Improve model card title, pipeline tag, and GitHub link

README.md

@@ -1,10 +1,11 @@
 ---
-pipeline_tag: image-text-to-text
 datasets:
 - openbmb/RLAIF-V-Dataset
-library_name: transformers
 language:
 - multilingual
+library_name: transformers
+license: apache-2.0
+pipeline_tag: video-text-to-text
 tags:
 - minicpm-v
 - vision
@@ -12,12 +13,13 @@ tags:
 - multi-image
 - video
 - custom_code
-license: apache-2.0
 ---
 
-<h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
+# MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
 
-[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Technical Report](https://huggingface.co/papers/2509.18154) | [Demo](http://101.126.42.235:30910/) </a> 
+A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone.
+
+[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Technical Report](https://huggingface.co/papers/2509.18154) | [Demo](http://101.126.42.235:30910/) </a> 
 
 
 
@@ -50,7 +52,7 @@ MiniCPM-V 4.5 can be easily used in various ways: (1) [llama.cpp](https://github
 
 - **Pre-training: Unified Learning for OCR and Knowledge from Documents.** Existing MLLMs learn OCR capability and knowledge from documents in isolated training approaches. We observe that the essential difference between these two training approaches is the visibility of the text in images. By dynamically corrupting text regions in documents with varying noise levels and asking the model to reconstruct the text, the model learns to adaptively and properly switch between accurate text recognition (when text is visible) and multimodal context-based knowledge reasoning (when text is heavily obscured). This eliminates reliance on error-prone document parsers in knowledge learning from documents, and prevents hallucinations from over-augmented OCR data, resulting in top-tier OCR and multimodal knowledge performance with minimal engineering overhead.
 
-- **Post-training: Hybrid Fast/Deep Thinking with Multimodal RL.** MiniCPM-V 4.5 offers a balanced reasoning experience through two switchable modes: fast thinking for efficient daily use and deep thinking for complex tasks. Using a new hybrid reinforcement learning method, the model jointly optimizes both modes, significantly enhancing fast-mode performance without compromising deep-mode capability. Incorporated with [RLPR](https://github.com/OpenBMB/RLPR) and [RLAIF-V](https://github.com/RLHF-V/RLAIF-V), it generalizes robust reasoning skills from broad multimodal data while effectively reducing hallucinations.
+- **Post-training: Hybrid Fast/Deep Thinking with Multimodal RL.** MiniCPM-V 4.5 offers a balanced reasoning experience through two switchable modes: fast thinking for efficient daily use and deep thinking for more complex tasks. Using a new hybrid reinforcement learning method, the model jointly optimizes both modes, significantly enhancing fast-mode performance without compromising deep-mode capability. Incorporated with [RLPR](https://github.com/OpenBMB/RLPR) and [RLAIF-V](https://github.com/RLHF-V/RLAIF-V), it generalizes robust reasoning skills from broad multimodal data while effectively reducing hallucinations.
 
 ### Evaluation
 
@@ -150,362 +152,1865 @@ Both Video-MME and OpenCompass were evaluated using 8×A100 GPUs for inference.
   <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case3.jpeg" alt="en_case3" style="margin-bottom: 5px;">
 </div>
 
-We deploy MiniCPM-V 4.5 on iPad M4 with [iOS demo](https://github.com/tc-mb/MiniCPM-o-demo-iOS). The demo video is the raw screen recording without editing.
-
-<div align="center">
-  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
-  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_cot.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
+<details>
+<summary>Click to view more cases.</summary>
+<div style="display: flex; flex-direction: column; align-items: center;">
+  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/zh_extra.jpeg" alt="zh_extra" style="margin-bottom: 5px;">
 </div>
 
-<div align="center">
-  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
-  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_travel.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
-</div> 
-
-## Framework Support Matrix
-<table>
-  <thead>
-    <tr>
-      <th>Category</th>
-      <th>Framework</th>
-      <th>Cookbook Link</th>
-      <th>Upstream PR</th>
-      <th>Supported since(branch)</th>
-      <th>Supported since(release)</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td rowspan="2">Edge(On-device)</td>
-      <td>Llama.cpp</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-v4_5_llamacpp.md">Llama.cpp Doc</a></td>
-      <td><a href="https://github.com/ggml-org/llama.cpp/pull/15575">#15575</a>(2025-08-26)</td>
-      <td>master(2025-08-26)</td>
-      <td><a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6282">b6282</a></td>
-    </tr>
-    <tr>
-      <td>Ollama</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-v4_5_ollama.md">Ollama Doc</a></td>
-      <td><a href="https://github.com/ollama/ollama/pull/12078">#12078</a>(2025-08-26)</td>
-      <td>Merging</td>
-      <td>Waiting for official release</td>
-    </tr>
-    <tr>
-      <td rowspan="2">Serving(Cloud)</td>
-      <td>vLLM</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-v4_5_vllm.md">vLLM Doc</a></td>
-      <td><a href="https://github.com/vllm-project/vllm/pull/23586">#23586</a>(2025-08-26)</td>
-      <td>main(2025-08-27)</td>
-      <td><a href="https://github.com/vllm-project/vllm/releases/tag/v0.10.2">v0.10.2</td>
-    </tr>
-    <tr>
-      <td>SGLang</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-v4_5_sglang.md">SGLang Doc</a></td>
-      <td><a href="https://github.com/sgl-project/sglang/pull/9610">#9610</a>(2025-08-26)</td>
-      <td>Merging</td>
-      <td>Waiting for official release</td>
-    </tr>
-    <tr>
-      <td>Finetuning</td>
-      <td>LLaMA-Factory</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_llamafactory.md">LLaMA-Factory Doc</a></td>
-      <td><a href="https://github.com/hiyouga/LLaMA-Factory/pull/9022">#9022</a>(2025-08-26)</td>
-      <td>main(2025-08-26)</td>
-      <td>Waiting for official release</td>
-    </tr>
-    <tr>
-      <td rowspan="3">Quantization</td>
-      <td>GGUF</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-v4_5_gguf_quantize.md">GGUF Doc</a></td>
-      <td>—</td>
-      <td>—</td>
-      <td>—</td>
-    </tr>
-    <tr>
-      <td>BNB</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/bnb/minicpm-v4_5_bnb_quantize.md">BNB Doc</a></td>
-      <td>—</td>
-      <td>—</td>
-      <td>—</td>
-    </tr>
-    <tr>
-      <td>AWQ</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/awq/minicpm-v4_5_awq_quantize.md">AWQ Doc</a></td>
-      <td>—</td>
-      <td>—</td>
-      <td>—</td>
-    </tr>
-    <tr>
-      <td>Demos</td>
-      <td>Gradio Demo</td>
-      <td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/gradio/README.md">Gradio Demo Doc</a></td>
-      <td>—</td>
-      <td>—</td>
-      <td>—</td>
-    </tr>
-  </tbody>
- </table>
- 
-> Note: If you'd like us to prioritize support for another open-source framework, please let us know via this [short form](https://docs.google.com/forms/d/e/1FAIpQLSdyTUrOPBgWqPexs3ORrg47ZcZ1r4vFQaA4ve2iA7L9sMfMWw/viewform).
-
-## Usage
-
-If you wish to enable thinking mode, provide the argument `enable_thinking=True` to the chat function.
-
-#### Chat with Image
-```python
-import torch
-from PIL import Image
-from transformers import AutoModel, AutoTokenizer
-
-torch.manual_seed(100)
-
-model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
-    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
-model = model.eval().cuda()
-tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
-
-image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')
-
-enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.
-stream=True # If `stream=True`, the answer is string
-
-# First round chat 
-question = "What is the landform in the picture?"
-msgs = [{'role': 'user', 'content': [image, question]}]
-
-answer = model.chat(
-    msgs=msgs,
-    tokenizer=tokenizer,
-    enable_thinking=enable_thinking,
-    stream=True
-)
-
-generated_text = ""
-for new_text in answer:
-    generated_text += new_text
-    print(new_text, flush=True, end='')
-
-# Second round chat, pass history context of multi-turn conversation
-msgs.append({"role": "assistant", "content": [generated_text]})
-msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
-
-answer = model.chat(
-    msgs=msgs,
-    tokenizer=tokenizer,
-    stream=True
-)
-
-generated_text = ""
-for new_text in answer:
-    generated_text += new_text
-    print(new_text, flush=True, end='')
-```
-
-You will get the following output:
-
-```shell
-# round1
-The landform in the picture is karst topography. Karst landscapes are characterized by distinctive, jagged limestone hills or mountains with steep, irregular peaks and deep valleys—exactly what you see here These unique formations result from the dissolution of soluble rocks like limestone over millions of years through water erosion.
-
-This scene closely resembles the famous karst landscape of Guilin and Yangshuo in China’s Guangxi Province. The area features dramatic, pointed limestone peaks rising dramatically above serene rivers and lush green forests, creating a breathtaking and iconic natural beauty that attracts millions of visitors each year for its picturesque views.
-
-# round2
-When traveling to a karst landscape like this, here are some important tips:
+</details>
 
-1. Wear comfortable shoes: The terrain can be uneven and hilly.
-2. Bring water and snacks for energy during hikes or boat rides.
-3. Protect yourself from the sun with sunscreen, hats, and sunglasses—especially since you’ll likely spend time outdoors exploring scenic spots.
-4. Respect local customs and nature regulations by not littering or disturbing wildlife.
+We deploy MiniCPM-V 4.5 on iPad M4 with [iOS demo](https://github.com/tc-mb/MiniCPM-o-demo-iOS). The demo video is the raw screen recording without editing.
 
