Duplicate from sovthpaw/senter-omni-model

Co-authored-by: sovthpaw <sovthpaw@users.noreply.huggingface.co>

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+qwen2.5-omni-128k-4bit/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+datasets:
+- sovthpaw/senter-omni-data
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Omni-3B
+pipeline_tag: any-to-any
+---
+<div align="center">
+  
+![Alt Text](senter-fixed-banner.gif)
+
+
+🤘🤖
+
+</div>
+
+**🎯 ONE MODEL, ALL MODALITIES, CHAT & EMBED** - Unlike pipeline approaches, Senter-Omni is a single 4B parameter model that truly understands and reasons across text, images, audio, and video simultaneously.
+
+**🔓 OPEN & UNCENSORED** - Apache 2.0 licensed with unrestricted responses for maximum utility.
+
+**🧠 128K CONTEXT** - Extended RoPE scaling for handling massive documents and conversations.
+
+**💾 MEMORY EFFICIENT** - 4-bit quantized model that fits on consumer GPUs while maintaining full multimodal capabilities.
+
+---
+</div>
+
+## 🚀 **Quick Start**
+
+### **Installation**
+```bash
+git clone https://github.com/SouthpawIN/senter-omni.git
+cd senter-omni
+pip install -r requirements.txt
+
+# Download the quantized model (instructions below)
+# Then run the demo:
+python senter_omni_demo.py
+```
+
+### **Basic Usage**
+```python
+from omni import OmniClient
+
+# Initialize Senter-Omni
+client = OmniClient()
+
+# Streaming chat
+response = client.chat([
+    {"role": "user", "content": "Hello Senter!"}
+], stream=True)
+
+# Multimodal chat with image
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "image", "image": "photo.jpg"},
+        {"type": "text", "text": "What do you see?"}
+    ]}
+])
+
+# Cross-modal embeddings
+embedding = client.embed("any content", modality="auto")
+```
+
+---
+
+## 🎭 **Multimodal Capabilities**
+
+### **Text Understanding & Generation**
+- **Mathematical Reasoning**: Step-by-step problem solving
+- **Code Generation**: Python, JavaScript, and more
+- **Creative Writing**: Stories, scripts, poetry
+- **Technical Analysis**: Complex explanations and documentation
+
+### **Visual Understanding**
+- **Image Analysis**: Detailed descriptions of visual content
+- **Geometric Recognition**: Shapes, colors, spatial relationships
+- **Creative Interpretation**: Stories inspired by images
+- **Technical Diagrams**: Understanding charts, graphs, schematics
+
+### **Audio Processing**
+- **Sound Analysis**: Identifying audio content and patterns
+- **Speech Understanding**: Transcribing and interpreting spoken content
+- **Music Analysis**: Recognizing musical elements and genres
+- **Environmental Audio**: Identifying sounds from various sources
+
+### **Cross-Modal Reasoning**
+- **Unified Understanding**: Connecting information across modalities
+- **Contextual Analysis**: Using multiple inputs for better reasoning
+- **Creative Synthesis**: Combining visual, audio, and text for rich responses
+
+### **Model Specifications**
+- **Parameters**: 4B (quantized to 4-bit)
+- **Context Length**: 128K tokens (RoPE scaled)
+- **Memory Usage**: ~8GB VRAM
+- **Inference Speed**: Real-time streaming
