Eagle2-9B

Eagle-2

[📂 GitHub] [📜 Eagle2 Tech Report]

[🗨️ Chat Demo] [🤗 HF Demo]

Introduction

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.

In this repo, we are open-sourcing Eagle2-9B, which strikes the perfect balance between performance and inference speed.

Model Zoo

We provide the following models:

| model name | LLM | Vision | Max Length| HF Link|

| ----------- | ------- |---------|-|-|

| Eagle2-1B | Qwen2.5-0.5B-Instruct | Siglip | 16K| 🤗 link|

| Eagle2-2B | Qwen2.5-1.5B-Instruct | Siglip | 16K| 🤗 link|

| Eagle2-9B | Qwen2.5-7B-Instruct | Siglip+ConvNext | 16K| 🤗 link|

Benchmark Results

| Benchmark | MiniCPM-Llama3-V-2_5 | InternVL-Chat-V1-5 | InternVL2-8B |QwenVL2-7B| Eagle2-9B|

| :--------------------------: | :------------------: | :----------------: | :----------: |:----------: |:----------: |

| Model Size | 8.5B | 25.5B | 8.1B | 8.3B|8.9B|

| | | | | | |

| DocVQA_test | 84.8 | 90.9 | 91.6 |94.5|92.6|

| ChartQA_test | - | 83.8 | 83.3 |83.0|86.4|

| InfoVQA_test | - | 72.5 | 74.8 |74.3|77.2|

| TextVQA_val | 76.6 | 80.6 | 77.4 |84.3|83.0|

| OCRBench | 725 | 724 | 794 |845|868|

| MME_sum | 2024.6 | 2187.8 | 2210.3 | 2326.8|2260|

| RealWorldQA | 63.5 | 66.0 | 64.4 |70.1|69.3|

| AI2D_test | 78.4 | 80.7 | 83.8 | - |83.9|

| MMMU_val | 45.8 | 45.2 / 46.8 | 49.3 / 51.8 |54.1|56.1|

| MMBench_V11_test | | | 79.5 |79.4|80.6|

| MMVet_GPT-4-Turbo | 52.8 | 55.4 | 54.2 | 62.0|62.2|

| SEED-Image | 72.3 | 76.0 | 76.2 ||77.1|

| HallBench_avg | 42.4 | 49.3 | 45.2 |50.6|49.3

| MathVista_testmini | 54.3 | 53.5 | 58.3 |58.2|63.8|

| MMstar | - | - | 60.9|60.7|62.6|

Quick Start

We provide a demo inference script to help you quickly start using the model. We support different input types:

• pure text input

• single image input

• multiple image input

• video input

0. Install the dependencies

pip install transformers==4.37.2

pip install flash-attn

Note: Latest version of transformers is not compatible with the model.

1. Prepare the Model worker

<details> <summary>Click to expand</summary>

"""

A model worker executes the model.

Copied and modified from https://github.com/OpenGVLab/InternVL/blob/main/streamlit_demo/model_worker.py

"""

# Importing torch before transformers can cause `segmentation fault`

from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer, AutoConfig



import argparse

import base64

import json

import os

import decord

import threading

import time

from io import BytesIO

from threading import Thread

import math

import requests

import torch

import torchvision.transforms as T

from PIL import Image

from torchvision.transforms.functional import InterpolationMode

import numpy as np





IMAGENET_MEAN = (0.485, 0.456, 0.406)

IMAGENET_STD = (0.229, 0.224, 0.225)



SIGLIP_MEAN = (0.5, 0.5, 0.5)

SIGLIP_STD = (0.5, 0.5, 0.5)





def get_seq_frames(total_num_frames, desired_num_frames=-1, stride=-1):

    """

    Calculate the indices of frames to extract from a video.



    Parameters:

    total_num_frames (int): Total number of frames in the video.

    desired_num_frames (int): Desired number of frames to extract.



    Returns:

    list: List of indices of frames to extract.