-By following these guidelines, you'll have a safe and enjoyable trip while appreciating the stunning natural beauty of places such as Guilin’s karst mountains.
-```
+<table align="center"> 
+    <p align="center">
+      <img src="assets/minicpmv4_5/v45_en_handwriting.gif" width=45%/>
+      &nbsp;&nbsp;&nbsp;&nbsp;
+      <img src="assets/minicpmv4_5/v45_en_cot.gif" width=45%/>
+    </p>
+    <p align="center">
+      <img src="assets/minicpmv4_5/v45_cn_handwriting.gif" width=45%/>
+      &nbsp;&nbsp;&nbsp;&nbsp;
+      <img src="assets/minicpmv4_5/v45_cn_travel.gif" width=45%/>
+    </p>
+</table>
 
+## MiniCPM-o 2.6
 
-#### Chat with Video
+**MiniCPM-o 2.6** is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming. Notable features of MiniCPM-o 2.6 include:
 
-```python
-## The 3d-resampler compresses multiple frames into 64 tokens by introducing temporal_ids. 
-# To achieve this, you need to organize your video data into two corresponding sequences: 
-#   frames: List[Image]
-#   temporal_ids: List[List[Int]].
+- 🔥 **Leading Visual Capability.**
+  MiniCPM-o 2.6 achieves an average score of 70.2 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-202405, Gemini 1.5 Pro, and Claude 3.5 Sonnet** for single image understanding. It also **outperforms GPT-4V and Claude 3.5 Sonnet** in multi-image and video understanding, and shows promising in-context learning capability.
 
-import torch
-from PIL import Image
-from transformers import AutoModel, AutoTokenizer
-from decord import VideoReader, cpu    # pip install decord
-from scipy.spatial import cKDTree
-import numpy as np
-import math
+- 🎙 **State-of-the-art Speech Capability.** MiniCPM-o 2.6 supports **bilingual real-time speech conversation with configurable voices** in English and Chinese. It **outperforms GPT-4o-realtime on audio understanding tasks** such as ASR and STT translation, and shows **state-of-the-art performance on speech conversation in both semantic and acoustic evaluations in the open-source community**. It also allows for fun features such as emotion/speed/style control, end-to-end voice cloning, role play, etc.
 
-model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,  # or openbmb/MiniCPM-o-2_6
-    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
-model = model.eval().cuda()
-tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)  # or openbmb/MiniCPM-o-2_6
+- 🎬 **Strong Multimodal Live Streaming Capability.** As a new feature, MiniCPM-o 2.6 can **accept continuous video and audio streams independent of user queries, and support real-time speech interaction**. It **outperforms GPT-4o-202408 and Claude 3.5 Sonnet and shows state-of-the-art performance in the open-source community on StreamingBench**, a comprehensive benchmark for real-time video understanding, omni-source (video & audio) understanding, and multimodal contextual understanding.
 
-MAX_NUM_FRAMES=180 # Indicates the maximum number of frames received after the videos are packed. The actual maximum number of valid frames is MAX_NUM_FRAMES * MAX_NUM_PACKING.
-MAX_NUM_PACKING=3  # indicates the maximum packing number of video frames. valid range: 1-6
-TIME_SCALE = 0.1 
+- 💪 **Strong OCR Capability and Others.**
+Advancing popular visual capabilities from MiniCPM-V series, MiniCPM-o 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves **state-of-the-art performance on OCRBench for models under 25B, surpassing proprietary models such as GPT-4o-202405**.
+  Based on the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, outperforming GPT-4o and Claude 3.5 Sonnet on MMHal-Bench, and supports **multilingual capabilities** on more than 30 languages.
 
-def map_to_nearest_scale(values, scale):
-    tree = cKDTree(np.asarray(scale)[:, None])
-    _, indices = tree.query(np.asarray(values)[:, None])
-    return np.asarray(scale)[indices]
 
+- 🚀 **Superior Efficiency.**
+  In addition to its friendly size, MiniCPM-o 2.6 also shows **state-of-the-art token density** (i.e., the number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support **multimodal live streaming** on end-side devices such as iPads.
 
-def group_array(arr, size):
-    return [arr[i:i+size] for i in range(0, len(arr), size)]
+- 💫 **Easy Usage.**
+MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
 
-def encode_video(video_path, choose_fps=3, force_packing=None):
-    def uniform_sample(l, n):
-        gap = len(l) / n
-        idxs = [int(i * gap + gap / 2) for i in range(n)]
-        return [l[i] for i in idxs]
-    vr = VideoReader(video_path, ctx=cpu(0))
-    fps = vr.get_avg_fps()
-    video_duration = len(vr) / fps
-        
-    if choose_fps * int(video_duration) <= MAX_NUM_FRAMES:
-        packing_nums = 1
-        choose_frames = round(min(choose_fps, round(fps)) * min(MAX_NUM_FRAMES, video_duration))
-        
-    else:
-        packing_nums = math.ceil(video_duration * choose_fps / MAX_NUM_FRAMES)
-        if packing_nums <= MAX_NUM_PACKING:
-            choose_frames = round(video_duration * choose_fps)
-        else:
-            choose_frames = round(MAX_NUM_FRAMES * MAX_NUM_PACKING)
-            packing_nums = MAX_NUM_PACKING
+**Model Architecture.**
 
-    frame_idx = [i for i in range(0, len(vr))]      
-    frame_idx =  np.array(uniform_sample(frame_idx, choose_frames))
+- **End-to-end Omni-modal Architecture.** Different modality encoders/decoders are connected and trained in an **end-to-end** fashion to fully exploit rich multimodal knowledge. The model is trained in a fully end-to-end manner with only CE loss.
+- **Omni-modal Live Streaming Mechanism.** (1) We change the offline modality encoder/decoders into online ones for **streaming inputs/outputs.** (2) We devise a **time-division multiplexing (TDM) mechanism** for omni-modality streaming processing in the LLM backbone. It divides parallel omni-modality streams into sequential info within small periodic time slices. 
+- **Configurable Speech Modeling Design.** We devise a multimodal system prompt, including traditional text system prompt, and **a new audio system prompt to determine the assistant voice**. This enables flexible voice configurations in inference time, and also facilitates end-to-end voice cloning and description-based voice creation.
 
-    if force_packing:
-        packing_nums = min(force_packing, MAX_NUM_PACKING)
-    
-    print(video_path, ' duration:', video_duration)
-    print(f'get video frames={len(frame_idx)}, packing_nums={packing_nums}')
-    
-    frames = vr.get_batch(frame_idx).asnumpy()
+<div align="center">
+<img src="./assets/minicpm-o-26-framework-v2.png" , width=80%>
+</div>
 
-    frame_idx_ts = frame_idx / fps
-    scale = np.arange(0, video_duration, TIME_SCALE)
 
-    frame_ts_id = map_to_nearest_scale(frame_idx_ts, scale) / TIME_SCALE
-    frame_ts_id = frame_ts_id.astype(np.int32)
+### Evaluation  <!-- omit in toc -->
 
-    assert len(frames) == len(frame_ts_id)
+<div align="center">
+  <img src="./assets/radar.jpg", width=80%>
+</div>
 
-    frames = [Image.fromarray(v.astype('uint8')).convert('RGB') for v in frames]
-    frame_ts_id_group = group_array(frame_ts_id, packing_nums)
-    
-    return frames, frame_ts_id_group
+<details>
+<summary>Click to view visual understanding results.</summary>
 