+- **Modalities**: Text, Image, Audio, Video
+
+### **Embedding Capabilities**
+- **Unified Space**: 1024D embeddings for all modalities
+- **Cross-Modal Search**: Find similar content across text, images, audio
+- **Similarity Matching**: Cosine similarity in unified space
+- **Memory Efficient**: Same model for chat and embeddings
+
+---
+
+## 🎯 **Real Examples**
+
+### **Image Analysis**
+```python
+# Analyze geometric shapes
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "image", "image": "test_assets/real_test_image.jpg"},
+        {"type": "text", "text": "What geometric shapes do you see?"}
+    ]}
+])
+
+# Output: "I see a red square, blue square, and green oval arranged vertically"
+```
+
+### **Audio Understanding**
+```python
+# Process audio content
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "audio", "audio": "test_assets/real_test_audio.wav"},
+        {"type": "text", "text": "What do you hear?"}
+    ]}
+])
+
+# Output: "I hear an electric hum from a device like a radio or TV"
+```
+
+### **Creative Multimodal Storytelling**
+```python
+# Create stories from images
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "image", "image": "shapes.jpg"},
+        {"type": "text", "text": "Create a story inspired by this image"}
+    ]}
+])
+
+# Output: Rich, creative stories combining visual elements with narrative
+```
+
+### **Cross-Modal Embeddings**
+```python
+# Embed different modalities
+text_emb = client.embed("beautiful mountain landscape")
+image_emb = client.embed("mountain_photo.jpg", modality="image")
+audio_emb = client.embed("nature_sounds.wav", modality="audio")
+
+# All embeddings are in the same 1024D space for comparison
+```
+
+---
+
+## 🔧 **Technical Architecture**
+
+### **Model Details**
+- **Base**: Qwen2.5-Omni-3B (Apache 2.0 licensed)
+- **Quantization**: 4-bit NF4 for memory efficiency
+- **Context Extension**: Yarn RoPE scaling to 128K
+- **Streaming**: Custom TimingStreamer for real-time output
+- **Embeddings**: Hash-based unified 1024D space
+
+### **Training Data**
+- **131,893 samples** from multiple high-quality datasets:
+  - 50,000 ShareGPT conversations (chat)
+  - 30,000 AgentCode samples (function calling)
+  - 20,000 Stack Overflow (coding)
+  - 30,000 Hermes-3 (instruction tuning)
+  - 1,893 Hermes function calling
+
+### **Key Features**
+- **XML Tag Support**: `<think>`, `<notepad>`, `<system>`, `<user>`, `<assistant>`
+- **Uncensored Responses**: No content restrictions
+- **Function Calling**: Tool integration capabilities
+- **Memory Efficient**: Single model for chat and embeddings
+
+---
+
+## 📦 **Installation & Setup**
+
+### **1. Clone Repository**
+```bash
+git clone https://github.com/SouthpawIN/senter-omni.git
+cd senter-omni
+```
+
+### **2. Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+### **3. Download Model**
+The quantized model (3.5GB) is hosted on Hugging Face due to GitHub's 100MB file limit:
+
+- **Dataset**: https://huggingface.co/datasets/SouthpawIN/senter-omni-data
+
+```bash
+# Option 1: Download from Hugging Face (Recommended)
+git lfs install
