---
license: apache-2.0
datasets:
- THUdyh/Oryx-SFT-Data
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
language:
- en
- zh
---
# Oryx-1.5-7B
## Model Summary
The Oryx-1.5 models are 7/32B parameter models trained on [Oryx-SFT-Data](https://huggingface.co/datasets/THUdyh/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
- **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](https://github.com/liuzuyan/oryx)
```
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
#### Long-Form Video Understanding
#### Common Image Benchmark
#### 3D Spatial Understanding
### Model Architecture
- **Architecture:** Pre-trained [Oryx-ViT](https://huggingface.co/THUdyh/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