M4-LongVA-7B-Qwen2

M4-LongVA-7B-Qwen2

Project Page

This is the model described in the paper OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts.

The abstract of the paper is the following:

The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this work, we introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts. OmniMMI encompasses over 1,121 videos and 2,290 questions, addressing two critical yet underexplored challenges in existing video benchmarks: streaming video understanding and proactive reasoning, across six distinct subtasks. Moreover, we propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enable an inference-efficient streaming model that can see, listen while generating.

Enhancing Interactive Capabilities in MLLM

M4-7B is an extension of LongVA-7B, further trained using the M4-IT dataset, which comprises 9,963 visual instruction tuning instances. This training was conducted without any special modifications to the existing training pipeline.

Usage

Please refer to M4 to install relvevant packages

import os
from PIL import Image
import numpy as np
import torchaudio
import torch
from decord import VideoReader, cpu
import whisper
# fix seed
torch.manual_seed(0)

from intersuit.model.builder import load_pretrained_model
from intersuit.mm_utils import tokenizer_image_speech_tokens, process_images
from intersuit.constants import IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX


import warnings
warnings.filterwarnings("ignore")

model_path = "checkpoints/M4-LongVA-7B-Qwen2"
video_path = "local_demo/assets/water.mp4"
max_frames_num = 16 # you can change this to several thousands so long you GPU memory can handle it :)
gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "llava_qwen", device_map="cuda:0", attn_implementation="eager")

# original query
query = "Give a detailed caption of the video as if I am blind."
prompt = f"<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
<image>{query}
<|im_end|>
<|im_start|>assistant
"
input_ids = tokenizer_image_speech_tokens(prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
pad_token_ids = (tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id)
attention_masks = input_ids.ne(pad_token_ids).to(input_ids.device)

# new query
new_query = "How many people in the video?"
new_query = "Okay, I see."
new_query = "Sorry to interrupt."
new_query_pos = 10 # which token encounter the new query
new_prompt = f"<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{new_query}
<|im_end|>
<|im_start|>assistant
"
new_input_ids = tokenizer_image_speech_tokens(new_prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)

#video input
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
video_tensor = image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].to(model.device, dtype=torch.bfloat16)


with torch.inference_mode():
    output_ids = model.generate_parallel(input_ids, 
                                attention_mask=attention_masks,
                                images=[video_tensor], 
                                modalities=["video"], 
                                new_query=new_input_ids,
                                new_query_pos=new_query_pos,
                                query_str=query,
                                new_query_str=new_query,
                                tokenizer=tokenizer,
                                **gen_kwargs)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()

For more information about the interaction inference pipeline, please visit the M4 GitHub repository.