Duplicate from openbmb/MiniCPM-V-4_5

Co-authored-by: Tianchi Cai <tc-mb@users.noreply.huggingface.co>

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README.md

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+---
+pipeline_tag: image-text-to-text
+datasets:
+- openbmb/RLAIF-V-Dataset
+library_name: transformers
+language:
+- multilingual
+tags:
+- minicpm-v
+- vision
+- ocr
+- 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>
+
+[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://211.93.21.133:18120/) </a> 
+
+
+
+## MiniCPM-V 4.5
+
+**MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include:
+
+- 🔥 **State-of-the-art Vision-Language Capability.**
+  MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B** for vision-language capabilities, making it the most performant MLLM under 30B parameters.
+
+- 🎬 **Efficient High-FPS and Long Video Understanding.** Powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). This means that the model can perceive significantly more video frames without increasing the LLM inference cost. This brings state-of-the-art high-FPS (up to 10FPS) video understanding and long video understanding capabilities on Video-MME, LVBench, MLVU, MotionBench, FavorBench, etc., efficiently.
+
+- ⚙️ **Controllable Hybrid Fast/Deep Thinking.** MiniCPM-V 4.5 supports both fast thinking for efficient frequent usage with competitive performance, and deep thinking for more complex problem solving. To cover efficiency and performance trade-offs in different user scenarios, this fast/deep thinking mode can be switched in a highly controlled fashion.
+
+- 💪 **Strong OCR, Document Parsing and Others.**
+Based on [LLaVA-UHD](https://arxiv.org/pdf/2403.11703) architecture, MiniCPM-V 4.5 can process high-resolution images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), using 4x less visual tokens than most MLLMs. The model achieves **leading performance on OCRBench, surpassing proprietary models such as GPT-4o-latest and Gemini 2.5**. It also achieves state-of-the-art performance for PDF document parsing capability on OmniDocBench among general MLLMs. 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-latest on MMHal-Bench, and supports **multilingual capabilities** in more than 30 languages.
+
+- 💫 **Easy Usage.**
+MiniCPM-V 4.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/tc-mb/llama.cpp/blob/Support-MiniCPM-V-4.5/docs/multimodal/minicpmv4.5.md) and [ollama](https://github.com/tc-mb/ollama/tree/MIniCPM-V) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-4_5-int4), [GGUF](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) and [AWQ](https://github.com/tc-mb/AutoAWQ) format quantized models in 16 sizes, (3) [SGLang](https://github.com/tc-mb/sglang/tree/main) and [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [Transformers](https://github.com/tc-mb/transformers/tree/main) and [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), (6) optimized [local iOS app](https://github.com/tc-mb/MiniCPM-o-demo-iOS) on iPhone and iPad, and (7) online web demo on [server](http://101.126.42.235:30910/). See our [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) for full usages!
+
+
+### Key Techniques
+
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpm-v-4dot5-framework.png" , width=100%>
+</div>
+
+- **Architechture: Unified 3D-Resampler for High-density Video Compression.** MiniCPM-V 4.5 introduces a 3D-Resampler that overcomes the performance-efficiency trade-off in video understanding. By grouping and jointly compressing up to 6 consecutive video frames into just 64 tokens (the same token count used for a single image in MiniCPM-V series), MiniCPM-V 4.5 achieves a 96× compression rate for video tokens. This allows the model to process more video frames without additional LLM computational cost, enabling high-FPS video and long video understanding. The architecture supports unified encoding for images, multi-image inputs, and videos, ensuring seamless capability and knowledge transfer.
+
+- **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.
+
+### Evaluation
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/radar_minicpm_v45.png", width=60%>
+</div>
+<div align="center">
+<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv_4_5_evaluation_result.png" , width=100%>
+</div>
+
+### Inference Efficiency 
+
+**OpenCompass**
+<div align="left">
+<table style="margin: 0px auto;">
+    <thead>
+            <tr>
+              <th align="left">Model</th>
+              <th>Size</th>
+              <th>Avg Score ↑</th>
+              <th>Total Inference Time ↓</th>
+            </tr>
+    </thead>
+    <tbody align="center">
+        <tr>
+            <td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
+            <td>10.3B</td>
+            <td>76.6</td>
+            <td>17.5h</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiMo-VL-7B-RL</td>
+            <td>8.3B</td>
+            <td>76.4</td>
+            <td>11h</td>
+        </tr>
+        <tr>
+            <td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
