File size: 2,873 Bytes
f3393ce fb41ece f3393ce fb41ece f3393ce 5c69edc f3393ce fb41ece f3393ce fb41ece | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | ---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
language:
- en
license: apache-2.0
pipeline_tag: video-text-to-text
tags:
- multimodal
library_name: transformers
---
# TimeSearch-R-7B
- **Code:** https://github.com/Time-Search/TimeSearch-R
- **Paper:** [TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning](https://arxiv.org/abs/2511.05489)
## Usage
We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/Time-Search/TimeSearch-R).
```python
import numpy as np
import torch
from longvu.builder import load_pretrained_model
from longvu.constants import (
DEFAULT_IMAGE_TOKEN,
IMAGE_TOKEN_INDEX,
)
from longvu.conversation import conv_templates, SeparatorStyle
from longvu.mm_datautils import (
KeywordsStoppingCriteria,
process_images,
tokenizer_image_token,
)
from decord import cpu, VideoReader
tokenizer, model, image_processor, context_len = load_pretrained_model(
"./checkpoints/longvu_qwen", None, "cambrian_qwen",
)
model.eval()
video_path = "./examples/video1.mp4"
qs = "Describe this video in detail"
vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
fps = float(vr.get_avg_fps())
frame_indices = np.array([i for i in range(0, len(vr), round(fps),)])
video = []
for frame_index in frame_indices:
img = vr[frame_index].asnumpy()
video.append(img)
video = np.stack(video)
image_sizes = [video[0].shape[:2]]
video = process_images(video, image_processor, model.config)
video = [item.unsqueeze(0) for item in video]
qs = DEFAULT_IMAGE_TOKEN + "
" + qs
conv = conv_templates["qwen"].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=video,
image_sizes=image_sizes,
do_sample=False,
temperature=0.2,
max_new_tokens=128,
use_cache=True,
stopping_criteria=[stopping_criteria],
)
pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
```
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{timesearch-r,
title={TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning},
author={Pan, Junwen and Zhang, Qizhe and Zhang, Rui and Lu, Ming and Wan, Xin and Zhang, Yuan and Liu, Chang and She, Qi},
journal={arXiv preprint arXiv:2511.05489},
year={2025}
}
``` |