[📂 GitHub] [📜 Eagle2 Tech Report] [🤗 HF Demo]
eagle_2_5_vl to support generate feature.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 Eagle2-2B, a lightweight model that achieves remarkable efficiency and speed while maintaining solid performance.
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 | 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 |
We provide a inference script to help you quickly start using the model. We support different input types:
pip install transformers
pip install flash-attn
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)]
image_inputs, video_inputs = processor.process_vision_info(messages)
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
inputs = inputs.to("cuda")
model = model.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel, AutoTokenizer
import torch
from transformers import TextIteratorStreamer
import threading
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)]
image_inputs, video_inputs = processor.process_vision_info(messages)
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
inputs = inputs.to("cuda")
model = model.to("cuda")
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
temperature=0.8
)
thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
for new_text in streamer:
print(new_text, end="", flush=True)
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
{
"type": "image",
"image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
},
{"type": "text", "text": "Describe these two images."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)]
image_inputs, video_inputs = processor.process_vision_info(messages)
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
inputs = inputs.to("cuda")
model = model.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "../Eagle2-8B/space_woaudio.mp4",
},
{"type": "text", "text": "Describe this video."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)]
image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
inputs = inputs.to("cuda")
model = model.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "../Eagle2-8B/space_woaudio.mp4",
"nframes": 10,
},
{
"type": "video",
"video": "../Eagle2-8B/video_ocr.mp4",
"nframes": 10,
},
{"type": "text", "text": "Describe these two videos respectively."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)]
image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
inputs = inputs.to("cuda")
model = model.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
processor.tokenizer.padding_side = "left"
messages1 = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
messages2 = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text_list = [processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
) for messages in [messages1, messages2]]
image_inputs, video_inputs = processor.process_vision_info([messages1, messages2])
inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
inputs = inputs.to("cuda")
model = model.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
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.