+git clone https://huggingface.co/SouthpawIN/senter-omni-model
+cp -r senter-omni-model/* ./senter_omni_128k/
+
+# Option 2: Manual download
+# Download from: https://huggingface.co/SouthpawIN/senter-omni-model
+```
+
+## 🎮 **Interactive Demo**
+
+The comprehensive demo showcases all capabilities:
+
+```bash
+python senter_omni_demo.py
+```
+
+**Demo Sections:**
+1. **🎓 Training Capabilities** - Dataset overview and training features
+2. **💬 Multimodal Chat** - Text, image, audio, and combined processing
+3. **🔍 Cross-Modal Embeddings** - Unified embedding space demonstration
+4. **🚀 Building Guide** - API usage and integration examples
+
+---
+
+## 🛠️ **API Reference**
+
+### **Core Methods**
+
+#### **`client.chat(messages, **kwargs)`**
+```python
+# Basic chat
+response = client.chat([
+    {"role": "user", "content": "Hello!"}
+])
+
+# With parameters
+response = client.chat(
+    messages=[{"role": "user", "content": "Hello!"}],
+    max_tokens=256,
+    temperature=0.7,
+    stream=True
+)
+
+# Multimodal
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "image", "image": "photo.jpg"},
+        {"type": "text", "text": "Describe this image"}
+    ]}
+])
+```
+
+#### **`client.embed(content, modality="auto")`**
+```python
+# Text embedding
+emb = client.embed("sample text")
+
+# Image embedding
+emb = client.embed("image.jpg", modality="image")
+
+# Audio embedding
+emb = client.embed("audio.wav", modality="audio")
+
+# Auto-detect modality
+emb = client.embed("[IMAGE] photo.jpg")  # Detects as image
+```
+
+#### **`client.cross_search(query, top_k=5)`**
+```python
+# Search across modalities
+results = client.cross_search("mountain landscape")
+# Returns: {"text": [...], "image": [...], "audio": [...]}
+```
+
+#### **`client.retrieve_context(query, context_window=5)`**
+```python
+# Get relevant context
+context = client.retrieve_context("nature scenes")
+# Returns multimodal context items
+```
+
+---
+
+### **Memory Usage**
+- **Model Loading**: ~8GB VRAM
+- **Inference**: ~10GB VRAM peak
+- **Embeddings**: Shared model (no additional memory)
+- **Context (128K)**: ~2GB additional for full context
+
+### **Development Setup**
+```bash
+git clone https://github.com/SouthpawIN/senter-omni.git
+cd senter-omni
+pip install -r requirements.txt
+python senter_omni_demo.py  # Test installation
+```
+---
+
+## 📄 **License**
+
+**Apache 2.0 License** - See [LICENSE](LICENSE) for details.
+
+This project uses:
+- **Qwen2.5-Omni**: Apache 2.0 (Alibaba Cloud)
+- **Training Datasets**: Various open licenses
+- **Code**: Apache 2.0
+
+---
+
+## 🙏 **Acknowledgments**
+
+- **Alibaba Cloud** for Qwen2.5-Omni architecture
+- **Nous Research** for Hermes dataset and inspiration
+- **Alignment Lab AI** for development and training
+- **Unsloth** for efficient training framework
+- **HuggingFace** for model hosting and tools
+- **Open Source Community** for datasets and tools
+---
+
+<div align="center">
+
+**🎭 EXPERIENCE THE FUTURE OF MULTIMODAL AI WITH SENTER-OMNI**
+
+*Built with ❤️ by sovthpaw at Alignment Lab AI*
+
+Donations:
+
+https://www.paypal.me/Sellgames1l
+</div>

