Add pipeline tag, library name, link to paper and project page (#1)

- Add pipeline tag, library name, link to paper and project page (804910e94826b29c41c642dbb1b609431c85c817)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,80 +1,103 @@
----
-license: mit
----
-# M4-LongVA-7B-Qwen2
-
-![images](./assets/framework.png)
-
-Enhancing Interactive Capabilities in MLLM
-
-M4-7B is an extension of [LongVA-7B](https://github.com/EvolvingLMMs-Lab/LongVA), further trained using the [M4-IT](https://huggingface.co/datasets/ColorfulAI/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](https://github.com/patrick-tssn/M4) to install relvevant packages*
-
-```python
-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\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>{query}\n<|im_end|>\n<|im_start|>assistant\n"
-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\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{new_query}\n<|im_end|>\n<|im_start|>assistant\n"
-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](https://github.com/patrick-tssn/M4).
+---
+license: mit
+pipeline_tag: video-text-to-text
+library_name: transformers
+---
+
+# M4-LongVA-7B-Qwen2
+
+[Project Page](https://omnimmi.github.io/)
+
+This is the model described in the paper [OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts](https://huggingface.co/papers/2503.22952).
+
+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.
+
+![images](./assets/framework.png)
+
+Enhancing Interactive Capabilities in MLLM
+
+M4-7B is an extension of [LongVA-7B](https://github.com/EvolvingLMMs-Lab/LongVA), further trained using the [M4-IT](https://huggingface.co/datasets/ColorfulAI/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](https://github.com/patrick-tssn/M4) to install relvevant packages*
+
+```python
+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](https://github.com/patrick-tssn/M4).