Oryx-1.5-7B

Oryx-1.5-7B

Model Summary

The Oryx-1.5 models are 7/32B parameter models trained on Oryx-SFT-Data, based on Qwen2.5 language model with a context window of 32K tokens.

Oryx offers an on-demand solution to seamlessly and efficiently process visual inputs with arbitrary spatial sizes and temporal lengths.

• Repository: https://github.com/Oryx-mllm/Oryx
• Project Page: https://oryx-mllm.github.io
• Languages: English, Chinese
• Paper: https://arxiv.org/abs/2409.12961

Use

We provide a simple generation process for using our model. For more details, please refer to our Github Repo

from oryx.model.builder import load_pretrained_model
from oryx.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from oryx.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from oryx.conversation import conv_templates, SeparatorStyle
from PIL import Image
import requests
import copy
import torch
import sys
import warnings
from decord import VideoReader, cpu
import numpy as np

def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
    if max_frames_num == 0:
        return np.zeros((1, 336, 336, 3))
    vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
    total_frame_num = len(vr)
    video_time = total_frame_num / vr.get_avg_fps()
    fps = round(vr.get_avg_fps()/fps)
    frame_idx = [i for i in range(0, len(vr), fps)]
    frame_time = [i/fps for i in frame_idx]
    if len(frame_idx) > max_frames_num or force_sample:
        sample_fps = max_frames_num
        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
        frame_idx = uniform_sampled_frames.tolist()
        frame_time = [i/vr.get_avg_fps() for i in frame_idx]
    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
    spare_frames = vr.get_batch(frame_idx).asnumpy()
    # import pdb;pdb.set_trace()
    return spare_frames,frame_time,video_time
pretrained = "THUdyh/Oryx-7B"
model_name = "oryx_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)
model.eval()
video_path = ""
max_frames_num = "64"
video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
video = [video]
video_data = (video, video)
input_data = (video_data, (384, 384), "video")
conv_template = "qwen_1_5"
question = DEFAULT_IMAGE_TOKEN + "\nPlease describe this video in detail."
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
output_ids = model.generate(
    inputs=input_ids,
    images=input_data[0][0],
    images_highres=input_data[0][1],
    modalities=video_data[2],
    do_sample=False,
    temperature=0,
    max_new_tokens=128,
    use_cache=True,
)

text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)

Results

General Video Benchmark

<img src="https://cdn-uploads.huggingface.co/production/uploads/652965773a416e1f2173443b/hKfOK0u3OXly_u4hgGLDB.png" alt="image/png" style="zoom: 33%;" />

Long-Form Video Understanding

<img src="https://cdn-uploads.huggingface.co/production/uploads/652965773a416e1f2173443b/Xweq9f4OWkqeVc_FZIMuO.png" alt="image/png" style="zoom:33%;" />

Common Image Benchmark

<img src="https://cdn-uploads.huggingface.co/production/uploads/652965773a416e1f2173443b/ybfroSA9WaKXtJbP_9cLR.png" alt="image/png" style="zoom:33%;" />

3D Spatial Understanding

<img src="https://cdn-uploads.huggingface.co/production/uploads/652965773a416e1f2173443b/5v8ACRzAoKS0FbcVBXZhT.png" alt="image/png" style="zoom:33%;" />

Model Architecture

• Architecture: Pre-trained Oryx-ViT + Qwen2.5-7B
• Data: a mixture of 1.2M image/video data
• Precision: BFloat16

Hardware & Software

• Hardware: 64 * NVIDIA Tesla A100
• Orchestration: HuggingFace Trainer
• Code: Pytorch

Citation

@article{liu2024oryx,
title={Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution},
author={Liu, Zuyan and Dong, Yuhao and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming},
journal={arXiv preprint arXiv:2409.12961},
year={2024}
}