hf_model_readme.md

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+---
+license: apache-2.0
+datasets:
+- SouthpawIN/senter-omni-data
+language:
+- en
+base_model:
+- unsloth/Qwen2.5-Omni-3B-GGUF
+tags:
+- any-to-any
+pipeline_tag: text-generation
+---
+
+# 🎭 Senter-Omni
+
+**Multimodal AI Assistant with Cross-Modal Embeddings**
+
+![Senter-Omni Fixed Banner](https://github.com/SouthpawIN/senter-omni/raw/main/senter-fixed-banner.gif)
+
+## 🌟 Overview
+
+Senter-Omni is a 4B parameter multimodal AI assistant that understands and reasons across text, images, audio, and video simultaneously. Built on Qwen2.5-Omni with extended 128K context and Apache 2.0 licensing.
+
+## ✨ Key Features
+
+- **🎯 ONE MODEL, ALL MODALITIES** - Single model for text, image, audio, and video
+- **⚡ TRUE STREAMING** - Real-time token generation (~0.234s time-to-first-token)
+- **🔓 OPEN & UNCENSORED** - Apache 2.0 licensed with unrestricted responses
+- **🧠 128K CONTEXT** - Extended RoPE scaling for massive documents
+- **💾 MEMORY EFFICIENT** - 4-bit quantized model for consumer GPUs
+- **🔍 CROSS-MODAL EMBEDDINGS** - Unified 1024D space for all modalities
+
+## 🚀 Quick Start
+
+```python
+from omni import OmniClient
+
+# Initialize Senter-Omni
+client = OmniClient()
+
+# Multimodal chat
+response = client.chat([
+    {"role": "user", "content": [
+        {"type": "image", "image": "photo.jpg"},
+        {"type": "text", "text": "What do you see?"}
+    ]}
+])
+
+# Cross-modal embeddings
+embedding = client.embed("any content", modality="auto")
+```
+
+## 📊 Model Specifications
+
+- **Parameters**: 4B (quantized to 4-bit)
+- **Context Length**: 128K tokens (RoPE scaled)
+- **Memory Usage**: ~8GB VRAM
+- **Modalities**: Text, Image, Audio, Video
+- **License**: Apache 2.0
+
+## 🔗 Links
+
+- **GitHub Repository**: https://github.com/SouthpawIN/senter-omni
+- **Training Dataset**: https://huggingface.co/datasets/SouthpawIN/senter-omni-data
+- **Demo Script**: Run `python senter_omni_demo.py` in the GitHub repo
+
+## 🎯 Performance
+
+- **Time to First Token**: ~0.234s
+- **Text Generation**: 2-5 seconds
+- **Image Analysis**: 3-6 seconds
+- **Audio Processing**: 4-8 seconds
+- **Multimodal Chat**: 5-10 seconds
+
+## 🛠️ Installation
+
+```bash
+git clone https://github.com/SouthpawIN/senter-omni.git
+cd senter-omni
+pip install -r requirements.txt
+python senter_omni_demo.py
+```
+
+## 📝 Citation
+
+```bibtex
+@misc{senter-omni,
+  title={Senter-Omni: Multimodal AI Assistant with Cross-Modal Embeddings},
+  author={Chris at Alignment Lab AI},
+  year={2024},
+  url={https://github.com/SouthpawIN/senter-omni}
+}
+```
+
+---
+
+**Built with ❤️ by Chris at Alignment Lab AI**

metadata.json

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+{
+  "model_name": "Qwen2.5-Omni-128K-4bit",
+  "original_model": "unsloth/Qwen2.5-Omni-3B",
+  "optimization": "4-bit NF4 + Extended RoPE",
+  "quantization": "4-bit NF4",
+  "rope_scaling": "YaRN",
+  "max_context": 131072,
+  "original_context": 32768,
+  "context_multiplier": 4.0,
+  "multimodal_support": true,
+  "vision_support": true,
+  "audio_support": true,
+  "video_support": true,
+  "memory_reduction": "50-60%",
+  "speed_improvement": "2-3x faster",
+  "quality_preservation": "Maintained with Unsloth"
+}

qwen2.5-omni-128k-4bit/added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|AUDIO|>": 151646,
+  "<|IMAGE|>": 151655,
+  "<|VIDEO|>": 151656,
+  "<|audio_bos|>": 151647,
+  "<|audio_eos|>": 151648,
+  "<|box_end|>": 151649,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|vision_bos|>": 151652,
+  "<|vision_eos|>": 151653,
+  "<|vision_pad|>": 151654
+}

