Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: video-text-to-text
+tags:
+- video
+- video-understanding
+- vision
+- multimodal
+- conversational
+- custom_code
+- instruction-tuning
+library_name: transformers
+---
+
+# Apollo: An Exploration of Video Understanding in Large Multimodal Models
+
+Apollo is a family of Large Multimodal Models (LMMs) that push the state-of-the-art in video understanding. It supports tasks including:
+- Long-form video comprehension
+- Temporal reasoning
+- Complex video question-answering
+- Multi-turn conversations grounded in video content
+
+Apollo models excel at handling hour-long videos, balancing speed and accuracy through strategic design decisions. Our models outperform most 7B competitors at just 3B parameters and even rival 30B-scale models.
+
+
+
+**Key Highlights:**
+- **7B model varient**
+- **32 tokens/frame**
+
+## Quick Start
+
+**Installation:**
+```bash
+pip install -e .
+pip install flash-attn --no-build-isolation
+```
+
+**Inference Example:**
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from apollo.mm_utils import (
+    KeywordsStoppingCriteria,
+    tokenizer_mm_token,
+    ApolloMMLoader
+)
+from apollo.conversations import conv_templates, SeparatorStyle
+from huggingface_hub import snapshot_download
+
+model_url = "Apollo-LMMs/Apollo-3B-t32"
+model_path = snapshot_download(model_url, repo_type="model")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True
+).to(device=device, dtype=torch.bfloat16)
+
+tokenizer = model.tokenizer
+vision_processors = model.vision_tower.vision_processor
+config = model.config
+num_repeat_token = config.mm_connector_cfg['num_output_tokens']
+mm_processor = ApolloMMLoader(
+    vision_processors,
+    config.clip_duration,
+    frames_per_clip=4,
+    clip_sampling_ratio=0.65,
+    model_max_length=config.model_max_length,
+    device=device,
+    num_repeat_token=num_repeat_token
+)
+
+video_path = "path/to/video.mp4"
+question = "Describe this video in detail"
+mm_data, replace_string = mm_processor.load_video(video_path)
+
+conv = conv_templates["qwen_2"].copy()
+conv.append_message(conv.roles[0], replace_string + "\n\n" + question)
+conv.append_message(conv.roles[1], None)
+
+prompt = conv.get_prompt()
+input_ids = tokenizer_mm_token(prompt, tokenizer, return_tensors="pt").unsqueeze(0).to(device)
+
+stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids)
+
+with torch.inference_mode():
+    output_ids = model.generate(
+        input_ids,
+        vision_input=[mm_data],
+        data_types=['video'],
+        do_sample=True,
+        temperature=0.4,
+        max_new_tokens=256,
+        top_p=0.7,
+        use_cache=True,
+        num_beams=1,
+        stopping_criteria=[stopping_criteria]
+    )
+
+pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+print(pred)
+```
+
+
+For more details, visit the [project website](https://apollo-lmms.github.io) or check out the [paper](https://arxiv.org/abs/2412.10360).