[📜 Eagle2.5 Paper] [📜 Eagle2 Paper] [📜 Eagle1 Paper] [🤗 HF Models] [🤗 HF Demo] [Faster Demo] [Project Page]
Eagle 2.5 is a family of frontier vision-language models (VLMs) designed for long-context multimodal learning. While most existing VLMs focus on short-context tasks, Eagle 2.5 addresses the challenges of long video comprehension and high-resolution image understanding, providing a generalist framework for both. The Eagle 2.5 training framework introduces two key techniques—Automatic Degrade Sampling (ADS) and Image Area Preservation (IAP)—to preserve contextual integrity and visual details. Additionally, the training pipeline is optimized for efficient long-context data training.
A major contribution of Eagle 2.5 is the introduction of Eagle-Video-110K, a novel dataset with both story-level and clip-level annotations, specifically curated for long video understanding. Eagle 2.5 demonstrates substantial improvements on long-context multimodal benchmarks, offering a robust solution to the limitations of existing VLMs. Notably, Eagle 2.5-8B achieves 72.4% on Video-MME with 512 input frames, matching the results of top-tier commercial models such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72B and InternVL2.5-78B, despite having significantly fewer parameters.
- Information-First Sampling:
- Image Area Preservation (IAP): Optimizes image tiling to retain most of the original image area and aspect ratio, preserving fine-grained details.
- Automatic Degrade Sampling (ADS): Dynamically balances visual and textual input, ensuring complete text retention while maximizing visual content within context length constraints.
- Progressive Mixed Post-Training:
- Gradually increases context length during training, enhancing the model's ability to process varying input sizes and improving information density over static sampling.
- Diversity-Driven Data Recipe:
- Combines open-source data (human-annotated and synthetic) with the self-curated Eagle-Video-110K dataset, collected via a diversity-driven strategy and annotated with both story-level and clip-level QA pairs.
- [2025/04] 🔥 Release Eagle-2.5!
- [2025/01] 🔥 Release Eagle-2!
- [2025/01] 🔥 Eagle-1 is accepted by ICLR 2025.
- [2024/08] Release Eagle-1.
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 | GPT-4o | Gemini-1.5 Pro | InternVL2.5-8B | Qwen2.5-VL-8B | Eagle2.5-8B |
|---|---|---|---|---|---|
| MVBenchtest | - | - | 72.0 | 69.6 | 74.8 |
| Perception_testval | - | - | - | 70.5 | 82.0 |
| EgoSchemafullset | - | 72.2 | - | 65.0 | 72.2 |
| MMB-Video | 1.63 | 1.30 | 1.68 | 1.79 | 1.94 |
| MLVUval | - | - | 68.9 | 70.2 | 77.6 |
| LVBenchval | 66.7 | 64.0 | 60.0 | 56.0 | 66.4 |
| Video-MMEw/o subtitle | 71.9 | 75.0 | 64.2 | 65.1 | 72.4 |
| Video-MMEw subtitle | 77.2 | 81.3 | 66.9 | 71.6 | 75.7 |
| CG-BenchClue | 58.6 | 50.9 | - | 44.5 | 55.8 |
| CG-BenchLong | 44.9 | 37.8 | - | 35.5 | 46.6 |
| CG-BenchmIoU | 5.73 | 3.85 | - | 2.48 | 13.4 |
| HourVideoDev | - | 37.2 | - | - | 44.5 |
| HourVideoTest | - | 37.4 | - | - | 41.8 |
| Charade-STAmIoU | 35.7 | - | - | 43.6 | 65.9 |
| HD-EPIC | - | 37.6 | - | - | 42.9 |
| HRVideoBench | - | - | - | - | 68.5 |
| EgoPlanval | - | - | - | - | 45.3 |