+**Image Understanding**
 
-video_path="video_test.mp4"
-fps = 5 # fps for video
-force_packing = None # You can set force_packing to ensure that 3D packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
-frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
+<div align="center">
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Model</th>
+            <th>Size</th>
+            <th>Token Density<sup>+</sup></th>
+            <th>OpenCompass</th>
+            <th>OCRBench</th>
+            <th>MathVista mini</th>
+            <th>ChartQA</th>
+            <th>MMVet</th>
+            <th>MMStar</th>
+            <th>MME</th>
+            <th>MMB1.1 test</th>
+            <th>AI2D</th>
+            <th>MMMU val</th>
+            <th>HallusionBench</th>
+            <th>TextVQA val</th>
+            <th>DocVQA test</th>
+            <th>MathVerse mini</th>
+            <th>MathVision</th>
+            <th>MMHal Score</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td colspan="19" align="left"><strong>Proprietary</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-20240513</td>
+            <td>-</td>
+            <td>1088</td>
+            <td><u>69.9</u></td>
+            <td>736</td>
+            <td>61.3</td>
+            <td>85.7</td>
+            <td><strong>69.1</strong></td>
+            <td>63.9</td>
+            <td>2328.7</td>
+            <td>82.2</td>
+            <td>84.6</td>
+            <td><strong>69.2</strong></td>
+            <td><strong>55.0</strong></td>
+            <td>-</td>
+            <td>92.8</td>
+            <td><strong>50.2</strong></td>
+            <td><strong>30.4</strong></td>
+            <td><u>3.6</u></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Claude3.5-Sonnet</td>
+            <td>-</td>
+            <td>750</td>
+            <td>67.9</td>
+            <td>788</td>
+            <td>61.6</td>
+            <td><strong>90.8</strong></td>
+            <td>66.0</td>
+            <td>62.2</td>
+            <td>1920.0</td>
+            <td>78.5</td>
+            <td>80.2</td>
+            <td><u>65.9</u></td>
+            <td>49.9</td>
+            <td>-</td>
+            <td><strong>95.2</strong></td>
+            <td>-</td>
+            <td>-</td>
+            <td>3.4</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
+            <td>-</td>
+            <td>-</td>
+            <td>64.4</td>
+            <td>754</td>
+            <td>57.7</td>
+            <td>81.3</td>
+            <td>64.0</td>
+            <td>59.1</td>
+            <td>2110.6</td>
+            <td>73.9</td>
+            <td>79.1</td>
+            <td>60.6</td>
+            <td>45.6</td>
+            <td>73.5</td>
+            <td>86.5</td>
+            <td>-</td>
+            <td>19.2</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-mini-20240718</td>
+            <td>-</td>
+            <td>1088</td>
+            <td>64.1</td>
+            <td>785</td>
+            <td>52.4</td>
+            <td>-</td>
+            <td>66.9</td>
+            <td>54.8</td>
+            <td>2003.4</td>
+            <td>76.0</td>
+            <td>77.8</td>
+            <td>60.0</td>
+            <td>46.1</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>3.3</td>
+        </tr>
+        <tr>
+            <td colspan="19" align="left"><strong>Open Source</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Cambrian-34B</td>
+            <td>34B</td>
+            <td><u>1820</u></td>
+            <td>58.3</td>
+            <td>591</td>
+            <td>50.3</td>
+            <td>75.6</td>
+            <td>53.2</td>
+            <td>54.2</td>
+            <td>2049.9</td>
+            <td>77.8</td>
+            <td>79.5</td>
+            <td>50.4</td>
+            <td>41.6</td>
+            <td>76.7</td>
+            <td>75.5</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GLM-4V-9B</td>
+            <td>13B</td>
+            <td>784</td>
+            <td>59.1</td>
+            <td>776</td>
+            <td>51.1</td>
+            <td>-</td>
+            <td>58.0</td>
+            <td>54.8</td>
+            <td>2018.8</td>
+            <td>67.9</td>
+            <td>71.2</td>
+            <td>46.9</td>
+            <td>45.0</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Pixtral-12B</td>
+            <td>12B</td>
+            <td>256</td>
+            <td>61.0</td>
+            <td>685</td>
+            <td>56.9</td>
+            <td>81.8</td>
+            <td>58.5</td>
+            <td>54.5</td>
+            <td>-</td>
+            <td>72.7</td>
+            <td>79.0</td>
+            <td>51.1</td>
+            <td>47.0</td>
+            <td>75.7</td>
+            <td>90.7</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">VITA-1.5</td>
+            <td>8B</td>
+            <td>784</td>
+            <td>63.3</td>
+            <td>741</td>
+            <td>66.2</td>
+            <td>-</td>
+            <td>52.7</td>
+            <td>60.2</td>
+            <td>2328.1</td>
+            <td>76.8</td>
+            <td>79.2</td>
+            <td>52.6</td>
+            <td>44.6</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">DeepSeek-VL2-27B (4B)</td>
+            <td>27B</td>
+            <td>672</td>
+            <td>66.4</td>
+            <td>809</td>
+            <td>63.9</td>
+            <td>86.0</td>
+            <td>60.0</td>
+            <td>61.9</td>
+            <td>2253.0</td>
+            <td>81.2</td>
+            <td>83.8</td>
+            <td>54.0</td>
+            <td>45.3</td>
+            <td><u>84.2</u></td>
+            <td>93.3</td>
+            <td>-</td>
+            <td>-</td>
+            <td>3.0</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
+            <td>8B</td>
+            <td>784</td>
+            <td>67.1</td>
+            <td><u>866</u></td>
+            <td>58.2</td>
+            <td>83.0</td>
+            <td>62.0</td>
+            <td>60.7</td>
+            <td>2326.0</td>
+            <td>81.8</td>
+            <td>83.0</td>
+            <td>54.1</td>
+            <td>50.6</td>
+            <td><strong>84.3</strong></td>
+            <td><u>94.5</u></td>
+            <td>31.9</td>
+            <td>16.3</td>
+            <td>3.2</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LLaVA-OneVision-72B</td>
+            <td>72B</td>
+            <td>182</td>
+            <td>68.1</td>
+            <td>741</td>
+            <td>67.5</td>
+            <td>83.7</td>
+            <td>60.6</td>
+            <td><strong>65.8</strong></td>
+            <td>2261.0</td>
+            <td><strong>85.0</strong></td>
+            <td><u>85.6</u></td>
+            <td>56.8</td>
+            <td>49.0</td>
+            <td>80.5</td>
+            <td>91.3</td>
+            <td>39.1</td>
+            <td>-</td>
+            <td>3.5</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">InternVL2.5-8B</td>
+            <td>8B</td>
+            <td>706</td>
+            <td>68.3</td>
+            <td>822</td>
+            <td><u>64.4</u></td>
+            <td>84.8</td>
+            <td>62.8</td>
+            <td>62.8</td>
+            <td>2344.0</td>
+            <td><u>83.6</u></td>
+            <td>84.5</td>
+            <td>56.0</td>
+            <td>50.1</td>
+            <td>79.1</td>
+            <td>93.0</td>
+            <td>39.5</td>
+            <td>19.7</td>
+            <td>3.4</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
+            <td>8B</td>
+            <td><strong>2822</strong></td>
+            <td>65.2</td>
+            <td>852*</td>
+            <td>60.6</td>
+            <td>79.4</td>
+            <td>60.0</td>
+            <td>57.5</td>
+            <td><u>2348.4*</u></td>
+            <td>78.0</td>
+            <td>82.1</td>
+            <td>49.8*</td>
+            <td>48.1*</td>
+            <td>80.1</td>
+            <td>90.8</td>
+            <td>25.7</td>
+            <td>18.3</td>
+            <td>3.6</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>8B</td>
+            <td><strong>2822</strong></td>
+            <td><strong>70.2</strong></td>
+            <td><strong>897*</strong></td>
+            <td><strong>71.9*</strong></td>
+            <td><u>86.9*</u></td>
+            <td><u>67.5</u></td>
+            <td><u>64.0</u></td>
+            <td><strong>2372.0*</strong></td>
+            <td>80.5</td>
+            <td><strong>85.8</strong></td>
+            <td>50.4*</td>
+            <td><u>51.9</u></td>
+            <td>82.0</td>
+            <td>93.5</td>
+            <td><u>41.4*</u></td>
+            <td><u>23.1*</u></td>
+            <td><strong>3.8</strong></td>
+        </tr>
+    </tbody>
+</table>
+</div>
+* We evaluate this benchmark using chain-of-thought prompting. Specifically, for MME, we used this technique only for the Cognition set.
 
-question = "Describe the video"
-msgs = [
-    {'role': 'user', 'content': frames + [question]}, 
-]
 
+<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens.
 
-answer = model.chat(
-    msgs=msgs,
-    tokenizer=tokenizer,
-    use_image_id=False,
-    max_slice_nums=1,
-    temporal_ids=frame_ts_id_group
-)
-print(answer)
-```
+Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation.
 
-#### Chat with multiple images
-<details>
-<summary> Click to show Python code running MiniCPM-V 4.5 with multiple images input. </summary>
-  
-```python
-import torch
-from PIL import Image
-from transformers import AutoModel, AutoTokenizer
 
-model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
-    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2
-model = model.eval().cuda()
-tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
+**Multi-image and Video Understanding**
 
-image1 = Image.open('image1.jpg').convert('RGB')
-image2 = Image.open('image2.jpg').convert('RGB')
-question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
+<div align="center">
+ 
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Model</th>
+            <th>Size</th>
+            <th>BLINK val</th>
+            <th>Mantis Eval</th>
+            <th>MIRB</th>
+            <th>Video-MME (wo / w subs)</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td colspan="6" align="left"><strong>Proprietary</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-20240513</td>
+            <td>-</td>
+            <td><strong>68.0</strong></td>
+            <td>-</td>
+            <td>-</td>
+            <td><strong>71.9/77.2<strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT4V</td>
+            <td>-</td>
+            <td>54.6</td>
+            <td>62.7</td>
+            <td>53.1</td>
+            <td>59.9/63.3</td>
+        </tr>
+        <tr>
+            <td colspan="6" align="left"><strong>Open-source</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">VITA-1.5</td>
+            <td>8B</td>
+            <td>45.0</td>
+            <td>-</td>
+            <td>-</td>
+            <td>56.1/58.7</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LLaVA-NeXT-Interleave 14B</td>
+            <td>14B</td>
+            <td>52.6</td>
+            <td>66.4</td>
+            <td>30.2</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LLaVA-OneVision-72B</td>
+            <td>72B</td>
+            <td>55.4</td>
+            <td><strong>77.6</strong></td>
+            <td>-</td>
+            <td><u>66.2/69.5</u></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MANTIS 8B</td>
+            <td>8B</td>
+            <td>49.1</td>
+            <td>59.5</td>
+            <td>34.8</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
+            <td>8B</td>
+            <td>53.2</td>
+            <td>69.6*</td>
+            <td><strong>67.6*</strong></td>
+            <td>63.3/69.0</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">InternVL2.5-8B</td>
+            <td>8B</td>
+            <td>54.8</td>
+            <td>67.7</td>
+            <td>52.5</td>
+            <td>64.2/66.9</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
+            <td>8B</td>
+            <td>53.0</td>
+            <td>69.1</td>
+            <td>53.8</td>
+            <td>60.9/63.6</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>8B</td>
+            <td><u>56.7</u></td>
+            <td><u>71.9</u></td>
+            <td><u>58.6</u></td>
+            <td>63.9/67.9</td>
+        </tr>
+    </tbody>
+</table>
 
-msgs = [{'role': 'user', 'content': [image1, image2, question]}]
+</div>
+* We evaluate officially released checkpoints by ourselves.
 
