的位置增量。得益于V2PE，InternVL3与之前的版本相比，表现出更好的长上下文理解能力。

训练策略

原生多模态预训练

我们提出了 原生多模态预训练 方法，将语言和视觉学习整合到单个预训练阶段。与先训练仅语言模型，然后使其适应处理其他模态的标准范式不同，我们的方法将多模态数据（如图像 - 文本、视频 - 文本或图像 - 文本交错序列）与大规模文本语料库交织在一起。这种统一的训练方案使模型能够同时学习语言和多模态表示，最终提高其处理视觉 - 语言任务的能力，而无需单独的对齐或桥接模块。更多细节请参阅 我们的论文。

监督微调

在这个阶段，InternVL2.5 中提出的随机JPEG压缩、平方损失重新加权和多模态数据打包技术也被应用于InternVL3系列。与InternVL2.5相比，InternVL3监督微调阶段的主要改进在于使用了更高质量和更多样化的训练数据。具体来说，我们进一步扩展了工具使用、3D场景理解、GUI操作、长上下文任务、视频理解、科学图表、创意写作和多模态推理的训练样本。

混合偏好优化

在预训练和监督微调期间，模型根据先前的真实标记来预测下一个标记。然而，在推理期间，模型根据其自身的先前输出预测每个标记。这种真实标记和模型预测标记之间的差异会引入分布偏移，这可能会损害模型的思维链（CoT）推理能力。为了缓解这个问题，我们采用了 MPO，它引入了来自正样本和负样本的额外监督，以使模型响应分布与真实分布对齐，从而提高推理性能。具体来说，MPO的训练目标是偏好损失 \(\mathcal{L}{\text{p}}\)、质量损失 \(\mathcal{L}{\text{q}}\) 和生成损失 \(\mathcal{L}_{\text{g}}\) 的组合，可以表述如下：

$$ \mathcal{L}=w_{p}\cdot\mathcal{L}{\text{p}} + w{q}\cdot\mathcal{L}{\text{q}} + w{g}\cdot\mathcal{L}_{\text{g}}, $$

其中 \(w_{*}\) 表示分配给每个损失组件的权重。有关MPO的更多细节，请参阅 我们的论文。

测试时缩放

测试时缩放已被证明是提高大语言模型（LLM）和多模态大语言模型（MLLM）推理能力的有效方法。在这项工作中，我们使用Best - of - N评估策略，并使用 [VisualPRM - 8B](https://huggingface.co/OpenGVLab/VisualPRM - 8B) 作为评判模型，为推理和数学评估选择最佳响应。

多模态能力评估

• 多模态推理和数学：展示了模型在多模态推理和数学任务上的性能。
• OCR、图表和文档理解：评估了模型对OCR、图表和文档的理解能力。
• 多图像和现实世界理解：体现了模型对多图像和现实世界场景的理解能力。
• 综合多模态和幻觉评估：对模型的综合多模态能力和幻觉情况进行评估。
• 视觉定位：展示了模型的视觉定位能力。
• 多模态多语言理解：评估了模型在多模态多语言任务上的理解能力。
• 视频理解：体现了模型对视频的理解能力。
• GUI定位：展示了模型在GUI定位任务上的表现。
• 空间推理：评估了模型的空间推理能力。

语言能力评估

我们将InternVL3与Qwen2.5聊天模型进行了比较，Qwen2.5的对应预训练基础模型被用作InternVL3语言组件的初始化。得益于原生多模态预训练，InternVL3系列在整体文本性能上优于Qwen2.5系列。请注意，Qwen2.5系列的评估分数可能与官方报告的不同，因为我们在所有数据集上都采用了表中提供的提示版本进行OpenCompass评估。

消融研究

原生多模态预训练

我们在InternVL2 - 8B模型上进行了实验，同时保持其架构、初始化参数和训练数据完全不变。传统上，InternVL2 - 8B采用的训练流程是先进行MLP预热阶段以进行特征对齐，然后进行指令微调阶段。在我们的实验中，我们用原生多模态预训练过程取代了传统的MLP预热阶段。这种修改隔离了原生多模态预训练对模型整体多模态能力的贡献。

下图的评估结果表明，采用原生多模态预训练的模型在大多数基准测试中的性能与经过完整多阶段训练的InternVL2 - 8B基线相当。此外，当在更高质量的数据上进行指令微调后，该模型在评估的多模态任务中表现出进一步的性能提升。这些发现强调了原生多模态预训练在赋予多模态大语言模型强大多模态能力方面的效率。

