Create README.md

README.md

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+---
+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/](https://mmathislab.github.io/llavaction/)
+- **Paper**: For more details, please check our [paper](https://arxiv.org/abs/tbd)
+- **Repository**:  [https://github.com/AdaptiveMotorControlLab/LLaVAction](https://github.com/AdaptiveMotorControlLab/LLaVAction)
+- **Point of Contact**: [Mackenzie Mathis](https://people.epfl.ch/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.
+
+```python
+!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
+
+```bibtex
+@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}
+}
+```