-answer = model.chat(
-    msgs=msgs,
-    tokenizer=tokenizer
-)
-print(answer)
-```
 </details>
 
 
-#### In-context few-shot learning
 <details>
-<summary> Click to view Python code running MiniCPM-V 4.5 with few-shot input. </summary>
+<summary>Click to view audio understanding and speech conversation results.</summary>
 
-```python
-import torch
-from PIL import Image
-from transformers import AutoModel, AutoTokenizer
+**Audio Understanding**
 
-model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
-    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
-model = model.eval().cuda()
-tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
+<div align="center">
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Task</th>
+            <th>Size</th>
+            <th colspan="3">ASR (zh)</th>
+            <th colspan="3">ASR (en)</th>
+            <th colspan="2">AST</th>
+            <th>Emotion</th>
+        </tr>
+        <tr>
+            <th align="left">Metric</th>
+            <td></td>
+            <th colspan="3">CER↓</th>
+            <th colspan="3">WER↓</th>
+            <th colspan="2">BLEU↑</th>
+            <th>ACC↑</th>
+        </tr>
+        <tr>
+            <th align="left">Dataset</th>
+            <td></td>
+            <th>AISHELL-1</th>
+            <th>Fleurs zh</th>
+            <th>WenetSpeech test-net</th>
+            <th>LibriSpeech test-clean</th>
+            <th>GigaSpeech</th>
+            <th>TED-LIUM</th>
+            <th>CoVoST en2zh</th>
+            <th>CoVoST zh2en</th>
+            <th>MELD emotion</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td colspan="11" align="left"><strong>Proprietary</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-Realtime</td>
+            <td>-</td>
+            <td>7.3*</td>
+            <td><u>5.4*</u></td>
+            <td>28.9*</td>
+            <td>2.6*</td>
+            <td>12.9*</td>
+            <td>4.8*</td>
+            <td>37.1*</td>
+            <td>15.7*</td>
+            <td>33.2*</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
+            <td>-</td>
+            <td>4.5*</td>
+            <td>5.9*</td>
+            <td>14.3*</td>
+            <td>2.9*</td>
+            <td>10.6*</td>
+            <td><strong>3.0*</strong></td>
+            <td><u>47.3*</u></td>
+            <td>22.6*</td>
+            <td>48.4*</td>
+        </tr>
+        <tr>
+            <td colspan="11" align="left"><strong>Open-Source</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Qwen2-Audio-7B</td>
+            <td>8B</td>
+            <td>-</td>
+            <td>7.5</td>
+            <td>-</td>
+            <td><strong>1.6</strong></td>
+            <td>-</td>
+            <td>-</td>
+            <td>45.2</td>
+            <td><u>24.4</u></td>
+            <td><strong>55.3</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Qwen2-Audio-7B-Instruct</td>
+            <td>8B</td>
+            <td>2.6*</td>
+            <td>6.9*</td>
+            <td><u>10.3*</u></td>
+            <td>3.1*</td>
+            <td><u>9.7</u>*</td>
+            <td>5.9*</td>
+            <td>39.5*</td>
+            <td>22.9*</td>
+            <td>17.4*</td>
+        </tr>
+          <tr>
+            <td nowrap="nowrap" align="left">VITA-1.5</td>
+            <td>8B</td>
+            <td>2.16</td>
+            <td>-</td>
+            <td>8.4</td>
+            <td>3.4</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GLM-4-Voice-Base</td>
+            <td>9B</td>
+            <td><u>2.5</u></td>
+            <td>-</td>
+            <td>-</td>
+            <td>2.8</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>8B</td>
+            <td><strong>1.6</strong></td>
+            <td><strong>4.4</strong></td>
+            <td><strong>6.9</strong></td>
+            <td><u>1.7</u></td>
+            <td><strong>8.7</strong></td>
+            <td><strong>3.0</strong></td>
+            <td><strong>48.2</strong></td>
+            <td><strong>27.2</strong></td>
+            <td><u>52.4</u></td>
+        </tr>
+    </tbody>
+</table>
+</div>
+* We evaluate officially released checkpoints by ourselves.<br><br>
+
+**Speech Generation**
+
+<div align="center">
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Task</th>
+            <th>Size</th>
+            <th colspan="9">SpeechQA</th>
+        </tr>
+        <tr>
+            <th align="left">Metric</th>
+            <th></th>
+            <th colspan="3">ACC↑</th>
+            <th>G-Eval (10 point)↑</th>
+            <th>Semantic ELO score↑</th>
+            <th>Acoustic ELO score↑</th>
+            <th>Overall ELO score↑</th>
+            <th>UTMOS↑</th>
+            <th>ASR-WER↓</th>
+        </tr>
+        <tr>
+            <th align="left">Dataset</th>
+            <th></th>
+            <th>Speech Llama Q.</th>
+            <th>Speech Web Q.</th>
+            <th>Speech Trivia QA</th>
+            <th>Speech AlpacaEval</th>
+            <th colspan="5">AudioArena</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td colspan="11" align="left"><strong>Proprietary</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-Realtime</td>
+            <td></td>
+            <td><strong>71.7</strong></td>
+            <td><strong>51.6</strong></td>
+            <td><strong>69.7</strong></td>
+            <td><strong>7.4</strong></td>
+            <td><strong>1157</strong></td>
+            <td><strong>1203</strong></td>
+            <td><strong>1200</strong></td>
+            <td><strong>4.2</strong></td>
+            <td><strong>2.3</strong></td>
+        </tr>
+        <tr>
+            <td colspan="11" align="left"><strong>Open-Source</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GLM-4-Voice</td>
+            <td>9B</td>
+            <td>50.0</td>
+            <td>32.0</td>
+            <td>36.4</td>
+            <td><u>5.1</u></td>
+            <td>999</td>
+            <td>1147</td>
+            <td>1035</td>
+            <td><u>4.1</u></td>
+            <td><u>11.7</u></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Llama-Omni</td>
+            <td>8B</td>
+            <td>45.3</td>
+            <td>22.9</td>
+            <td>10.7</td>
+            <td>3.9</td>
+            <td>960</td>
+            <td>878</td>
+            <td>897</td>
+            <td>3.2</td>
+            <td>24.3</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">VITA-1.5</td>
+            <td>8B</td>
+            <td>46.7</td>
+            <td>28.1</td>
+            <td>23.3</td>
+            <td>2.0</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+            <td>-</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Moshi</td>
+            <td>7B</td>
+            <td>43.7</td>
+            <td>23.8</td>
+            <td>16.7</td>
+            <td>2.4</td>
+            <td>871</td>
+            <td>808</td>
+            <td>875</td>
+            <td>2.8</td>
+            <td>8.2</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Mini-Omni</td>
+            <td>1B</td>
+            <td>22.0</td>
+            <td>12.8</td>
+            <td>6.9</td>
+            <td>2.5</td>
+            <td>926</td>
+            <td>803</td>
+            <td>865</td>
+            <td>3.4</td>
+            <td>10.0</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>8B</td>
+            <td><u>61.0</u></td>
+            <td><u>40.0</u></td>
+            <td><u>40.2</u></td>
+            <td><u>5.1</u></td>
+            <td><u>1088</u></td>
+            <td><u>1163</u></td>
+            <td><u>1131</u></td>
+            <td><strong>4.2</strong></td>
+            <td>9.8</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+All results are from AudioEvals, and the evaluation methods along with further details can be found in <a href="https://github.com/OpenBMB/UltraEval-Audio" target="_blank">AudioEvals</a>.<br><br>
+
+**End-to-end Voice Cloning**
+
+<div align="center">
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Task</th>
+            <th colspan="2">Voice cloning</th>
+        </tr>
+        <tr>
+            <th align="left">Metric</th>
+            <th>SIMO↑</th>
+            <th>SIMO↑</th>
+        </tr>
+        <tr>
+            <th align="left">Dataset</th>
+            <th>Seed-TTS test-zh</th>
+            <th>Seed-TTS test-en</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td nowrap="nowrap" align="left">F5-TTS</td>
+            <td><strong>76</strong></td>
+            <td><strong>67</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">CosyVoice</td>
+            <td><u>75</u></td>
+            <td><u>64</u></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">FireRedTTS</td>
+            <td>63</td>
+            <td>46</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>57</td>
+            <td>47</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+
+</details>
+
+<details>
+<summary>Click to view multimodal live streaming results.</summary>
+  
+**Multimodal Live Streaming**: results on StreamingBench
+
+<table style="margin: 0px auto;">
+    <thead>
+        <tr>
+            <th align="left">Model</th>
+            <th>Size</th>
+            <th>Real-Time Video Understanding</th>
+            <th>Omni-Source Understanding</th>
+            <th>Contextual Understanding</th>
+            <th>Overall</th>
+        </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td colspan="7" align="left"><strong>Proprietary</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Gemini 1.5 Pro</td>
+            <td>-</td>
+            <td><u>77.4</u></td>
+            <td><strong>67.8</strong></td>
+            <td><strong>51.1</strong></td>
+            <td><strong>70.3</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">GPT-4o-202408</td>
+            <td>-</td>
+            <td>74.5</td>
+            <td>51.0</td>
+            <td><u>48.0</u></td>
+            <td>64.1</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Claude-3.5-Sonnet</td>
+            <td>-</td>
+            <td>74.0</td>
+            <td>41.4</td>
+            <td>37.8</td>
+            <td>59.7</td>
+        </tr>
+        <tr>
+            <td colspan="9" align="left"><strong>Open-source</strong></td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">VILA-1.5</td>
+            <td>8B</td>
+            <td>61.5</td>
+            <td>37.5</td>
+            <td>26.7</td>
+            <td>49.5</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LongVA</td>
+            <td>7B</td>
+            <td>63.1</td>
+            <td>35.9</td>
+            <td>30.2</td>
+            <td>50.7</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LLaVA-Next-Video-34B</td>
+            <td>34B</td>
+            <td>69.8</td>
+            <td>41.7</td>
+            <td>34.3</td>
+            <td>56.7</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">Qwen2-VL-7B</td>
+            <td>8B</td>
+            <td>71.2</td>
+            <td>40.7</td>
+            <td>33.1</td>
+            <td>57.0</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">InternVL2-8B</td>
+            <td>8B</td>
+            <td>70.1</td>
+            <td>42.7</td>
+            <td>34.1</td>
+            <td>57.0</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">VITA-1.5</td>
+            <td>8B</td>
+            <td>70.9</td>
+            <td>40.8</td>
+            <td>35.8</td>
+            <td>57.4</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">LLaVA-OneVision-7B</td>
+            <td>8B</td>
+            <td>74.3</td>
+            <td>40.8</td>
+            <td>31.0</td>
+            <td>58.4</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">InternLM-XC2.5-OL-7B</td>
+            <td>8B</td>
+            <td>75.4</td>
+            <td>46.2</td>
+            <td>33.6</td>
+            <td>60.8</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-V 2.6</td>
+            <td>8B</td>
+            <td>72.4</td>
+            <td>40.2</td>
+            <td>33.4</td>
+            <td>57.7</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-o 2.6</td>
+            <td>8B</td>
+            <td><strong>79.9</strong></td>
+            <td><u>53.4</u></td>
+            <td>38.5</td>
+            <td><u>66.0</u></td>
+        </tr>
+    </tbody>
+</table>
+
+</details>
+
+
+### Examples <!-- omit in toc -->
+
+We deploy MiniCPM-o 2.6 on end devices. The demo video is the raw-speed recording on an iPad Pro and a Web demo.
+
+<div align="center">
+  <a href="https://www.youtube.com/watch?v=vRIMbxJzStY&t=2s"><img src="./assets/minicpmo2_6/2dot6_o_demo_video_img.png", width=70%></a>
+</div>
+
+<br>
+
+<div style="display: flex; flex-direction: column; align-items: center;">
+  <img src="assets/minicpmo2_6/minicpmo2_6_math_intersect.png" alt="math" style="margin-bottom: 5px;">
+  <img src="assets/minicpmo2_6/minicpmo2_6_diagram_train_NN.png" alt="diagram" style="margin-bottom: 5px;">
+  <img src="assets/minicpmo2_6/minicpmo2_6_multi-image_bike.png" alt="bike" style="margin-bottom: 5px;">
