Ovis2 1B Dev

🚀 Ovis2-1B

Ovis2-1B是一款多模態大語言模型，繼承了Ovis系列的創新架構設計，在數據集管理和訓練方法上有顯著改進，具備小模型高性能、增強推理能力、支持視頻和多圖像處理以及多語言OCR等特性。

🚀 快速開始

你可以按照以下步驟使用Ovis2-1B模型：

pip install torch==2.4.0 transformers==4.46.2 numpy==1.25.0 pillow==10.3.0
pip install flash-attn==2.7.0.post2 --no-build-isolation

import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# 加載模型
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-1B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=32768,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()

# 單圖像輸入
image_path = '/data/images/example_1.jpg'
images = [Image.open(image_path)]
max_partition = 9
text = 'Describe the image.'
query = f'<image>\n{text}'

## 思維鏈風格輸入
# cot_suffix = "Provide a step-by-step solution to the problem, and conclude with 'the answer is' followed by the final solution."
# image_path = '/data/images/example_1.jpg'
# images = [Image.open(image_path)]
# max_partition = 9
# text = "What's the area of the shape?"
# query = f'<image>\n{text}\n{cot_suffix}'

## 多圖像輸入
# image_paths = [
#     '/data/images/example_1.jpg',
#     '/data/images/example_2.jpg',
#     '/data/images/example_3.jpg'
# ]
# images = [Image.open(image_path) for image_path in image_paths]
# max_partition = 4
# text = 'Describe each image.'
# query = '\n'.join([f'Image {i+1}: <image>' for i in range(len(images))]) + '\n' + text

## 視頻輸入 (需要 `pip install moviepy==1.0.3`)
# from moviepy.editor import VideoFileClip
# video_path = '/data/videos/example_1.mp4'
# num_frames = 12
# max_partition = 1
# text = 'Describe the video.'
# with VideoFileClip(video_path) as clip:
#     total_frames = int(clip.fps * clip.duration)
#     if total_frames <= num_frames:
#         sampled_indices = range(total_frames)
#     else:
#         stride = total_frames / num_frames
#         sampled_indices = [min(total_frames - 1, int((stride * i + stride * (i + 1)) / 2)) for i in range(num_frames)]
#     frames = [clip.get_frame(index / clip.fps) for index in sampled_indices]
#     frames = [Image.fromarray(frame, mode='RGB') for frame in frames]
# images = frames
# query = '\n'.join(['<image>'] * len(images)) + '\n' + text

## 純文本輸入
# images = []
# max_partition = None
# text = 'Hello'
# query = text

# 格式化對話
prompt, input_ids, pixel_values = model.preprocess_inputs(query, images, max_partition=max_partition)
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
input_ids = input_ids.unsqueeze(0).to(device=model.device)
attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
if pixel_values is not None:
    pixel_values = pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)
pixel_values = [pixel_values]

# 生成輸出
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
    output = text_tokenizer.decode(output_ids, skip_special_tokens=True)
    print(f'輸出:\n{output}')

import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# 加載模型
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-1B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=32768,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()

# 預處理輸入
batch_inputs = [
    ('/data/images/example_1.jpg', 'What colors dominate the image?'),
    ('/data/images/example_2.jpg', 'What objects are depicted in this image?'),
    ('/data/images/example_3.jpg', 'Is there any text in the image?')
]

batch_input_ids = []
batch_attention_mask = []
batch_pixel_values = []

for image_path, text in batch_inputs:
    image = Image.open(image_path)
    query = f'<image>\n{text}'
    prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image], max_partition=9)
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    batch_input_ids.append(input_ids.to(device=model.device))
    batch_attention_mask.append(attention_mask.to(device=model.device))
    batch_pixel_values.append(pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device))

batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids], batch_first=True,
                                                  padding_value=0.0).flip(dims=[1])
batch_input_ids = batch_input_ids[:, -model.config.multimodal_max_length:]
batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask],
                                                       batch_first=True, padding_value=False).flip(dims=[1])
batch_attention_mask = batch_attention_mask[:, -model.config.multimodal_max_length:]

