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---
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license: cc-by-nc-4.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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base_model:
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- google/paligemma-3b-mix-448
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- Qwen/Qwen2.5-7B-Instruct
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- google/siglip-so400m-patch14-384
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- timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k
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base_model_relation: merge
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language:
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- multilingual
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tags:
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- eagle
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- VLM
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---
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# Eagle-2
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[\[📂 GitHub\]](https://github.com/NVlabs/EAGLE) [\[📜 Eagle2 Tech Report\]](https://github.com/NVlabs/EAGLE/blob/main/Eagle2/Eagle2_report.pdf)
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[\[🗨️ Chat Demo\]](http://eagle-vlm.xyz/) [\[🤗 HF Demo\]](TODO)
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## Introduction
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We are thrilled to release our latest Eagle2 series Vision-Language Model. Open-source Vision-Language Models (VLMs) have made significant strides in narrowing the gap with proprietary models. However, critical details about data strategies and implementation are often missing, limiting reproducibility and innovation. In this project, we focus on VLM post-training from a data-centric perspective, sharing insights into building effective data strategies from scratch. By combining these strategies with robust training recipes and model design, we introduce Eagle2, a family of performant VLMs. Our work aims to empower the open-source community to develop competitive VLMs with transparent processes.
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In this repo, we are open-sourcing Eagle2-9B, which strikes the perfect balance between performance and inference speed.
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## Model Zoo
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We provide the following models:
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| model name | LLM | Vision | Max Length| HF Link|
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| ----------- | ------- |---------|-|-|
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| Eagle2-1B | [Qwen2.5-0.5B-Instruct](https://huggingface.co./Qwen/Qwen2.5-0.5B-Instruct) | Siglip | 16K| [🤗 link](https://huggingface.co./NVIDIA/Eagle2-1B)|
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| Eagle2-2B | [Qwen2.5-1.5B-Instruct](https://huggingface.co./Qwen/Qwen2.5-1.5B-Instruct) | Siglip | 16K| [🤗 link](https://huggingface.co./NVIDIA/Eagle2-2B)|
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| Eagle2-9B | [Qwen2.5-7B-Instruct](https://huggingface.co./Qwen/Qwen2.5-7B-Instruct) | Siglip+ConvNext | 16K| [🤗 link](https://huggingface.co./NVIDIA/Eagle2-9B)|
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## Benchmark Results
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| Benchmark | MiniCPM-Llama3-V-2_5 | InternVL-Chat-V1-5 | InternVL2-8B |QwenVL2-7B| Eagle2-9B|
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| :--------------------------: | :------------------: | :----------------: | :----------: |:----------: |:----------: |
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| Model Size | 8.5B | 25.5B | 8.1B | 8.3B|8.9B|
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| | | | | | |
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| DocVQA<sub>test</sub> | 84.8 | 90.9 | 91.6 |**94.5**|92.6|
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| ChartQA<sub>test</sub> | - | 83.8 | 83.3 |83.0|**86.4**|
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| InfoVQA<sub>test</sub> | - | 72.5 | 74.8 |74.3|**77.2**|
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| TextVQA<sub>val</sub> | 76.6 | 80.6 | 77.4 |**84.3**|83.0|
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| OCRBench | 725 | 724 | 794 |845|**868**|
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| MME<sub>sum</sub> | 2024.6 | 2187.8 | 2210.3 | **2326.8**|2260|
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| RealWorldQA | 63.5 | 66.0 | 64.4 |**70.1**|69.3|
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| AI2D<sub>test</sub> | 78.4 | 80.7 | 83.8 | - |**83.9**|
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| MMMU<sub>val</sub> | 45.8 | 45.2 / 46.8 | 49.3 / 51.8 |54.1|**56.1**|
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| MMBench_V11<sub>test</sub> | | | 79.5 |79.4|**80.6**|
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| MMVet<sub>GPT-4-Turbo</sub> | 52.8 | 55.4 | 54.2 | 62.0|**62.2**|
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| SEED-Image | 72.3 | 76.0 | 76.2 ||**77.1**|
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| HallBench<sub>avg</sub> | 42.4 | 49.3 | 45.2 |**50.6**|49.3
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| MathVista<sub>testmini</sub> | 54.3 | 53.5 | 58.3 |58.2|**63.8**|
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| MMstar | - | - | 60.9|60.7|**62.6**|
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## Quick Start
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We provide a [demo inference script](./demo.py) to help you quickly start using the model. We support different input types:
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- pure text input
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- single image input
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- multiple image input
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- video input
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### 0. Install the dependencies
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```bash
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pip install transformers==4.37.2
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pip install flash-attn
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```
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**Note**: Latest version of transformers is not compatible with the model.
