Internvl3 1B Instruct

🚀 InternVL3-1B-Instruct

InternVL3-1B-Instruct 是 InternVL3 系列的指令微調版本，這是一個先進的多模態大語言模型（MLLM），在多模態感知、推理等能力上表現出色，還拓展到工具使用、GUI 代理等更多領域。

[📂 GitHub] [📜 InternVL 1.0] [📜 InternVL 1.5] [📜 InternVL 2.5] [📜 InternVL2.5-MPO] [📜 InternVL3]

[🆕 Blog] [🗨️ Chat Demo] [🤗 HF Demo] [🚀 Quick Start] [📖 Documents]

✨ 主要特性

• 多模態能力卓越：相比 InternVL 2.5，InternVL3 在多模態感知和推理能力上更出色，還拓展到工具使用、GUI 代理、工業圖像分析、3D 視覺感知等領域。
• 文本性能優秀：通過原生多模態預訓練，InternVL3 系列在整體文本性能上比 Qwen2.5 系列更優。
• 長上下文理解能力強：集成了可變視覺位置編碼（V2PE），使用更小、更靈活的位置增量處理視覺標記，提升了長上下文理解能力。

📦 安裝指南

使用 transformers 庫運行 InternVL3-1B

# 請使用 transformers>=4.37.2 以確保模型正常工作
pip install transformers>=4.37.2

安裝 lmdeploy 進行部署

# 如果 lmdeploy<0.7.3，需要顯式設置 chat_template_config=ChatTemplateConfig(model_name='internvl2_5')
pip install lmdeploy>=0.7.3

安裝 openai 以使用 OpenAI 風格接口

pip install openai

💻 使用示例

基礎用法

import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3-1B"
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    use_flash_attn=True,
    trust_remote_code=True).eval().cuda()

高級用法

多 GPU 推理

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
    # 由於第一個 GPU 將用於 ViT，將其視為半個 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/InternVL3-1B"
device_map = split_model('InternVL3-1B')
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()

推理示例

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

    # 計算現有圖像的寬高比
    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])

    # 找到最接近目標的寬高比
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # 計算目標寬度和高度
    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]

    # 調整圖像大小
    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
        )
        # 分割圖像
        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
    # 由於第一個 GPU 將用於 ViT，將其視為半個 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

# 如果你設置 `load_in_8bit=True`，你需要兩個 80GB 的 GPU。
# 如果你設置 `load_in_8bit=False`，你至少需要三個 80GB 的 GPU。
path = 'OpenGVLab/InternVL3-1B'
device_map = split_model('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=device_map).eval()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

# 設置 `max_num` 中的最大圖塊數
pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)

# 純文本對話
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}')

# 單圖單輪對話
question = '<image>\nPlease describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')

# 單圖多輪對話
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}')

# 多圖多輪對話，拼接圖像
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}')

# 多圖多輪對話，獨立圖像
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}')

# 單圖批處理
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}')

# 視頻多輪對話
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}')

流式輸出

from transformers import TextIteratorStreamer
from threading import Thread

# 初始化流處理器
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10)
# 定義生成配置
generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer)
# 在單獨的線程中啟動模型對話
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()

# 初始化一個空字符串來存儲生成的文本
generated_text = ''
# 遍歷流處理器以獲取生成的新文本
for new_text in streamer:
    if new_text == model.conv_template.sep:
        break
    generated_text += new_text
    print(new_text, end='', flush=True)  # 在同一行打印每個新生成的文本塊

📚 詳細文檔

InternVL3 家族

模型名稱	視覺部分	語言部分	Hugging Face 鏈接
InternVL3-1B	InternViT-300M-448px-V2_5	Qwen2.5-0.5B	🤗 link
InternVL3-2B	InternViT-300M-448px-V2_5	Qwen2.5-1.5B	🤗 link
InternVL3-8B	InternViT-300M-448px-V2_5	Qwen2.5-7B	🤗 link
InternVL3-9B	InternViT-300M-448px-V2_5	internlm3-8b-instruct	🤗 link
InternVL3-14B	InternViT-300M-448px-V2_5	Qwen2.5-14B	🤗 link
InternVL3-38B	InternViT-6B-448px-V2_5	Qwen2.5-32B	🤗 link
InternVL3-78B	InternViT-6B-448px-V2_5	Qwen2.5-72B	🤗 link

模型架構

InternVL3 保留了與 InternVL 2.5 及其前身（InternVL 1.5 和 2.0）相同的模型架構，遵循“ViT-MLP-LLM”範式。在新版本中，使用隨機初始化的 MLP 投影器，將新的增量預訓練的 InternViT 與各種預訓練的大語言模型（LLM）集成，包括 InternLM 3 和 Qwen 2.5。

訓練策略

原生多模態預訓練

提出了一種原生多模態預訓練方法，將語言和視覺學習整合到一個預訓練階段。與先訓練純語言模型再適應其他模態的標準範式不同，該方法將多模態數據（如圖文、視頻文本或圖文交錯序列）與大規模文本語料交織。這種統一的訓練方案使模型能夠同時學習語言和多模態表示，最終增強其處理視覺語言任務的能力，無需單獨的對齊或橋接模塊。