# 生成輸出
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(batch_input_ids, pixel_values=batch_pixel_values, attention_mask=batch_attention_mask,
                                **gen_kwargs)

for i in range(len(batch_inputs)):
    output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True)
    print(f'輸出 {i + 1}:\n{output}\n')

✨ 主要特性

• 小模型高性能：優化的訓練策略使小模型實現更高的能力密度，展現跨層級的領先優勢。
• 增強推理能力：通過指令微調與偏好學習相結合，顯著增強思維鏈（CoT）推理能力。
• 視頻和多圖像處理：將視頻和多圖像數據納入訓練，增強處理跨幀和圖像的複雜視覺信息的能力。
• 多語言支持和OCR：增強英語和中文以外的多語言OCR能力，改進從表格和圖表等複雜視覺元素中提取結構化數據的能力。

📦 安裝指南

pip install torch==2.4.0 transformers==4.46.2 numpy==1.25.0 pillow==10.3.0
pip install flash-attn==2.7.0.post2 --no-build-isolation

💻 使用示例

基礎用法

import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# 加載模型
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-1B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=32768,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()

# 單圖像輸入
image_path = '/data/images/example_1.jpg'
images = [Image.open(image_path)]
max_partition = 9
text = 'Describe the image.'
query = f'<image>\n{text}'

# 格式化對話
prompt, input_ids, pixel_values = model.preprocess_inputs(query, images, max_partition=max_partition)
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
input_ids = input_ids.unsqueeze(0).to(device=model.device)
attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
if pixel_values is not None:
    pixel_values = pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)
pixel_values = [pixel_values]

# 生成輸出
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
    output = text_tokenizer.decode(output_ids, skip_special_tokens=True)
    print(f'輸出:\n{output}')

高級用法

# 批量推理示例
import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# 加載模型
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis2-1B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=32768,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()

# 預處理輸入
batch_inputs = [
    ('/data/images/example_1.jpg', 'What colors dominate the image?'),
    ('/data/images/example_2.jpg', 'What objects are depicted in this image?'),
    ('/data/images/example_3.jpg', 'Is there any text in the image?')
]

batch_input_ids = []
batch_attention_mask = []
batch_pixel_values = []

for image_path, text in batch_inputs:
    image = Image.open(image_path)
    query = f'<image>\n{text}'
    prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image], max_partition=9)
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    batch_input_ids.append(input_ids.to(device=model.device))
    batch_attention_mask.append(attention_mask.to(device=model.device))
    batch_pixel_values.append(pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device))

batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids], batch_first=True,
                                                  padding_value=0.0).flip(dims=[1])
batch_input_ids = batch_input_ids[:, -model.config.multimodal_max_length:]
batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask],
                                                       batch_first=True, padding_value=False).flip(dims=[1])
batch_attention_mask = batch_attention_mask[:, -model.config.multimodal_max_length:]

# 生成輸出
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(batch_input_ids, pixel_values=batch_pixel_values, attention_mask=batch_attention_mask,
                                **gen_kwargs)

for i in range(len(batch_inputs)):
    output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True)
    print(f'輸出 {i + 1}:\n{output}\n')

📚 詳細文檔

模型庫

性能評估

我們使用 VLMEvalKit 對Ovis2進行評估，該工具也用於OpenCompass 多模態 和 推理 排行榜。

圖像基準測試

視頻基準測試

📄 許可證

本項目採用 Apache許可證2.0版（SPDX許可證標識符：Apache-2.0）。

📚 引用

如果你發現Ovis模型有用，請考慮引用以下論文：

@article{lu2024ovis,
  title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model},
  author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye},
  year={2024},
  journal={arXiv:2405.20797}
}

⚠️ 免責聲明

我們在訓練過程中使用了合規檢查算法，以盡力確保訓練模型的合規性。由於數據的複雜性和語言模型使用場景的多樣性，我們不能保證模型完全沒有版權問題或不當內容。如果你認為有任何內容侵犯了你的權利或產生了不當內容，請聯繫我們，我們將及時處理。