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### 1. Prepare the Model worker
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<details>
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<summary>Click to expand</summary>
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```python
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"""
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A model worker executes the model.
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Copied and modified from https://github.com/OpenGVLab/InternVL/blob/main/streamlit_demo/model_worker.py
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"""
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# Importing torch before transformers can cause `segmentation fault`
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from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer, AutoConfig
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import argparse
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import base64
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import json
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import os
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import decord
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import threading
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import time
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from io import BytesIO
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from threading import Thread
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import math
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import requests
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import torch
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import torchvision.transforms as T
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from PIL import Image
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from torchvision.transforms.functional import InterpolationMode
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import numpy as np
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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SIGLIP_MEAN = (0.5, 0.5, 0.5)
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SIGLIP_STD = (0.5, 0.5, 0.5)
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def get_seq_frames(total_num_frames, desired_num_frames=-1, stride=-1):
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"""
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Calculate the indices of frames to extract from a video.
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Parameters:
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total_num_frames (int): Total number of frames in the video.
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desired_num_frames (int): Desired number of frames to extract.
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Returns:
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list: List of indices of frames to extract.
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"""
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assert desired_num_frames > 0 or stride > 0 and not (desired_num_frames > 0 and stride > 0)
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if stride > 0:
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return list(range(0, total_num_frames, stride))
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# Calculate the size of each segment from which a frame will be extracted
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seg_size = float(total_num_frames - 1) / desired_num_frames
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seq = []
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for i in range(desired_num_frames):
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# Calculate the start and end indices of each segment
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start = int(np.round(seg_size * i))
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end = int(np.round(seg_size * (i + 1)))
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# Append the middle index of the segment to the list
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seq.append((start + end) // 2)
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return seq
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def build_video_prompt(meta_list, num_frames, time_position=False):
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# if time_position is True, the frame_timestamp is used.
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# 1. pass time_position, 2. use env TIME_POSITION
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time_position = os.environ.get("TIME_POSITION", time_position)
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prefix = f"This is a video:\n"
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for i in range(num_frames):
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if time_position:
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frame_txt = f"Frame {i+1} sampled at {meta_list[i]:.2f} seconds: <image>\n"
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else:
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frame_txt = f"Frame {i+1}: <image>\n"
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prefix += frame_txt
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return prefix
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def load_video(video_path, num_frames=64, frame_cache_root=None):
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if isinstance(video_path, str):
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video = decord.VideoReader(video_path)
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elif isinstance(video_path, dict):
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assert False, 'we not support vidoe: "video_path" as input'
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fps = video.get_avg_fps()
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sampled_frames = get_seq_frames(len(video), num_frames)
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samepld_timestamps = [i / fps for i in sampled_frames]
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frames = video.get_batch(sampled_frames).asnumpy()
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images = [Image.fromarray(frame) for frame in frames]
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return images, build_video_prompt(samepld_timestamps, len(images), time_position=True)
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def load_image(image):
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if isinstance(image, str) and os.path.exists(image):
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return Image.open(image)
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elif isinstance(image, dict):
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if 'disk_path' in image:
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return Image.open(image['disk_path'])
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elif 'base64' in image:
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return Image.open(BytesIO(base64.b64decode(image['base64'])))
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elif 'url' in image:
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response = requests.get(image['url'])
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return Image.open(BytesIO(response.content))
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elif 'bytes' in image:
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return Image.open(BytesIO(image['bytes']))
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else:
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raise ValueError(f'Invalid image: {image}')
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else:
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raise ValueError(f'Invalid image: {image}')
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def build_transform(input_size, norm_type='imagenet'):
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if norm_type == 'imagenet':
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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elif norm_type == 'siglip':
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MEAN, STD = SIGLIP_MEAN, SIGLIP_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
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"""
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previous version mainly foucs on ratio.
