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README.md

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----
-license: llama2
----
+---
+language:
+- en
+license: llama2
+pipeline_tag: image-text-to-text
+---
+
+# LLaVA-NeXT-Video Model Card
+
+Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CZggLHrjxMReG-FNOmqSOdi4z7NPq6SO?usp=sharing)
+
+Disclaimer: The team releasing LLaVa-NeXT-Video did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## 📄 Model details
+
+**Model type:**
+LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. The model is buit on top of LLaVa-NeXT by tuning on a mix of video and image data to achieves better video understanding capabilities. The videos were sampled uniformly to be 32 frames per clip.
+The model is a current SOTA among open-source models on [VideoMME bench](https://arxiv.org/abs/2405.21075).
+Base LLM: [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
+
+<img src="http://drive.google.com/uc?export=view&id=1fVg-r5MU3NoHlTpD7_lYPEBWH9R8na_4">
+
+
+**Model date:**
+LLaVA-Next-Video-7B was trained in April 2024.
+
+**Paper or resources for more information:** https://github.com/LLaVA-VL/LLaVA-NeXT
+
+
+## 📚 Training dataset
+
+### Image
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 500K academic-task-oriented VQA data mixture.
+- 50K GPT-4V data mixture.
+- 40K ShareGPT data.
+
+### Video
+- 100K VideoChatGPT-Instruct.
+
+## 📊 Evaluation dataset
+A collection of 4 benchmarks, including 3 academic VQA benchmarks and 1 captioning benchmark.
+
+
+
+## 🚀 How to use the model
+
+First, make sure to have `transformers >= 4.42.0`. 
+The model supports multi-visual and multi-prompt generation. Meaning that you can pass multiple images/videos in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` or `<video>` to the location where you want to query images/videos:
+
+Below is an example script to run generation in `float16` precision on a GPU device:
+
+```python
+import av
+import torch
+from transformers import LlavaNextVideoProcessor, LlavaNextVideoForConditionalGeneration
+
+model_id = "llava-hf/LLaVA-NeXT-Video-7B-hf-DPO"
+
+model = LlavaNextVideoForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True, 
+).to(0)
+
+processor = LlavaNextVideoProcessor.from_pretrained(model_id)
+
+def read_video_pyav(container, indices):
+    '''
+    Decode the video with PyAV decoder.
+    Args:
+        container (`av.container.input.InputContainer`): PyAV container.
+        indices (`List[int]`): List of frame indices to decode.
+    Returns:
+        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    container.seek(0)
+    start_index = indices[0]
+    end_index = indices[-1]
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > end_index:
+            break
+        if i >= start_index and i in indices:
+            frames.append(frame)
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+
+# define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image", "video") 
+conversation = [
+    {
+
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Why is this video funny?"},
+            {"type": "video"},
+            ],
+    },
+]
+
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
+container = av.open(video_path)
+
+# sample uniformly 8 frames from the video, can sample more for longer videos
+total_frames = container.streams.video[0].frames
+indices = np.arange(0, total_frames, total_frames / 8).astype(int)
+clip = read_video_pyav(container, indices)
+inputs_video = processor(text=prompt, videos=clip, padding=True, return_tensors="pt").to(model.device)
+
+output = model.generate(**inputs_video, max_new_tokens=100, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+```
+
+### Inference with images as inputs
+
+To generate from images use the below code after loading the model as shown above:
+
+```python
+import requests
+from PIL import Image
+
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+conversation = [
+    {
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "nWhat are these?"},
+          {"type": "image"},
+        ],
+    }
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+inputs_image = processor(prompt, images=raw_image, return_tensors='pt').to(0, torch.float16)
+
+output = model.generate(**inputs_image, max_new_tokens=100, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+```
+
+### Inference with images and videos as inputs
+
+To generate from images and videos in one generate use the below code after loading the model as shown above:
+
+```python
+conversation_1 = [
+    {
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What's the content of the image?"},
+          {"type": "image"},
+        ],
+    }
+]
+conversation_2 = [
+    {
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "Why is this video funny?"},
+          {"type": "video"},
+        ],
+    },
+]
+prompt_1 = processor.apply_chat_template(conversation, add_generation_prompt=True)
+prompt_2 = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+s = processor(text=[prompt_1, prompt_2], images=image, videos=clip, padding=True, return_tensors="pt").to(model.device)
+
+# Generate
+generate_ids = model.generate(**inputs, max_new_tokens=100)
+out = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(out)
+```
+
+### Model optimization
+
+#### 4-bit quantization through `bitsandbytes` library
+
+First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: 
+
+```diff
+model = LlavaNextVideoForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   load_in_4bit=True
+)
+```
+
+#### Use Flash-Attention 2 to further speed-up generation
+
+First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: 
+
+```diff
+model = LlavaNextVideoForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   use_flash_attention_2=True
+).to(0)
+```
+
+
+## 🔒 License
+Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+
+## ✏️ Citation
+If you find our paper and code useful in your research:
+
+```BibTeX
+@misc{zhang2024llavanextvideo,
+  title={LLaVA-NeXT: A Strong Zero-shot Video Understanding Model},
+  url={https://llava-vl.github.io/blog/2024-04-30-llava-next-video/},
+  author={Zhang, Yuanhan and Li, Bo and Liu, haotian and Lee, Yong jae and Gui, Liangke and Fu, Di and Feng, Jiashi and Liu, Ziwei and Li, Chunyuan},
+  month={April},
+  year={2024}
+}
+```
+
+```BibTeX
+@misc{liu2024llavanext,
+    title={LLaVA-NeXT: Improved reasoning, OCR, and world knowledge},
+    url={https://llava-vl.github.io/blog/2024-01-30-llava-next/},
+    author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Li, Bo and Zhang, Yuanhan and Shen, Sheng and Lee, Yong Jae},
+    month={January},
+    year={2024}
+}
+```
+