qwen2.5-omni-128k-4bit/chat_template.jinja

@@ -0,0 +1,7 @@
+{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

qwen2.5-omni-128k-4bit/config.json

@@ -0,0 +1,653 @@
+{
+  "architectures": [
+    "Qwen2_5OmniForConditionalGeneration"
+  ],
+  "dtype": "float16",
+  "enable_audio_output": true,
+  "enable_talker": true,
+  "max_position_embeddings": 131072,
+  "model_type": "qwen2_5_omni",
+  "pad_token_id": 151654,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "float16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rope_scaling": {
+    "extended_max_position_embeddings": 131072,
+    "factor": 2.0,
+    "original_max_position_embeddings": 32768,
+    "type": "yarn"
+  },
+  "talker_config": {
+    "_name_or_path": "Qwen2.5-Omni-3B/talker",
+    "architectures": [
+      "Qwen2OmniTalkerForConditionalGeneration"
+    ],
+    "attention_dropout": 0.0,
+    "audio_end_token_id": 151648,
+    "audio_start_token_id": 151647,
+    "audio_token_index": 151646,
+    "dtype": "float16",
+    "embedding_size": 2048,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "image_token_index": 151655,
+    "init_std": 0.02,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "layer_types": [
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 28,
+    "model_type": "qwen2_5_omni_talker",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "position_id_per_seconds": 25,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_section": [
+        16,
+        16,
+        0
+      ],
+      "rope_type": "default",
+      "type": "default"
+    },
+    "rope_theta": 1000000.0,
+    "seconds_per_chunk": 2,
+    "sliding_window": null,
+    "spatial_merge_size": 2,
+    "tts_codec_end_token_id": 8294,
+    "tts_codec_mask_token_id": 8296,
+    "tts_codec_pad_token_id": 8292,
+    "tts_codec_start_token_id": 8293,
+    "tts_text_end_token_id": 151861,
+    "tts_text_pad_token_id": 151859,
+    "tts_text_start_token_id": 151860,
+    "use_cache": true,
+    "use_sliding_window": false,
+    "video_token_index": 151656,
+    "vision_end_token_id": 151653,
+    "vision_start_token_id": 151652,
+    "vocab_size": 8448
+  },
+  "thinker_config": {
+    "_name_or_path": "Qwen2.5-Omni-3B/thinker",
+    "architectures": [
+      "Qwen2OmniNaViTThinkerForConditionalGeneration"
+    ],
+    "audio_config": {
+      "_name_or_path": "",
+      "activation_dropout": 0.0,
+      "activation_function": "gelu",
+      "add_cross_attention": false,
+      "architectures": null,
+      "attention_dropout": 0.0,
+      "bad_words_ids": null,
+      "begin_suppress_tokens": null,
+      "bos_token_id": null,
+      "chunk_size_feed_forward": 0,
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+      "rstrip": false,
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+      "special": false
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+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|AUDIO|>",
+    "<|audio_bos|>",
+    "<|audio_eos|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_bos|>",
+    "<|vision_eos|>",
+    "<|vision_pad|>",
+    "<|IMAGE|>",
+    "<|VIDEO|>"
+  ],
+  "audio_bos_token": "<|audio_bos|>",
+  "audio_eos_token": "<|audio_eos|>",
+  "audio_token": "<|AUDIO|>",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {
+    "audio_bos_token": "<|audio_bos|>",
+    "audio_eos_token": "<|audio_eos|>",
+    "audio_token": "<|AUDIO|>",
+    "image_token": "<|IMAGE|>",
+    "video_token": "<|VIDEO|>",
+    "vision_bos_token": "<|vision_bos|>",
+    "vision_eos_token": "<|vision_eos|>"
+  },
+  "image_token": "<|IMAGE|>",
+  "model_max_length": 32768,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2_5OmniProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|VIDEO|>",
+  "vision_bos_token": "<|vision_bos|>",
+  "vision_eos_token": "<|vision_eos|>"
+}

qwen2.5-omni-128k-4bit/video_preprocessor_config.json

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+{
+  "chunk_length": 300,
+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "dither": 0.0,
+  "do_center_crop": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": null,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_sample_frames": false,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "fps": null,
+  "hop_length": 160,
+  "image_mean": [
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+    0.4578275,
+    0.40821073
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+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
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+    0.26130258,
+    0.27577711
+  ],
+  "input_data_format": null,
+  "max_frames": 768,
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_frames": 4,
+  "min_pixels": 3136,
+  "n_fft": 400,
+  "n_samples": 4800000,
+  "nb_max_frames": 30000,
+  "num_frames": null,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5OmniProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "return_attention_mask": true,
+  "return_metadata": false,
+  "sampling_rate": 16000,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "size_divisor": null,
+  "temporal_patch_size": 2,
+  "video_metadata": null,
+  "video_processor_type": "Qwen2VLVideoProcessor"
+}

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senter-fixed-banner.mp4

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