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We also consider area ratio here.
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"""
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best_factor = float('-inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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area_ratio = (ratio[0]*ratio[1]*image_size*image_size)/ area
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"""
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new area > 60% of original image area is enough.
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"""
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factor_based_on_area_n_ratio = min((ratio[0]*ratio[1]*image_size*image_size)/ area, 0.6)* \
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min(target_aspect_ratio/aspect_ratio, aspect_ratio/target_aspect_ratio)
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if factor_based_on_area_n_ratio > best_factor:
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best_factor = factor_based_on_area_n_ratio
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best_ratio = ratio
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return best_ratio
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def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# calculate the existing image aspect ratio
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target_ratios = set(
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(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
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def split_model(model_path, device):
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device_map = {}
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world_size = torch.cuda.device_count()
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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num_layers = config.llm_config.num_hidden_layers
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print('world_size', world_size)
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num_layers_per_gpu_ = math.floor(num_layers / (world_size - 1))
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num_layers_per_gpu = [num_layers_per_gpu_] * world_size
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num_layers_per_gpu[device] = num_layers - num_layers_per_gpu_ * (world_size-1)
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print(num_layers_per_gpu)
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layer_cnt = 0
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for i, num_layer in enumerate(num_layers_per_gpu):
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for j in range(num_layer):
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device_map[f'language_model.model.layers.{layer_cnt}'] = i
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layer_cnt += 1
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device_map['vision_model'] = device
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device_map['mlp1'] = device
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device_map['language_model.model.tok_embeddings'] = device
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device_map['language_model.model.embed_tokens'] = device
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device_map['language_model.output'] = device
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device_map['language_model.model.norm'] = device
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device_map['language_model.lm_head'] = device
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device_map['language_model.model.rotary_emb'] = device
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device_map[f'language_model.model.layers.{num_layers - 1}'] = device
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return device_map
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class ModelWorker:
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def __init__(self, model_path, model_name,
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load_8bit, device):
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if model_path.endswith('/'):
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model_path = model_path[:-1]
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if model_name is None:
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model_paths = model_path.split('/')
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if model_paths[-1].startswith('checkpoint-'):
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self.model_name = model_paths[-2] + '_' + model_paths[-1]
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else:
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self.model_name = model_paths[-1]
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else:
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self.model_name = model_name
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print(f'Loading the model {self.model_name}')
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
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tokens_to_keep = ['<box>', '</box>', '<ref>', '</ref>']
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tokenizer.additional_special_tokens = [item for item in tokenizer.additional_special_tokens if item not in tokens_to_keep]
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self.tokenizer = tokenizer
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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model_type = config.vision_config.model_type
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self.device = torch.cuda.current_device()
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if model_type == 'siglip_vision_model':
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self.norm_type = 'siglip'
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elif model_type == 'MOB':
