Update README.md

README.md

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----
-license: apache-2.0
----
+---
+datasets:
+- lmms-lab/LLaVA-NeXT-Video-SFT-Data
+language:
+- en
+library_name: transformers
+license: apache-2.0
+metrics:
+- accuracy
+tags:
+- multimodal
+model-index:
+- name: LLaVA-NeXT-Video-7B-Qwen2
+  results:
+  - task:
+      type: multimodal
+    dataset:
+      name: ActNet-QA
+      type: actnet-qa
+    metrics:
+    - type: accuracy
+      value: 56.5
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: EgoSchema
+      type: egoschema
+    metrics:
+    - type: accuracy
+      value: 57.3
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MLVU
+      type: mlvu
+    metrics:
+    - type: accuracy
+      value: 70.8
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: MVBench
+      type: mvbench
+    metrics:
+    - type: accuracy
+      value: 58.6
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: NextQA
+      type: nextqa
+    metrics:
+    - type: accuracy
+      value: 83.2
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: PercepTest
+      type: percepTest
+    metrics:
+    - type: accuracy
+      value: 67.9
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoChatGPT
+      type: videochatgpt
+    metrics:
+    - type: score
+      value: 3.52
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoDC
+      type: videodc
+    metrics:
+    - type: score
+      value: 3.66
+      name: score
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: LongVideoBench
+      type: longvideobench
+    metrics:
+    - type: accuracy
+      value: 58.2
+      name: accuracy
+      verified: true
+  - task:
+      type: multimodal
+    dataset:
+      name: VideoMME
+      type: videomme
+    metrics:
+    - type: accuracy
+      value: 63.3
+      name: accuracy
+      verified: true
+---
+
+# LLaVA-NeXT-Video
+
+##  Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Use](##use)
+3. [Limitations](##limitations)
+4. [Training](##training)
+5. [License](##license)
+6. [Citation](##citation)
+
+## Model Summary
+
+The LLaVA-OneVision models are 7/72B parameter models trained on [LLaVA-NeXT-Video-SFT](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Video-SFT-Data), based on Qwen2 language model with a context window of 32K tokens.
+
+- **Repository:** [LLaVA-VL/LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT?tab=readme-ov-file)
+- **Point of Contact:** [Yuanhan Zhang](mailto:drluodian@gmail.com)
+- **Languages:** English, Chinese
+
+
+## Use
+
+### Intended use
+
+The model was trained on [LLaVA-NeXT-Video-SFT](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Video-SFT-Data) and have the ability to interact with images, multi-image and videos, but specific to videos.
+
+**Feel free to share your generations in the Community tab!**
+
+### Generation
+
+We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT).
+
+```python
+# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava.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
+warnings.filterwarnings("ignore")
+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 = "lmms-lab/LLaVA-NeXT-Video-7B-Qwen2"
+model_name = "llava_qwen"
+device = "cuda"
+device_map = "auto"
+tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args
+model.eval()
+video_path = "XXXX"
+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()
+conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
+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)
+cont = model.generate(
+    input_ids,
+    images=video,
+    modalities="video"
+    do_sample=False,
+    temperature=0,
+    max_new_tokens=4096,
+)
+text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
+print(text_outputs)
+```
+
+
+# Training
+
+## Model
+
+- **Architecture:** SO400M + Qwen2
+- **Initialized Model:** lmms-lab/llava-onevision-qwen2-7b-si
+- **Data:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
+- **Precision:** bfloat16
+
+## Hardware & Software
+
+- **GPUs:** 256 * Nvidia Tesla A100 (for whole model series training)
+- **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
+- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
+
+# Citation