+</div>
+
+
+## Legacy Models <!-- omit in toc --> 
+
+| Model                | Introduction and Guidance       |
+|:-----------|:-------------------:|
+| MiniCPM-V 4.0  | [Document](./docs/minicpm_v4_en.md)   | 
+| MiniCPM-V 2.6  | [Document](./docs/minicpm_v2dot6_en.md)   | 
+| MiniCPM-Llama3-V 2.5  | [Document](./docs/minicpm_llama3_v2dot5.md)   | 
+| MiniCPM-V 2.0  | [Document](./docs/minicpm_v2.md)   | 
+| MiniCPM-V 1.0  | [Document](./docs/minicpm_v1.md)   | 
+| OmniLMM-12B  | [Document](././docs/omnilmm_en.md)   |  
+
+
+## MiniCPM-V & o Cookbook
+
+Discover comprehensive, ready-to-deploy solutions for the MiniCPM-V and MiniCPM-o model series in our structured [cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook), which empowers developers to rapidly implement multimodal AI applications with integrated vision, speech, and live-streaming capabilities. Key features include:
+
+**Easy Usage Documentation**
+
+Our comprehensive [documentation website](https://minicpm-o.readthedocs.io/en/latest/index.html) presents every recipe in a clear, well-organized manner.
+All features are displayed at a glance, making it easy for you to quickly find exactly what you need.
+
+**Broad User Spectrum**
+
+We support a wide range of users, from individuals to enterprises and researchers.
+
+*   **Individuals**: Enjoy effortless inference using [Ollama](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-v4_ollama.md) and [Llama.cpp](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-v4_llamacpp.md) with minimal setup.
+*   **Enterprises**: Achieve high-throughput, scalable performance with [vLLM](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-v4_vllm.md) and [SGLang](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-v4_sglang.md).
+*   **Researchers**: Leverage advanced frameworks including [Transformers](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_full.md), [LLaMA-Factory](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_llamafactory.md), [SWIFT](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/swift.md), and [Align-anything](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/align_anything.md) to enable flexible model development and cutting-edge experimentation.
+
+**Versatile Deployment Scenarios**
+
+Our ecosystem delivers optimal solution for a variety of hardware environments and deployment demands.
+
+*   **Web demo**: Launch interactive multimodal AI web demo with [FastAPI](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/README.md).
+*   **Quantized deployment**: Maximize efficiency and minimize resource consumption using [GGUF](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-v4_gguf_quantize.md) and [BNB](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/bnb/minicpm-v4_bnb_quantize.md).
+*   **End devices**: Bring powerful AI experiences to [iPhone and iPad](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/ios_demo/ios.md), supporting offline and privacy-sensitive applications.
+
+
+## Chat with Our Demo on Gradio 🤗
+
+We provide online and local demos powered by Hugging Face Gradio <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>, the most popular model deployment framework nowadays. It supports streaming outputs, progress bars, queuing, alerts, and other useful features.
+
+
+### Online Demo <!-- omit in toc --> 
+
+Click here to try out the online demo of [MiniCPM-o 2.6](https://minicpm-omni-webdemo-us.modelbest.cn/) | [MiniCPM-V 2.6](http://120.92.209.146:8887/) | [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | [MiniCPM-V 2.0](https://huggingface.co/spaces/openbmb/MiniCPM-V-2).
+
+### Local WebUI Demo <!-- omit in toc --> 
+  
+You can easily build your own local WebUI demo using the following commands.
+
+Please ensure that `transformers==4.44.2` is installed, as other versions may have compatibility issues.
+
+If you are using an older version of PyTorch, you might encounter this issue `"weight_norm_fwd_first_dim_kernel" not implemented for 'BFloat16'`, Please add `self.minicpmo_model.tts.float()` during the model initialization.
+
+**For real-time voice/video call demo:**
+1. launch model server:
+```shell
+pip install -r requirements_o2.6.txt
+
+python web_demos/minicpm-o_2.6/model_server.py
+```
+
+2. launch web server:
+
+```shell
+# Make sure Node and PNPM is installed.
+sudo apt-get update
+sudo apt-get install nodejs npm
+npm install -g pnpm
+
+
+cd web_demos/minicpm-o_2.6/web_server
+# create ssl cert for https, https is required to request camera and microphone permissions.
+bash ./make_ssl_cert.sh  # output key.pem and cert.pem
+
+pnpm install  # install requirements
+pnpm run dev  # start server
+```
+Open `https://localhost:8088/` in browser and enjoy the real-time voice/video call.
+
+**For chatbot demo:**
+```shell
+pip install -r requirements_o2.6.txt
+
+python web_demos/minicpm-o_2.6/chatbot_web_demo_o2.6.py
+```
+Open `http://localhost:8000/` in browser and enjoy the vision mode chatbot.
+
+## Inference
+
+
+### Model Zoo
+
+| Model           | Device | Memory    | &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp; Description       | Download |
+|:-----------|:--:|:-----------:|:-------------------|:---------------:|
+| MiniCPM-V 4.5| GPU | 18 GB  | The latest version, strong end-side multimodal performance for single image, multi-image and video understanding.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-V-4_5) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-4_5) |
+| MiniCPM-V 4.5 gguf | CPU | 8 GB  | The gguf version, lower memory usage and faster inference.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-4_5-gguf) |
+| MiniCPM-V 4.5 int4 | GPU | 9 GB  | The int4 quantized version, lower GPU memory usage.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-V-4_5-int4) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-4_5-int4) |
+| MiniCPM-V 4.5 AWQ | GPU | 9 GB  | The int4 quantized version, lower GPU memory usage.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-V-4_5-AWQ) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-V-4_5-AWQ) |
+| MiniCPM-o 2.6| GPU | 18 GB  | The latest version, achieving GPT-4o level performance for vision, speech and multimodal live streaming on end-side devices.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-o-2_6) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-o-2_6) |
+| MiniCPM-o 2.6 gguf | CPU | 8 GB  | The gguf version, lower memory usage and faster inference.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-o-2_6-gguf) |
+| MiniCPM-o 2.6 int4 | GPU | 9 GB  | The int4 quantized version, lower GPU memory usage.   |  [🤗](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) &nbsp;&nbsp; [<img src="./assets/modelscope_logo.png" width="20px"></img>](https://modelscope.cn/models/OpenBMB/MiniCPM-o-2_6-int4) |
+
+### Multi-turn Conversation
+
+If you wish to enable long-thinking mode, provide the argument `enable_thinking=True` to the chat function.
+
+```shell
+pip install -r requirements_o2.6.txt
+```
+
+Please refer to the following codes to run.
+
+<div align="center">
+<img src="assets/minicpmo2_6/show_demo.jpg" width="500px">
+</div>
+
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+torch.manual_seed(100)
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
+
+image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')
+
+enable_thinking=False # If `enable_thinking=True`, the long-thinking mode is enabled.
+
+# First round chat 
+question = "What is the landform in the picture?"
+msgs = [{'role': 'user', 'content': [image, question]}]
+
+answer = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    enable_thinking=enable_thinking
+)
+print(answer)
+
+# Second round chat, pass history context of multi-turn conversation
+msgs.append({"role": "assistant", "content": [answer]})
+msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
+
+answer = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer
+)
+print(answer)
+```
+
+You will get the following output:
+
+```shell
+# round1
+The landform in the picture is karst topography. Karst landscapes are characterized by distinctive, jagged limestone hills or mountains with steep, irregular peaks and deep valleys—exactly what you see here These unique formations result from the dissolution of soluble rocks like limestone over millions of years through water erosion.
+
+This scene closely resembles the famous karst landscape of Guilin and Yangshuo in China’s Guangxi Province. The area features dramatic, pointed limestone peaks rising dramatically above serene rivers and lush green forests, creating a breathtaking and iconic natural beauty that attracts millions of visitors each year for its picturesque views.
+
+# round2
+When traveling to a karst landscape like this, here are some important tips:
+
+1. Wear comfortable shoes: The terrain can be uneven and hilly.
+2. Bring water and snacks for energy during hikes or boat rides.
+3. Protect yourself from the sun with sunscreen, hats, and sunglasses—especially since you’ll likely spend time outdoors exploring scenic spots.
+4. Respect local customs and nature regulations by not littering or disturbing wildlife.
+
+By following these guidelines, you'll have a safe and enjoyable trip while appreciating the stunning natural beauty of places such as Guilin’s karst mountains.
+```
+
+#### Chat with Multiple Images
+<details>
+<summary> Click to view Python code running MiniCPM-V-4_5 with multiple images input. </summary>
+  
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,  # or openbmb/MiniCPM-o-2_6
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)  # or openbmb/MiniCPM-o-2_6
+
+image1 = Image.open('image1.jpg').convert('RGB')
+image2 = Image.open('image2.jpg').convert('RGB')
+question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
+
+msgs = [{'role': 'user', 'content': [image1, image2, question]}]
+
+answer = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer
+)
+print(answer)
+```
+</details>
+
+#### In-context Few-shot Learning
+<details>
+<summary> Click to view Python code running MiniCPM-V-4_5 with few-shot input. </summary>
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,  # or openbmb/MiniCPM-o-2_6
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)  # or openbmb/MiniCPM-o-2_6
+
+question = "production date" 
+image1 = Image.open('example1.jpg').convert('RGB')
+answer1 = "2023.08.04"
+image2 = Image.open('example2.jpg').convert('RGB')
+answer2 = "2007.04.24"
+image_test = Image.open('test.jpg').convert('RGB')
+
+msgs = [
+    {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
+    {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
+    {'role': 'user', 'content': [image_test, question]}
+]
+
+answer = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer
+)
+print(answer)
+```
+</details>
+
+#### Chat with Video
+<details>
+<summary> Click to view Python code running MiniCPM-V-4_5 by with video input and 3D-Resampler. </summary>
+
+```python
+## The 3d-resampler compresses multiple frames into 64 tokens by introducing temporal_ids. 
+# To achieve this, you need to organize your video data into two corresponding sequences: 
+#   frames: List[Image]
+#   temporal_ids: List[List[Int]].
+
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+from decord import VideoReader, cpu    # pip install decord
+from scipy.spatial import cKDTree
+import numpy as np
+import math
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,  # or openbmb/MiniCPM-o-2_6
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)  # or openbmb/MiniCPM-o-2_6
+
+MAX_NUM_FRAMES=180 # Indicates the maximum number of frames received after the videos are packed. The actual maximum number of valid frames is MAX_NUM_FRAMES * MAX_NUM_PACKING.
+MAX_NUM_PACKING=3  # indicates the maximum packing number of video frames. valid range: 1-6
+TIME_SCALE = 0.1 
+
+def map_to_nearest_scale(values, scale):
+    tree = cKDTree(np.asarray(scale)[:, None])
+    _, indices = tree.query(np.asarray(values)[:, None])
+    return np.asarray(scale)[indices]
+
+
+def group_array(arr, size):
+    return [arr[i:i+size] for i in range(0, len(arr), size)]
+
+def encode_video(video_path, choose_fps=3, force_packing=None):
+    def uniform_sample(l, n):
+        gap = len(l) / n
+        idxs = [int(i * gap + gap / 2) for i in range(n)]
+        return [l[i] for i in idxs]
+    vr = VideoReader(video_path, ctx=cpu(0))
+    fps = vr.get_avg_fps()
+    video_duration = len(vr) / fps
+        
+    if choose_fps * int(video_duration) <= MAX_NUM_FRAMES:
+        packing_nums = 1
+        choose_frames = round(min(choose_fps, round(fps)) * min(MAX_NUM_FRAMES, video_duration))
+        
+    else:
+        packing_nums = math.ceil(video_duration * choose_fps / MAX_NUM_FRAMES)
+        if packing_nums <= MAX_NUM_PACKING:
+            choose_frames = round(video_duration * choose_fps)
+        else:
+            choose_frames = round(MAX_NUM_FRAMES * MAX_NUM_PACKING)
+            packing_nums = MAX_NUM_PACKING
 