| Benchmark | GPT-4o | Gemini-1.5 Pro | InternVL2.5-8B | Qwen2.5-VL-8B | Eagle2.5-8B |
|---|---|---|---|---|---|
| OpenEQA | - | - | - | - | 63.5 |
| ERQA | 47.0 | 41.8 | - | - | 38.3 |
| EgoPlanval | - | - | - | - | 45.3 |
| Benchmark | GPT-4o | Gemini-1.5 Pro | InternVL2.5-8B | Qwen2.5-VL-8B | Eagle2.5-8B |
|---|---|---|---|---|---|
| DocVQAtest | 92.8 | 93.1 | 93.0 | 95.7 | 94.1 |
| ChartQAtest | 85.7 | 87.2 | 84.8 | 87.3 | 87.5 |
| InfoVQAtest | 79.2 | 81.0 | 77.6 | 82.6 | 80.4 |
| TextVQAval | 77.4 | 78.8 | 79.1 | 84.9 | 83.7 |
| OCRBenchtest | 736 | 754 | 822 | 864 | 869 |
| MMstartest | 64.7 | 59.1 | 62.8 | 63.9 | 66.2 |
| RWQAtest | 75.4 | 67.5 | 70.1 | 68.5 | 76.7 |
| AI2Dtest | 84.6 | 79.1 | 84.5 | 83.9 | 84.5 |
| MMMUval | 69.1 | 62.2 | 56.0 | 58.6 | 55.8 |
| MMBench_V11test | 83.1 | 74.6 | 83.2 | 82.6 | 81.7 |
| MMVetGPT-4-Turbo | 69.1 | 64.0 | 62.8 | 67.1 | 62.9 |
| HallBenchavg | 55.0 | 45.6 | 50.1 | 52.9 | 54.7 |
| MathVistatestmini | 63.8 | 63.9 | 64.4 | 68.2 | 67.8 |
| Avg Score | 74.9 | 71.7 | 73.1 | 75.6 | 75.6 |
All numbers are directly extracted from Table 2 and Table 3 of the Eagle 2.5 Tech Report.
Eagle2-1B Results
| Benchmark | LLaVa-One-Vision-0.5B | InternVL2-1B | InternVL2.5-1B | Qwen2-VL-2B | Eagle2-1B |
|---|---|---|---|---|---|
| DocVQAtest | 70.0 | 81.7 | 84.8 | 90.1 | 81.8 |
| ChartQAtest | 61.4 | 72.9 | 75.9 | 73.0 | 77.0 |
| InfoVQAtest | 41.8 | 50.9 | 56.0 | 65.5 | 54.8 |
| TextVQAval | - | 70.0 | 72.0 | 79.7 | 76.6 |
| OCRBench | 565 | 754 | 785 | 809 | 767 |
| MMEsum | 1438.0 | 1794.4 | 1950.5 | 1872.0 | 1790.2 |
| RealWorldQA | 55.6 | 50.3 | 57.5 | 62.6 | 55.4 |
| AI2Dtest | 57.1 | 64.1 | 69.3 | 74.7 | 70.9 |
| MMMUval | 31.4 | 36.7 | 40.9 | 41.1 | 38.8 |
| MMVetGPT-4-Turbo | 32.2 | 32.7 | 48.8 | 49.5 | 40.9 |
| MathVistatestmini | 33.8 | 37.7 | 43.2 | 43.0 | 45.3 |
| MMstar | 37.7 | 45.7 | 50.1 | 48.0 | 48.5 |
Eagle2-2B Results
| Benchmark | InternVL2-2B | InternVL2.5-2B | InternVL2-4B | Qwen2-VL-2B | Eagle2-2B |
|---|---|---|---|---|---|
| DocVQAtest | 86.9 | 88.7 | 89.2 | 90.1 | 88.0 |
| ChartQAtest | 76.2 | 79.2 | 81.5 | 73.0 | 82.0 |
| InfoVQAtest | 58.9 | 60.9 | 67.0 | 65.5 | 65.8 |
| TextVQAval | 73.4 | 74.3 | 74.4 | 79.7 | 79.1 |
| OCRBench | 784 | 804 | 788 | 809 | 818 |
| MMEsum | 1876.8 | 2138.2 | 2059.8 | 1872.0 | 2109.8 |
| RealWorldQA | 57.3 | 60.1 | 60.7 | 62.6 | 63.1 |
| AI2Dtest | 74.1 | 74.9 | 74.7 | 78.9 | 79.3 |
| MMMUval | 36.3 | 43.6 | 47.9 | 41.1 | 43.1 |
| MMVetGPT-4-Turbo | 39.5 | 60.8 | 51.0 | 49.5 | 53.8 |
| HallBenchavg | 37.9 | 42.6 | 41.9 | 41.7 | 45.8 |
| MathVistatestmini | 46.3 | 51.3 | 58.6 | 43.0 | 54.7 |
| MMstar | 50.1 | 53.7 | 54.3 | 48.0 | 56.4 |
Eagle2-9B 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| | | | | | | | | DocVQAtest | 84.8 | 90.9 | 91.6 |**94.5**|92.6| | ChartQAtest | - | 83.8 | 83.3 |83.0|**86.4**| | InfoVQAtest | - | 72.5 | 74.8 |74.3|**77.2**| | TextVQAval | 76.6 | 80.6 | 77.4 |**84.3**|83.0| | OCRBench | 725 | 724 | 794 |845|**868**| | MMEsum | 2024.6 | 2187.8 | 2210.3 | **2326.8**|2260| | RealWorldQA | 63.5 | 66.0 | 64.4 |**70.1**|69.3| | AI2Dtest | 78.4 | 80.7 | 83.8 | - |**83.9**| | MMMUval | 45.8 | 45.2 / 46.8 | 49.3 / 51.8 |54.1|**56.1**| | MMBench_V11test | | | 79.5 |79.4|**80.6**| | MMVetGPT-4-Turbo | 52.8 | 55.4 | 54.2 | 62.0|**62.2**| | SEED-Image | 72.3 | 76.0 | 76.2 ||**77.1**| | HallBenchavg | 42.4 | 49.3 | 45.2 |**50.6**|49.3 | MathVistatestmini | 54.3 | 53.5 | 58.3 |58.2|**63.8**| | MMstar | - | - | 60.9|60.7|**62.6**|We provide a local chat demo powered by Streamlit to help users get started with Eagle2 quickly and easily.