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self.norm_type = 'siglip'
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else:
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self.norm_type = 'imagenet'
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if any(x in model_path.lower() for x in ['34b']):
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device_map = split_model(model_path, self.device)
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else:
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device_map = None
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if device_map is not None:
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self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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device_map=device_map,
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trust_remote_code=True,
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load_in_8bit=load_8bit).eval()
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else:
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self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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load_in_8bit=load_8bit).eval()
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if not load_8bit and device_map is None:
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self.model = self.model.to(device)
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self.load_8bit = load_8bit
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self.model_path = model_path
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self.image_size = self.model.config.force_image_size
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self.context_len = tokenizer.model_max_length
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self.per_tile_len = 256
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def reload_model(self):
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del self.model
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torch.cuda.empty_cache()
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if self.device == 'auto':
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os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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# This can make distributed deployment work properly
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self.model = AutoModel.from_pretrained(
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self.model_path,
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load_in_8bit=self.load_8bit,
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torch_dtype=torch.bfloat16,
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device_map=self.device_map,
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trust_remote_code=True).eval()
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else:
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self.model = AutoModel.from_pretrained(
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self.model_path,
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load_in_8bit=self.load_8bit,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True).eval()
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if not self.load_8bit and not self.device == 'auto':
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self.model = self.model.cuda()
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@torch.inference_mode()
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def generate(self, params):
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system_message = params['prompt'][0]['content']
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send_messages = params['prompt'][1:]
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max_input_tiles = params['max_input_tiles']
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temperature = params['temperature']
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top_p = params['top_p']
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max_new_tokens = params['max_new_tokens']
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repetition_penalty = params['repetition_penalty']
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video_frame_num = params.get('video_frame_num', 64)
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do_sample = True if temperature > 0.0 else False
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global_image_cnt = 0
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history, pil_images, max_input_tile_list = [], [], []
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for message in send_messages:
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if message['role'] == 'user':
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prefix = ''
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if 'image' in message:
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for image_data in message['image']:
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pil_images.append(load_image(image_data))
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prefix = prefix + f'<image {global_image_cnt + 1}><image>\n'
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global_image_cnt += 1
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max_input_tile_list.append(max_input_tiles)
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if 'video' in message:
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for video_data in message['video']:
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video_frames, tmp_prefix = load_video(video_data, num_frames=video_frame_num)
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pil_images.extend(video_frames)
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prefix = prefix + tmp_prefix