-question = "production date" 
-image1 = Image.open('example1.jpg').convert('RGB')
-answer1 = "2023.08.04"
-image2 = Image.open('example2.jpg').convert('RGB')
-answer2 = "2007.04.24"
-image_test = Image.open('test.jpg').convert('RGB')
+    frame_idx = [i for i in range(0, len(vr))]      
+    frame_idx =  np.array(uniform_sample(frame_idx, choose_frames))
+
+    if force_packing:
+        packing_nums = min(force_packing, MAX_NUM_PACKING)
+    
+    print(video_path, ' duration:', video_duration)
+    print(f'get video frames={len(frame_idx)}, packing_nums={packing_nums}')
+    
+    frames = vr.get_batch(frame_idx).asnumpy()
+
+    frame_idx_ts = frame_idx / fps
+    scale = np.arange(0, video_duration, TIME_SCALE)
+
+    frame_ts_id = map_to_nearest_scale(frame_idx_ts, scale) / TIME_SCALE
+    frame_ts_id = frame_ts_id.astype(np.int32)
+
+    assert len(frames) == len(frame_ts_id)
+
+    frames = [Image.fromarray(v.astype('uint8')).convert('RGB') for v in frames]
+    frame_ts_id_group = group_array(frame_ts_id, packing_nums)
+    
+    return frames, frame_ts_id_group
+
+
+video_path="video_test.mp4"
+fps = 5 # fps for video
+force_packing = None # You can set force_packing to ensure that 3D packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
+frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
 
+question = "Describe the video"
 msgs = [
-    {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
-    {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
-    {'role': 'user', 'content': [image_test, question]}
+    {'role': 'user', 'content': frames + [question]}, 
 ]
 