混合偏好优化

如下表所示，与未采用MPO的模型相比，采用MPO进行微调的模型在七个多模态推理基准测试中表现出更优的推理性能。具体来说，InternVL3 - 78B和InternVL3 - 38B分别比其对应模型高出4.1和4.5分。值得注意的是，用于MPO的训练数据是用于监督微调的训练数据的子集，这表明性能提升主要源于训练算法而非训练数据。

可变视觉位置编码

如下表所示，引入V2PE导致大多数评估指标的性能显著提升。此外，我们通过改变位置增量 \( \delta \) 进行的消融研究表明，即使对于主要涉及常规上下文的任务，相对较小的 \( \delta \) 值也能实现最佳性能。这些发现为未来改进多模态大语言模型中视觉标记的位置编码策略提供了重要见解。

🔧 技术细节

模型家族

训练方法

采用原生多模态预训练、监督微调、混合偏好优化和测试时缩放等训练策略，提升模型的多模态能力和推理性能。

评估指标

通过多模态推理和数学、OCR、图表和文档理解、多图像和现实世界理解等多个方面的评估指标，全面衡量模型的性能。

📦 安装指南

微调

许多仓库现在支持对InternVL系列模型进行微调，包括 InternVL、[SWIFT](https://github.com/modelscope/ms - swift)、XTurner 等。有关微调的更多详细信息，请参考它们的文档。

部署

LMDeploy

LMDeploy是一个用于压缩、部署和服务大语言模型（LLM）和视觉语言模型（VLM）的工具包。

# if lmdeploy<0.7.3, you need to explicitly set chat_template_config=ChatTemplateConfig(model_name='internvl2_5')
pip install lmdeploy>=0.7.3

LMDeploy将多模态视觉 - 语言模型（VLM）的复杂推理过程抽象为一个易于使用的管道，类似于大语言模型（LLM）推理管道。

“Hello, world”示例

from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image

model = 'OpenGVLab/InternVL3-78B'
image = load_image('https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/tests/data/tiger.jpeg')
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
response = pipe(('describe this image', image))
print(response.text)

如果在执行此示例时出现 ImportError，请按提示安装所需的依赖包。

多图像推理

处理多图像时，你可以将它们全部放在一个列表中。请记住，多图像会导致输入标记数量增加，因此通常需要增加上下文窗口的大小。

from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image
from lmdeploy.vl.constants import IMAGE_TOKEN

model = 'OpenGVLab/InternVL3-78B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))

image_urls=[
    'https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/demo/resources/human - pose.jpg',
    'https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/demo/resources/det.jpg'
]

images = [load_image(img_url) for img_url in image_urls]
# Numbering images improves multi - image conversations
response = pipe(('describe these two images', images))
print(response.text)

批量提示推理

进行批量提示推理非常简单，只需将它们放在一个列表结构中：

from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image

model = 'OpenGVLab/InternVL3-78B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))

image_urls=[
    "https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/demo/resources/human - pose.jpg",
    "https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/demo/resources/det.jpg"
]
prompts = [('describe this image', load_image(img_url)) for img_url in image_urls]
response = pipe(prompts)
print(response)

多轮对话

使用管道进行多轮对话有两种方法。一种是根据OpenAI的格式构造消息并使用上述介绍的方法，另一种是使用 pipeline.chat 接口。

from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig, ChatTemplateConfig
from lmdeploy.vl import load_image

model = 'OpenGVLab/InternVL3-78B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))

image = load_image('https://raw.githubusercontent.com/open - mmlab/mmdeploy/main/demo/resources/human - pose.jpg')
gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
sess = pipe.chat(('describe this image', image), gen_config=gen_config)
print(sess.response.text)
sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config)
print(sess.response.text)

服务

LMDeploy的 api_server 使模型能够通过一个命令轻松打包成服务。提供的RESTful API与OpenAI的接口兼容。以下是一个服务启动示例：

lmdeploy serve api_server OpenGVLab/InternVL3-78B --chat - template internvl2_5 --server - port 23333 --tp 4

要使用OpenAI风格的接口，你需要安装OpenAI：

pip install openai

然后，使用以下代码进行API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)

📄 许可证

本项目根据MIT许可证发布。本项目使用预训练的Qwen2.5作为组件，Qwen2.5根据Qwen许可证授权。

引用

如果您发现本项目在您的研究中有用，请考虑引用：

@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{wang2024mpo,
  title={Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization},
  author={Wang, Weiyun and Chen, Zhe and Wang, Wenhai and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Zhu, Jinguo and Zhu, Xizhou and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2411.10442},
  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}
}