    """

    

    assert desired_num_frames > 0 or stride > 0 and not (desired_num_frames > 0 and stride > 0)



    if stride > 0:

        return list(range(0, total_num_frames, stride))

    

    # Calculate the size of each segment from which a frame will be extracted

    seg_size = float(total_num_frames - 1) / desired_num_frames



    seq = []

    for i in range(desired_num_frames):

        # Calculate the start and end indices of each segment

        start = int(np.round(seg_size * i))

        end = int(np.round(seg_size * (i + 1)))



        # Append the middle index of the segment to the list

        seq.append((start + end) // 2)



    return seq



def build_video_prompt(meta_list, num_frames, time_position=False):

    # if time_position is True, the frame_timestamp is used.

    # 1. pass time_position, 2. use env TIME_POSITION

    time_position = os.environ.get("TIME_POSITION", time_position)

    prefix = f"This is a video:\n"

    for i in range(num_frames):

        if time_position:

            frame_txt = f"Frame {i+1} sampled at {meta_list[i]:.2f} seconds: <image>\n"

        else:

            frame_txt = f"Frame {i+1}: <image>\n"

        prefix += frame_txt

    return prefix



def load_video(video_path, num_frames=64, frame_cache_root=None):

    if isinstance(video_path, str):

        video = decord.VideoReader(video_path)

    elif isinstance(video_path, dict):

        assert False, 'we not support vidoe: "video_path" as input'

    fps = video.get_avg_fps()

    sampled_frames = get_seq_frames(len(video), num_frames)

    samepld_timestamps = [i / fps for i in sampled_frames]

    frames = video.get_batch(sampled_frames).asnumpy()

    images = [Image.fromarray(frame) for frame in frames]

    

    return images, build_video_prompt(samepld_timestamps, len(images), time_position=True)



def load_image(image):

    if isinstance(image, str) and os.path.exists(image):

        return Image.open(image)

    elif isinstance(image, dict):

        if 'disk_path' in image:

            return Image.open(image['disk_path'])

        elif 'base64' in image:

            return Image.open(BytesIO(base64.b64decode(image['base64'])))

        elif 'url' in image:

            response = requests.get(image['url'])

            return Image.open(BytesIO(response.content))

        elif 'bytes' in image:

            return Image.open(BytesIO(image['bytes']))

        else:

            raise ValueError(f'Invalid image: {image}')

    else:

        raise ValueError(f'Invalid image: {image}')



def build_transform(input_size, norm_type='imagenet'):

    if norm_type == 'imagenet':

        MEAN, STD = IMAGENET_MEAN, IMAGENET_STD

    elif norm_type == 'siglip':

        MEAN, STD = SIGLIP_MEAN, SIGLIP_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):

    """

    previous version mainly foucs on ratio.

    We also consider area ratio here.

    """

    best_factor = 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)

        area_ratio = (ratio[0]*ratio[1]*image_size*image_size)/ area

        """

        new area > 60% of original image area is enough.

        """

        factor_based_on_area_n_ratio = min((ratio[0]*ratio[1]*image_size*image_size)/ area, 0.6)* \

                                     min(target_aspect_ratio/aspect_ratio, aspect_ratio/target_aspect_ratio)

        

        if factor_based_on_area_n_ratio > best_factor:

            best_factor = factor_based_on_area_n_ratio

            best_ratio = ratio

        

    return best_ratio





def dynamic_preprocess(image, min_num=1, max_num=6, 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 split_model(model_path, device):



    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



    print('world_size', world_size)

    num_layers_per_gpu_ = math.floor(num_layers / (world_size - 1))

    num_layers_per_gpu = [num_layers_per_gpu_] * world_size

    num_layers_per_gpu[device] = num_layers - num_layers_per_gpu_ * (world_size-1)

    print(num_layers_per_gpu)

    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'] = device

    device_map['mlp1'] = device

    device_map['language_model.model.tok_embeddings'] = device

    device_map['language_model.model.embed_tokens'] = device

    device_map['language_model.output'] = device

    device_map['language_model.model.norm'] = device

    device_map['language_model.lm_head'] = device

    device_map['language_model.model.rotary_emb'] = device

    device_map[f'language_model.model.layers.{num_layers - 1}'] = device

    return device_map



class ModelWorker:

    def __init__(self, model_path, model_name,

                 load_8bit, device):



        if model_path.endswith('/'):

            model_path = model_path[:-1]

        if model_name is None:

            model_paths = model_path.split('/')

            if model_paths[-1].startswith('checkpoint-'):

                self.model_name = model_paths[-2] + '_' + model_paths[-1]

            else:

                self.model_name = model_paths[-1]

        else:

            self.model_name = model_name



        print(f'Loading the model {self.model_name}')