+
 answer = model.chat(
     msgs=msgs,
+    tokenizer=tokenizer,
+    use_image_id=False,
+    max_slice_nums=1,
+    temporal_ids=frame_ts_id_group
+)
+print(answer)
+```
+</details>
+
+
+#### Speech and Audio Mode
+
+Model initialization
+
+```python
+import torch
+import librosa
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-o-2_6', trust_remote_code=True,
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-o-2_6', trust_remote_code=True)
+
+model.init_tts()
+model.tts.float()
+```
+
+<hr/>
+
+##### Mimick <!-- omit in toc --> 
+
+`Mimick` task reflects a model's end-to-end speech modeling capability. The model takes audio input, and outputs an ASR transcription and subsequently reconstructs the original audio with high similarity. The higher the similarity between the reconstructed audio and the original audio, the stronger the model's foundational capability in end-to-end speech modeling.
+
+```python
+mimick_prompt = "Please repeat each user's speech, including voice style and speech content."
+audio_input, _ = librosa.load('./assets/input_examples/Trump_WEF_2018_10s.mp3', sr=16000, mono=True) # load the audio to be mimicked
+
+# `./assets/input_examples/fast-pace.wav`, 
+# `./assets/input_examples/chi-english-1.wav` 
+# `./assets/input_examples/exciting-emotion.wav` 
+# for different aspects of speech-centric features.
+
+msgs = [{'role': 'user', 'content': [mimick_prompt, audio_input]}]
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    temperature=0.3,
+    generate_audio=True,
+    output_audio_path='output_mimick.wav', # save the tts result to output_audio_path
+)
+```
+
+<hr/>
+
+##### General Speech Conversation with Configurable Voices <!-- omit in toc --> 
+
+A general usage scenario of `MiniCPM-o-2.6` is role-playing a specific character based on the audio prompt. It will mimic the voice of the character to some extent and act like the character in text, including language style. In this mode, `MiniCPM-o-2.6` sounds **more natural and human-like**. Self-defined audio prompts can be used to customize the voice of the character in an end-to-end manner.
+
+
+```python
+ref_audio, _ = librosa.load('./assets/input_examples/icl_20.wav', sr=16000, mono=True) # load the reference audio
+sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='audio_roleplay', language='en')
+
+# round one
+user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]}
+msgs = [sys_prompt, user_question]
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_roleplay_round_1.wav',
+)
+
+# round two
+history = msgs.append({'role': 'assistant', 'content': res})
+user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]}
+msgs = history.append(user_question)
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_roleplay_round_2.wav',
+)
+print(res)
+```
+
+<hr/>
+
+##### Speech Conversation as an AI Assistant <!-- omit in toc --> 
+
+An enhanced feature of `MiniCPM-o-2.6` is to act as an AI assistant, but only with limited choice of voices. In this mode, `MiniCPM-o-2.6` is **less human-like and more like a voice assistant**. In this mode, the model is more instruction-following. For demo, you are suggested to use `assistant_female_voice`, `assistant_male_voice`, and `assistant_default_female_voice`. Other voices may work but not as stable as the default voices.
+
+*Please note that, `assistant_female_voice` and `assistant_male_voice` are more stable but sounds like robots, while `assistant_default_female_voice` is more human-alike but not stable, its voice often changes in multiple turns. We suggest you to try stable voices `assistant_female_voice` and `assistant_male_voice`.*
+
+```python
+ref_audio, _ = librosa.load('./assets/input_examples/assistant_female_voice.wav', sr=16000, mono=True) # or use `./assets/input_examples/assistant_male_voice.wav`
+sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='audio_assistant', language='en') 
+user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]} # load the user's audio question
+
+# round one
+msgs = [sys_prompt, user_question]
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_assistant_round_1.wav',
+)
+
+# round two
+history = msgs.append({'role': 'assistant', 'content': res})
+user_question = {'role': 'user', 'content': [librosa.load('xxx.wav', sr=16000, mono=True)[0]]}
+msgs = history.append(user_question)
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_assistant_round_2.wav',
+)
+print(res)
+```
+
+<hr/>
+
+##### Instruction-to-Speech <!-- omit in toc --> 
+
+`MiniCPM-o-2.6` can also do Instruction-to-Speech, aka **Voice Creation**. You can describe a voice in detail, and the model will generate a voice that matches the description. For more Instruction-to-Speech sample instructions, you can refer to https://voxinstruct.github.io/VoxInstruct/.
+
+```python
+instruction = 'Speak like a male charming superstar, radiating confidence and style in every word.'
+
+msgs = [{'role': 'user', 'content': [instruction]}]
+
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_voice_creation.wav',
+)
+```
+
+<hr/>
+
+##### Voice Cloning <!-- omit in toc --> 
+
+`MiniCPM-o-2.6` can also do zero-shot text-to-speech, aka **Voice Cloning**. With this mode, model will act like a TTS model.
+
+
+```python
+ref_audio, _ = librosa.load('./assets/input_examples/icl_20.wav', sr=16000, mono=True) # load the reference audio
+sys_prompt = model.get_sys_prompt(ref_audio=ref_audio, mode='voice_cloning', language='en')
+text_prompt = f"Please read the text below."
+user_question = {'role': 'user', 'content': [text_prompt, "content that you want to read"]}
+
+msgs = [sys_prompt, user_question]
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_voice_cloning.wav',
+)
+
+```
+
+<hr/>
+
+##### Addressing Various Audio Understanding Tasks <!-- omit in toc --> 
+
+`MiniCPM-o-2.6` can also be used to address various audio understanding tasks, such as ASR, speaker analysis, general audio captioning, and sound scene tagging.
+
+For audio-to-text tasks, you can use the following prompts:
+
+- ASR with ZH(same as AST en2zh): `请仔细听这段音频片段，并将其内容逐字记录。`
+- ASR with EN(same as AST zh2en): `Please listen to the audio snippet carefully and transcribe the content.`
+- Speaker Analysis: `Based on the speaker's content, speculate on their gender, condition, age range, and health status.`
+- General Audio Caption: `Summarize the main content of the audio.`
+- General Sound Scene Tagging: `Utilize one keyword to convey the audio's content or the associated scene.`
+
+```python
+task_prompt = "Please listen to the audio snippet carefully and transcribe the content." + "
+" # can change to other prompts.
+audio_input, _ = librosa.load('./assets/input_examples/audio_understanding.mp3', sr=16000, mono=True) # load the audio to be captioned
+
+msgs = [{'role': 'user', 'content': [task_prompt, audio_input]}]
+
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    max_new_tokens=128,
+    use_tts_template=True,
+    generate_audio=True,
+    temperature=0.3,
+    output_audio_path='result_audio_understanding.wav',
+)
+print(res)
+```
+
+
+
+
+#### Multimodal Live Streaming
+<details>
+<summary> Click to view Python code running MiniCPM-o 2.6 with chat inference. </summary>
+
+```python
+import math
+import numpy as np
+from PIL import Image
+from moviepy.editor import VideoFileClip
+import tempfile
+import librosa
+import soundfile as sf
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+def get_video_chunk_content(video_path, flatten=True):
+    video = VideoFileClip(video_path)
+    print('video_duration:', video.duration)
+    
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_audio_file:
+        temp_audio_file_path = temp_audio_file.name
+        video.audio.write_audiofile(temp_audio_file_path, codec="pcm_s16le", fps=16000)
+        audio_np, sr = librosa.load(temp_audio_file_path, sr=16000, mono=True)
+    num_units = math.ceil(video.duration)
+    
+    # 1 frame + 1s audio chunk
+    contents= []
+    for i in range(num_units):
+        frame = video.get_frame(i+1)
+        image = Image.fromarray((frame).astype(np.uint8))
+        audio = audio_np[sr*i:sr*(i+1)]
+        if flatten:
+            contents.extend(["<unit>", image, audio])
+        else:
+            contents.append(["<unit>", image, audio])
+    
+    return contents
+
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-o-2_6', trust_remote_code=True,
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-o-2_6', trust_remote_code=True)
+
+model.init_tts()
+
+# If you are using an older version of PyTorch, you might encounter this issue "weight_norm_fwd_first_dim_kernel" not implemented for 'BFloat16', Please convert the TTS to float32 type.
+# model.tts.float()
+
+# https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/assets/Skiing.mp4
+video_path="assets/Skiing.mp4"
+sys_msg = model.get_sys_prompt(mode='omni', language='en')
+# if use voice clone prompt, please set ref_audio
+# ref_audio_path = '/path/to/ref_audio'
+# ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True)
+# sys_msg = model.get_sys_prompt(ref_audio=ref_audio, mode='omni', language='en')
+
+contents = get_video_chunk_content(video_path)
+msg = {"role":"user", "content": contents}
+msgs = [sys_msg, msg]
+
+# please set generate_audio=True and output_audio_path to save the tts result
+generate_audio = True
+output_audio_path = 'output.wav'
+
+res = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    temperature=0.5,
+    max_new_tokens=4096,
+    omni_input=True, # please set omni_input=True when omni inference
+    use_tts_template=True,
+    generate_audio=generate_audio,
+    output_audio_path=output_audio_path,
+    max_slice_nums=1,
+    use_image_id=False,
+    return_dict=True
+)
+print(res)
+```
+</details>
+
+<details>
+<summary> Click to view Python code running MiniCPM-o 2.6 with streaming inference. </summary>
+
+Note: The streaming inference has a slight performance degradation because the audio encoding is not global.
+```python
+# a new conversation need reset session first, it will reset the kv-cache
+model.reset_session()
+
+contents = get_video_chunk_content(video_path, flatten=False)
+session_id = '123'
+generate_audio = True
+
+# 1. prefill system prompt
+res = model.streaming_prefill(
+    session_id=session_id,
+    msgs=[sys_msg], 
     tokenizer=tokenizer
 )
+
+# 2. prefill video/audio chunks
+for content in contents:
+    msgs = [{"role":"user", "content": content}]
+    res = model.streaming_prefill(
+        session_id=session_id,
+        msgs=msgs, 
+        tokenizer=tokenizer
