README.md

metadata

license: cc-by-nc-sa-4.0
language:
  - en
base_model:
  - lmms-lab/llava-onevision-qwen2-0.5b-ov
pipeline_tag: video-text-to-text
tags:
  - Action
  - Video
  - MQA
  - multimodal
metrics:
  - accuracy
library_name: transformers

LLaVAction-0.5B

Model Summary

The LLaVAction-0.5B model is trained on EPIC-KITCHENS-100-MQA, based on Qwen2 language model with a context window of 32K tokens.

• Project Page: https://mmathislab.github.io/llavaction/
• Paper: For more details, please check our paper
• Repository: https://github.com/AdaptiveMotorControlLab/LLaVAction
• Point of Contact: Mackenzie Mathis
• Languages: English

Use

Intended use

The model was trained on EPIC-KITCHENS-100-MQA. It's intended to be used on videos that are similar to EPIC-KITCHENS-100.

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.

!pip install llavaction
from llavaction.model.builder import load_pretrained_model
from llavaction.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llavaction.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llavaction.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(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)]
    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]
    spare_frames = vr.get_batch(frame_idx).asnumpy()
    # import pdb;pdb.set_trace()
    return spare_frames,frame_time,video_time
pretrained = "MLAdaptiveIntelligence/LLaVAction-0.5B"
model_name = "llava_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", 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().half()
video = [video]
conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. "
perspective_prompt = "You are seeing this video from egocentric view and you are the person. Your hands are sometimes interacting with objects. What action are you doing?"
task_prompt = "Describe in details what you see from the video frames."
question = DEFAULT_IMAGE_TOKEN + f"\n{time_instruction}\n{perspective_prompt} {task_prompt}"
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)[0].strip()
print(text_outputs)

Training

Model

• Architecture: SO400M + Qwen2
• Initialized Model: lmms-lab/llava-onevision-qwen2-0.5b-ov
• Data: EPIC-KITCHENS-100-MQA, 2 epochs, full model
• Precision: bfloat16

Hardware & Software

GPUs: 32 * Nvidia GH-200 (for whole model series training) Orchestration: HuggingFace Trainer Neural networks: PyTorch

Citation

@article{YeQi2025llavaction,
  title={LLaVAction: evaluating and training multi-modal large language models for action recognition},
  author={Ye, Shaokai and Qi, Haozhe and Mathis, Alexander and Mathis, Mackenzie W.},
  journal={arXiv preprint},
  year={2025}
}