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global_image_cnt += len(video_frames)
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max_input_tile_list.extend([1] * len(video_frames))
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content = prefix + message['content']
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history.append([content, ])
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else:
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history[-1].append(message['content'])
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question, history = history[-1][0], history[:-1]
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if global_image_cnt == 1:
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question = question.replace('<image 1><image>\n', '<image>\n')
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history = [[item[0].replace('<image 1><image>\n', '<image>\n'), item[1]] for item in history]
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try:
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assert len(max_input_tile_list) == len(pil_images), 'The number of max_input_tile_list and pil_images should be the same.'
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except Exception as e:
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from IPython import embed; embed()
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exit()
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print(f'Error: {e}')
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print(f'max_input_tile_list: {max_input_tile_list}, pil_images: {pil_images}')
|
|
# raise e
|
|
|
|
old_system_message = self.model.system_message
|
|
self.model.system_message = system_message
|
|
|
|
transform = build_transform(input_size=self.image_size, norm_type=self.norm_type)
|
|
if len(pil_images) > 0:
|
|
max_input_tiles_limited_by_contect = params['max_input_tiles']
|
|
while True:
|
|
image_tiles = []
|
|
for current_max_input_tiles, pil_image in zip(max_input_tile_list, pil_images):
|
|
if self.model.config.dynamic_image_size:
|
|
tiles = dynamic_preprocess(
|
|
pil_image, image_size=self.image_size, max_num=min(current_max_input_tiles, max_input_tiles_limited_by_contect),
|
|
use_thumbnail=self.model.config.use_thumbnail)
|
|
else:
|
|
tiles = [pil_image]
|
|
image_tiles += tiles
|
|
if (len(image_tiles) * self.per_tile_len < self.context_len):
|
|
break
|
|
else:
|
|
max_input_tiles_limited_by_contect -= 2
|
|
|
|
if max_input_tiles_limited_by_contect < 1:
|
|
break
|
|
|
|
pixel_values = [transform(item) for item in image_tiles]
|
|
pixel_values = torch.stack(pixel_values).to(self.model.device, dtype=torch.bfloat16)
|
|
print(f'Split images to {pixel_values.shape}')
|
|
else:
|
|
pixel_values = None
|
|
|
|
generation_config = dict(
|
|
num_beams=1,
|
|
max_new_tokens=max_new_tokens,
|
|
do_sample=do_sample,
|
|
temperature=temperature,
|
|
repetition_penalty=repetition_penalty,
|
|
max_length=self.context_len,
|
|
top_p=top_p,
|
|
)
|
|
|
|
response = self.model.chat(
|
|
tokenizer=self.tokenizer,
|
|
pixel_values=pixel_values,
|
|
question=question,
|
|
history=history,
|
|
return_history=False,
|
|
generation_config=generation_config,
|
|
)
|
|
self.model.system_message = old_system_message
|
|
return {'text': response, 'error_code': 0}
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--model-path', type=str, default='nvidia/Eagle2-9B')
|
|
parser.add_argument('--model-name', type=str, default='Eagle2-9B')
|
|
parser.add_argument('--device', type=str, default='cuda')
|
|
parser.add_argument('--load-8bit', action='store_true')
|
|
args = parser.parse_args()
|
|
print(f'args: {args}')
|
|
|
|
worker = ModelWorker(
|
|
args.model_path,
|
|
args.model_name,
|
|
args.load_8bit,
|
|
args.device)
|
|
```
|
|
</details>
|
|
|
|
|
|
### 2. Prepare the Prompt
|
|
|
|
- Single image input
|
|
```python
|
|
prompt = [
|
|
{'role': 'system', 'content': 'You are a helpful assistant.'},
|
|
{'role': 'user', 'content': 'Describe this image in details.',
|
|
'image':[
|
|
{'url': 'https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]'}
|
|
],
|
|
}
|
|
]
|
|
```
|
|
|
|
- Multiple image input
|
|
```python
|
|
prompt = [
|
|
{'role': 'system', 'content': 'You are a helpful assistant.'},
|
|
{'role': 'user', 'content': 'Describe these two images in details.',
|
|
'image':[
|
|
{'url': 'https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]'},
|
|
{'url': 'https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]'}
|
|
],
|
|
}
|
|
]
|
|
```
|
|
|
|
- Video input
|
|
```python
|
|
prompt = [
|
|
{'role': 'system', 'content': 'You are a helpful assistant.'},
|
|
{'role': 'user', 'content': 'Describe this video in details.',
|
|
'video':[
|
|
'path/to/your/video.mp4'
|
|
],
|
|
}
|
|
]
|
|
```
|
|
|
|
### 3. Generate the response
|
|
```python
|
|
params = {
|
|
'prompt': prompt,
|
|
'max_input_tiles': 24,
|
|
'temperature': 0.7,
|
|
'top_p': 1.0,
|
|
'max_new_tokens': 4096,
|
|
'repetition_penalty': 1.0,
|
|
}
|
|
worker.generate(params)
|
|
```
|
|
|
|
## TODO
|
|
- [ ] Support vLLM Inference
|
|
- [ ] Provide AWQ Quantization Weights
|
|
- [ ] Provide fine-tuning scripts
|
|
|
|
|
|
## License/Terms of Use
|
|
- The code is released under the Apache 2.0 license as found in the [LICENSE](https://huggingface.co./NVEagle/Eagle-X5-13B-Chat/blob/main/LICENSE) file.
|
|
- The pretrained model weights are released under the [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0) <br>
|
|
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
|
|
- Model License of Qwen2.5-7B-Instruct: [Apache-2.0](https://huggingface.co./Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE)
|
|
- Model License of PaliGemma: [Gemma license](https://ai.google.dev/gemma/terms)
|
|
|
|
|
|
|
|
## Citation
|
|
|
|
## Ethical Considerations
|
|
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
|
|
|
|
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
|
|
|