        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False)

        tokens_to_keep = ['<box>', '</box>', '<ref>', '</ref>']

        tokenizer.additional_special_tokens = [item for item in tokenizer.additional_special_tokens if item not in tokens_to_keep]

        self.tokenizer = tokenizer

        config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)

        model_type = config.vision_config.model_type

        self.device = torch.cuda.current_device()

        if model_type == 'siglip_vision_model':

            self.norm_type = 'siglip'

        elif model_type == 'MOB':

            self.norm_type = 'siglip'

        else:

            self.norm_type = 'imagenet'



        if any(x in model_path.lower() for x in ['34b']):

            device_map = split_model(model_path, self.device)

        else:

            device_map = None

        

        if device_map is not None:    

            self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16,

                                               low_cpu_mem_usage=True,

                                               device_map=device_map, 

                                               trust_remote_code=True,

                                               load_in_8bit=load_8bit).eval()

        else:

            self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16,

                                               trust_remote_code=True,

                                               load_in_8bit=load_8bit).eval()  



        if not load_8bit and device_map is None:

            self.model = self.model.to(device)

        self.load_8bit = load_8bit

        

        self.model_path = model_path

        self.image_size = self.model.config.force_image_size

        self.context_len = tokenizer.model_max_length

        self.per_tile_len = 256



    def reload_model(self):

        del self.model

        torch.cuda.empty_cache()

        if self.device == 'auto':

            os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

            # This can make distributed deployment work properly

            self.model = AutoModel.from_pretrained(

                self.model_path,

                load_in_8bit=self.load_8bit,

                torch_dtype=torch.bfloat16,

                device_map=self.device_map,

                trust_remote_code=True).eval()

        else:

            self.model = AutoModel.from_pretrained(

                self.model_path,

                load_in_8bit=self.load_8bit,

                torch_dtype=torch.bfloat16,

                trust_remote_code=True).eval()

        if not self.load_8bit and not self.device == 'auto':

            self.model = self.model.cuda()



    @torch.inference_mode()

    def generate(self, params):

        system_message = params['prompt'][0]['content']

        send_messages = params['prompt'][1:]

        max_input_tiles = params['max_input_tiles']

        temperature = params['temperature']

        top_p = params['top_p']

        max_new_tokens = params['max_new_tokens']

        repetition_penalty = params['repetition_penalty']

        video_frame_num = params.get('video_frame_num', 64)

        do_sample = True if temperature > 0.0 else False



        global_image_cnt = 0

        history, pil_images, max_input_tile_list = [], [], []

        for message in send_messages:

            if message['role'] == 'user':

                prefix = ''

                if 'image' in message:

                    for image_data in message['image']:

                        pil_images.append(load_image(image_data))

                        prefix = prefix + f'<image {global_image_cnt + 1}><image>\n'

                        global_image_cnt += 1

                        max_input_tile_list.append(max_input_tiles)

                if 'video' in message:

                    for video_data in message['video']:

                        video_frames, tmp_prefix = load_video(video_data, num_frames=video_frame_num)

                        pil_images.extend(video_frames)

                        prefix = prefix + tmp_prefix

                        global_image_cnt += len(video_frames)

                        max_input_tile_list.extend([1] * len(video_frames))

                content = prefix + message['content']

                history.append([content, ])

            else:

                history[-1].append(message['content'])

        question, history = history[-1][0], history[:-1]



        if global_image_cnt == 1:

            question = question.replace('<image 1><image>\n', '<image>\n')

            history = [[item[0].replace('<image 1><image>\n', '<image>\n'), item[1]] for item in history]





        try:

            assert len(max_input_tile_list) == len(pil_images), 'The number of max_input_tile_list and pil_images should be the same.'

        except Exception as e:

            from IPython import embed; embed()

            exit()

            print(f'Error: {e}')

            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

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

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

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

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 file.

• The pretrained model weights are released under the Creative Commons Attribution: Non-Commercial 4.0 International <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

  • Model License of PaliGemma: Gemma license

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.