監督微調

在這個階段，採用了 InternVL2.5 中提出的隨機 JPEG 壓縮、平方損失重新加權和多模態數據打包技術。與 InternVL2.5 相比，InternVL3 的監督微調階段的主要進步在於使用了更高質量和更多樣化的訓練數據。

混合偏好優化

在預訓練和監督微調期間，模型基於先前的真實標記預測下一個標記。然而，在推理期間，模型根據自己的先前輸出預測每個標記。這種真實標記和模型預測標記之間的差異會引入分佈偏移，從而損害模型的思維鏈（CoT）推理能力。為緩解這個問題，採用了 MPO，它引入了來自正樣本和負樣本的額外監督，使模型響應分佈與真實分佈對齊，從而提高推理性能。

測試時縮放

測試時縮放已被證明是增強大語言模型（LLM）和多模態大語言模型（MLLM）推理能力的有效方法。在這項工作中，使用了 Best-of-N 評估策略，並採用 VisualPRM-8B 作為評估模型，為推理和數學評估選擇最佳響應。

評估

多模態能力評估

包括多模態推理和數學、OCR、圖表和文檔理解、多圖像和現實世界理解、綜合多模態和幻覺評估、視覺定位、多模態多語言理解、視頻理解、GUI 定位和空間推理等方面的評估。

語言能力評估

將 InternVL3 與 Qwen2.5 聊天模型進行比較，由於原生多模態預訓練，InternVL3 系列在整體文本性能上比 Qwen2.5 系列更優。

消融研究

原生多模態預訓練

在 InternVL2-8B 模型上進行實驗，保持其架構、初始化參數和訓練數據完全不變。將傳統的 MLP 預熱階段替換為原生多模態預訓練過程，隔離了原生多模態預訓練對模型整體多模態能力的貢獻。評估結果表明，經過原生多模態預訓練的模型在大多數基準測試中的性能與經過完整多階段訓練的 InternVL2-8B 基線相當。

混合偏好優化

如表所示，使用 MPO 進行微調的模型在七個多模態推理基準測試中比未使用 MPO 的模型表現更優。

可變視覺位置編碼

引入 V2PE 導致大多數評估指標的性能顯著提升。此外，消融研究表明，即使對於主要涉及傳統上下文的任務，相對較小的位置增量值也能實現最佳性能。

🔧 技術細節

模型架構

• ViT-MLP-LLM 範式：InternVL3 遵循“ViT-MLP-LLM”範式，將視覺特徵提取（ViT）、特徵投影（MLP）和語言生成（LLM）相結合。
• 像素重排操作：應用像素重排操作，將視覺標記數量減少到原來的四分之一。
• 動態分辨率策略：採用與 InternVL 1.5 類似的動態分辨率策略，將圖像分割成 448×448 像素的圖塊。
• 多圖像和視頻支持：從 InternVL 2.0 開始，增加了對多圖像和視頻數據的支持。
• 可變視覺位置編碼（V2PE）：集成了 V2PE，使用更小、更靈活的位置增量處理視覺標記，提升了長上下文理解能力。

訓練策略

• 原生多模態預訓練：將語言和視覺學習整合到一個預訓練階段，增強了模型處理視覺語言任務的能力。
• 監督微調：採用隨機 JPEG 壓縮、平方損失重新加權和多模態數據打包技術，使用更高質量和更多樣化的訓練數據。
• 混合偏好優化（MPO）：引入額外的監督，使模型響應分佈與真實分佈對齊，提高推理性能。
• 測試時縮放：使用 Best-of-N 評估策略和 VisualPRM-8B 作為評估模型，選擇最佳響應。

🚀 快速開始

模型加載

16 位（bf16 / fp16）

import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3-1B"
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/InternVL3-1B"
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()

推理示例

# 請參考上面的使用示例部分

微調

許多倉庫現在支持對 InternVL 系列模型進行微調，包括 InternVL、SWIFT、XTurner 等。請參考它們的文檔以獲取更多關於微調的詳細信息。

部署

使用 lmdeploy 進行部署

一個簡單示例

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

model = 'OpenGVLab/InternVL3-1B'
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=1), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
response = pipe(('describe this image', image))
print(response.text)

多圖像推理

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

model = 'OpenGVLab/InternVL3-1B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), 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]
# 為圖像編號有助於多圖像對話
response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images))
print(response.text)

批量提示推理

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

model = 'OpenGVLab/InternVL3-1B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), 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)

多輪對話

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

model = 'OpenGVLab/InternVL3-1B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), 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)

服務部署

lmdeploy serve api_server OpenGVLab/InternVL3-1B --chat-template internvl2_5 --server-port 23333 --tp 1

使用 OpenAI 風格接口

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)

📄 許可證

本項目遵循 MIT 許可證。本項目使用了預訓練的 Qwen2.5 作為組件，該組件遵循 Apache-2.0 許可證。

引用

如果您在研究中發現本項目有用，請考慮引用以下文獻：

@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}
}