+    )
+
+# 3. generate
+res = model.streaming_generate(
+    session_id=session_id,
+    tokenizer=tokenizer,
+    temperature=0.5,
+    generate_audio=generate_audio
+)
+
+audios = []
+text = ""
+
+if generate_audio:
+    for r in res:
+        audio_wav = r.audio_wav
+        sampling_rate = r.sampling_rate
+        txt = r.text
+
+        audios.append(audio_wav)
+        text += txt
+        
+    res = np.concatenate(audios)
+    sf.write("output.wav", res, samplerate=sampling_rate)
+    print("text:", text)
+    print("audio saved to output.wav")
+else:
+    for r in res:
+        text += r['text']
+    print("text:", text)
+```
+
+</details>
+
+### Inference on Multiple GPUs
+You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this [tutorial](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md) for detailed instructions on how to load the model and inference using multiple low VRAM GPUs.
+
+
+### Inference on Mac
+<details>
+<summary>Click to view an example, to run MiniCPM-Llama3-V 2.5 on 💻 Mac with MPS (Apple silicon or AMD GPUs). </summary>
+
+```python
+# test.py  Need more than 16GB memory.
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, low_cpu_mem_usage=True)
+model = model.to(device='mps')
+
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True)
+model.eval()
+
+image = Image.open('./assets/hk_OCR.jpg').convert('RGB')
+question = 'Where is this photo taken?'
+msgs = [{'role': 'user', 'content': question}]
+
+answer, context, _ = model.chat(
+    image=image,
+    msgs=msgs,
+    context=None,
+    tokenizer=tokenizer,
+    sampling=True
+)
 print(answer)
 ```
+Run with command:
+```shell
+PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py
+```
 </details>
 
 
-## License
-#### Model License
-* The MiniCPM-o/V model weights and code are open-sourced under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM-V/blob/main/LICENSE) license.
-* To help us better understand and support our users, we would deeply appreciate it if you could consider optionally filling out a brief registration ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g).
+### Efficient Inference with llama.cpp, Ollama, vLLM
+
+See [our fork of llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpmv-main/examples/llava/README-minicpmv2.6.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4).
+
+See [our fork of Ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md) for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4).
+
+
+<details>
+<summary> vLLM now officially supports MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0. And you can use our fork to run MiniCPM-o 2.6 for now. Click to see. </summary>
+
+1. Install vLLM(>=0.7.1):
+```shell
+pip install vllm
+```
+
+2. Run Example:
+* [Vision Language](https://docs.vllm.ai/en/latest/getting_started/examples/vision_language.html) 
+* [Audio Language](https://docs.vllm.ai/en/latest/getting_started/examples/audio_language.html) 
+  </details>
+
+## Fine-tuning
+
+### Simple Fine-tuning <!-- omit in toc -->
+
+We support simple fine-tuning with Hugging Face for MiniCPM-o 2.6, MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0.
+
+[Reference Document](./finetune/readme.md)
+
+
+### With Align-Anything <!-- omit in toc -->
+
+We support fine-tuning MiniCPM-o 2.6 by PKU-Alignment Team (both vision and audio, SFT and DPO) with the [Align-Anything framework](https://github.com/PKU-Alignment/align-anything). Align-Anything is a scalable framework that aims to align any-modality large models with human intentions, open-sourcing the [datasets, models and benchmarks](https://huggingface.co/datasets/PKU-Alignment/align-anything). Benefiting from its concise and modular design, it supports 30+ open-source benchmarks, 40+ models and algorithms including SFT, SimPO, RLHF, *etc*. It also provides 30+ directly runnable scripts, making it suitable for beginners to quickly get started.
+
+Best Practices: [MiniCPM-o 2.6](https://github.com/PKU-Alignment/align-anything/tree/main/scripts).
+
+
+### With LLaMA-Factory <!-- omit in toc -->
+
+We support fine-tuning MiniCPM-o 2.6 and MiniCPM-V 2.6 with the LLaMA-Factory framework. LLaMA-Factory provides a solution for flexibly customizing the fine-tuning (Lora/Full/Qlora) of 200+ LLMs without the need for coding through the built-in web UI LLaMABoard. It supports various training methods like sft/ppo/dpo/kto and advanced algorithms like Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA.
+
+
+Best Practices: [MiniCPM-o 2.6 | MiniCPM-V 2.6](./docs/llamafactory_train_and_infer.md). 
+
+
+### With the SWIFT Framework <!-- omit in toc -->
+
+We now support MiniCPM-V series fine-tuning with the SWIFT framework. SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs . It supports the lightweight training solutions provided by PEFT and a complete Adapters Library including techniques such as NEFTune, LoRA+ and LLaMA-PRO.
+
+Best Practices：[MiniCPM-V 1.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md), [MiniCPM-V 2.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md), [MiniCPM-V 2.6](https://github.com/modelscope/ms-swift/issues/1613).
+
+
+## Awesome work using MiniCPM-V & MiniCPM-o
+- [text-extract-api](https://github.com/CatchTheTornado/text-extract-api): Document extraction API using OCRs and Ollama supported models ![GitHub Repo stars](https://img.shields.io/github/stars/CatchTheTornado/text-extract-api)
+- [comfyui_LLM_party](https://github.com/heshengtao/comfyui_LLM_party): Build LLM workflows and integrate into existing image workflows ![GitHub Repo stars](https://img.shields.io/github/stars/heshengtao/comfyui_LLM_party)
+- [Ollama-OCR](https://github.com/imanoop7/Ollama-OCR): OCR package uses vlms through Ollama to extract text from images and PDF ![GitHub Repo stars](https://img.shields.io/github/stars/imanoop7/Ollama-OCR)
+- [comfyui-mixlab-nodes](https://github.com/MixLabPro/comfyui-mixlab-nodes): ComfyUI node suite supports Workflow-to-APP、GPT&3D and more ![GitHub Repo stars](https://img.shields.io/github/stars/MixLabPro/comfyui-mixlab-nodes)
+- [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat): Interactive digital human conversation implementation on single PC ![GitHub Repo stars](https://img.shields.io/github/stars/HumanAIGC-Engineering/OpenAvatarChat)
+- [pensieve](https://github.com/arkohut/pensieve): A privacy-focused passive recording project by recording screen content ![GitHub Repo stars](https://img.shields.io/github/stars/arkohut/pensieve)
+- [paperless-gpt](https://github.com/icereed/paperless-gpt): Use LLMs to handle paperless-ngx, AI-powered titles, tags and OCR ![GitHub Repo stars](https://img.shields.io/github/stars/icereed/paperless-gpt)
+- [Neuro](https://github.com/kimjammer/Neuro): A recreation of Neuro-Sama, but running on local models on consumer hardware ![GitHub Repo stars](https://img.shields.io/github/stars/kimjammer/Neuro)
+
+## FAQs
+Click here to view the [FAQs](./docs/faqs.md)
+
+## Limitations
+As an experimental trial, we find MiniCPM-o 2.6 has notable limitations worth further investigation and improvement.
+- **Unstable speech output.** The speech generation can be flawed with noisy backgrounds and unmeaningful sounds.
+- **Repeated response.** The model tends to repeat its response when encountering similar consecutive user queries.
+- **High-latency on Web Demo.** Users may experience unusual high-latency when using web demo hosted on overseas servers. We recommend deploying the demo locally or with good network connections.
+
+## Model License <!-- omit in toc -->
+
+*   The MiniCPM-o/V model weights and code are open-sourced under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM-V/blob/main/LICENSE) license.
+
+*   To help us better understand and support our users, we would deeply appreciate it if you could consider optionally filling out a brief registration ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g).
+
+## Statement <!-- omit in toc -->
+
+As MLLMs, MiniCPM-o/V models generate content by learning a large number of multimodal corpora, but they cannot comprehend, express personal opinions, or make value judgements. Anything generated by MiniCPM-o/V models does not represent the views and positions of the model developers
+
+We will not be liable for any problems arising from the use of MiniCPM-o/V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination, or misuse of the model.
+
+
+## Institutions  <!-- omit in toc -->
+
+This project is developed by the following institutions:
+
+- <img src="assets/thunlp.png" width="28px"> [THUNLP](https://nlp.csai.tsinghua.edu.cn/)
+- <img src="assets/modelbest.png" width="28px"> [ModelBest](https://modelbest.cn/)
+
+## 🌟 Star History <!-- omit in toc -->
+
+
+<table align="center">
+    <p align="center">
+      <img src="assets/star-history-25-09-02.png"/>
+    </p>
+</table>
+
+<!-- <picture>
+  <source
+    media="(prefers-color-scheme: dark)"
+    srcset="
+      https://api.star-history.com/svg?repos=OpenBMB/MiniCPM-o&type=Date&theme=dark
+    "
+  />
+  <source
+    media="(prefers-color-scheme: light)"
+    srcset="
+      https://api.star-history.com/svg?repos=OpenBMB/MiniCPM-o&type=Date
+    "
+  />
+  <img
+    alt="Star History Chart"
+    src="https://api.star-history.com/svg?repos=OpenBMB/MiniCPM-o&type=Date"
+  />
+</picture> -->
 
-#### Statement
-* As an LMM, MiniCPM-V 4.5 generates contents by learning a large amount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 4.5 does not represent the views and positions of the model developers
-* We will not be liable for any problems arising from the use of the MinCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
+## Key Techniques and Other Multimodal Projects <!-- omit in toc -->
 
-## Key Techniques and Other Multimodal Projects
+👏 Welcome to explore key techniques of MiniCPM-o/V and other multimodal projects of our team:
 
-👏 Welcome to explore key techniques of MiniCPM-V 4.5 and other multimodal projects of our team:
+[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLPR](https://github.com/OpenBMB/RLPR) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
 
-[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLPR](https://github.com/OpenBMB/RLPR) |  [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD)  | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
 
-## Citation
+## Citation <!-- omit in toc -->
 
-If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
+If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！
 
 ```bib
 @misc{yu2025minicpmv45cookingefficient,