This demo is built upon InternVL's template and extends it with additional video input support for enhanced functionality.
We provide a 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
pip install transformers==4.37.2
pip install flash-attnNote: Latest version of transformers is not compatible with the model.
Click to expand
"""
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/Eagle2-1B')
parser.add_argument('--model-name', type=str, default='Eagle2-1B')
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)- 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/01-nvidia-logo-vert-500x200-2c50-d@2x.png'}
],
}
]- 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/01-nvidia-logo-vert-500x200-2c50-d@2x.png'},
{'url': 'https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/01-nvidia-logo-vert-500x200-2c50-d@2x.png'}
],
}
]- 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'
],
}
]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)We evaluate the performance of Eagle2 based on VLMEvalKit. We temporarily provide a custom vlmeval implementation that supports Eagle2 in our repo, and we will support Eagle2 in the official version as soon as possible.
- Support vLLM Inference
- Provide AWQ Quantization Weights
- Provide fine-tuning scripts
If you find this project useful, please cite our work:
@misc{li2025eagle2buildingposttraining,
title={Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models},
author={Zhiqi Li and Guo Chen and Shilong Liu and Shihao Wang and Vibashan VS and Yishen Ji and Shiyi Lan and Hao Zhang and Yilin Zhao and Subhashree Radhakrishnan and Nadine Chang and Karan Sapra and Amala Sanjay Deshmukh and Tuomas Rintamaki and Matthieu Le and Ilia Karmanov and Lukas Voegtle and Philipp Fischer and De-An Huang and Timo Roman and Tong Lu and Jose M. Alvarez and Bryan Catanzaro and Jan Kautz and Andrew Tao and Guilin Liu and Zhiding Yu},
year={2025},
eprint={2501.14818},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.14818},
}
@article{shi2024eagle,
title = {Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders},
author={Min Shi and Fuxiao Liu and Shihao Wang and Shijia Liao and Subhashree Radhakrishnan and De-An Huang and Hongxu Yin and Karan Sapra and Yaser Yacoob and Humphrey Shi and Bryan Catanzaro and Andrew Tao and Jan Kautz and Zhiding Yu and Guilin Liu},
journal={arXiv:2408.15998},
year={2024}
}
- 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
- 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 LLama: Llama community license
- Model License of PaliGemma: Gemma license
- Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
-
InternVL: we built the codebase based on InternVL. Thanks for the great open-source project.
-
VLMEvalKit: We use vlmeval for evaluation. Many thanks for their wonderful tools.
-
Thanks to Cambrian, LLaVA-One-Vision and more great work for their efforts in organizing open-source data.
