README.md

metadata

datasets:
  - shenxq/OneVision
  - shenxq/VideoChat2
base_model:
  - Vision-CAIR/LongVU_Qwen2_7B_img
pipeline_tag: video-text-to-text
model-index:
  - name: llava-onevision-qwen-7b-ov
    results:
      - task:
          type: multimodal
        dataset:
          name: EgoSchema
          type: egoschema
        metrics:
          - type: accuracy
            value: 67.6
            name: accuracy
            verified: true
      - task:
          type: multimodal
        dataset:
          name: MLVU
          type: mlvu
        metrics:
          - type: accuracy
            value: 65.4
            name: accuracy
            verified: true
      - task:
          type: multimodal
        dataset:
          name: MVBench
          type: mvbench
        metrics:
          - type: accuracy
            value: 66.9
            name: accuracy
            verified: true
      - task:
          type: multimodal
        dataset:
          name: VideoMME
          type: videomme
        metrics:
          - type: accuracy
            value: 60.6
            name: accuracy
            verified: true

LongVU

This repository contains the model based on Qwen2-7B as presented in LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding.

Play with the model on the HF demo.

Use

We provide the simple generation process for using our model. For more details, you could refer to Github

# git clone https://github.com/Vision-CAIR/LongVU
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 + "\n" + 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()