+            <td>8.7B</td>
+            <td><b>77.0</td>
+            <td><b>7.5h</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+
+**Video-MME**
+
+<div align="left">
+<table style="margin: 0px auto;">
+    <thead>
+          <tr>
+              <th align="left">Model</th>
+              <th>Size</th>
+              <th>Avg Score ↑</th>
+              <th>Total Inference Time ↓</th>
+              <th>GPU Mem ↓</th>
+          </tr>
+    </thead>
+    <tbody align="center">
+          <tr>
+              <td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td>
+              <td>8.3B</td>
+              <td>71.6</td>
+              <td>3h</td>
+              <td>60G</td>
+          </tr>
+          <tr>
+              <td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
+              <td>10.3B</td>
+              <td><b>73.6</td>
+              <td>2.63h</td>
+              <td>32G</td>
+          </tr>
+          <tr>
+              <td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
+              <td>8.7B</td>
+              <td>73.5</td>
+              <td><b>0.26h</td>
+              <td><b>28G</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+
+Both Video-MME and OpenCompass were evaluated using 8×A100 GPUs for inference. The reported inference time of Video-MME includes full model-side computation, and excludes the external cost of video frame extraction (dependent on specific frame extraction tools) for fair comparison.
+
+### Examples
+
+<div align="center">
+  <a href="https://www.youtube.com/watch?v=Cn23FujYMMU"><img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/MiniCPM-V%204.5-8.26_img.jpeg", width=70%></a>
+</div>
+
+<div style="display: flex; flex-direction: column; align-items: center;">
+  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case1.png" alt="en_case1" style="margin-bottom: 5px;">
+  <img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case2.png" alt="en_case2" style="margin-bottom: 5px;">
+  <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;"/>
+</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:
+
+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 Video
+
+```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
+
+    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': 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)
+```
+
+#### 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)
+
+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,
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
+
+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>
+
+
+## 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).
+
+#### 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
+
+👏 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)
+
+## Citation
+
+If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
+
+```bib
+@misc{yu2025minicpmv45cookingefficient,
+      title={MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe}, 
+      author={Tianyu Yu and Zefan Wang and Chongyi Wang and Fuwei Huang and Wenshuo Ma and Zhihui He and Tianchi Cai and Weize Chen and Yuxiang Huang and Yuanqian Zhao and Bokai Xu and Junbo Cui and Yingjing Xu and Liqing Ruan and Luoyuan Zhang and Hanyu Liu and Jingkun Tang and Hongyuan Liu and Qining Guo and Wenhao Hu and Bingxiang He and Jie Zhou and Jie Cai and Ji Qi and Zonghao Guo and Chi Chen and Guoyang Zeng and Yuxuan Li and Ganqu Cui and Ning Ding and Xu Han and Yuan Yao and Zhiyuan Liu and Maosong Sun},
+      year={2025},
+      eprint={2509.18154},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2509.18154}, 
+}
+
+@article{yao2024minicpm,
+  title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
+  author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
+  journal={Nat Commun 16, 5509 (2025)},
+  year={2025}
+}
+
+```

added_tokens.json

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+  "</image>": 151670,
+  "</image_id>": 151682,
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+}

config.json

@@ -0,0 +1,58 @@
+{
+  "_name_or_path": "openbmb/MiniCPM-V-4_5",
+  "version": 4.5,
+  "architectures": [
+    "MiniCPMV"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_minicpm.MiniCPMVConfig",
+    "AutoModel": "modeling_minicpmv.MiniCPMV",
+    "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151748,
+  "batch_vision_input": true,
+  "batch_3d_resampler": true,
+  "drop_vision_last_layer": false,
+  "image_size": 448,
+  "model_type": "minicpmv",
+  "patch_size": 14,
+  "query_num": 64,
+  "slice_config": {
+    "max_slice_nums": 9,
+    "patch_size": 14,
+    "model_type": "minicpmv"
+  },
+  "slice_mode": true,
+  "vision_config": {
+    "hidden_size": 1152,
+    "image_size": 980,
+    "intermediate_size": 4304,
+    "model_type": "siglip",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14
+  }
+}

configuration_minicpm.py

@@ -0,0 +1,101 @@
+# coding=utf-8
+""" MiniCPMV model configuration"""
+
+import os
+from typing import Union
+
+from transformers.utils import logging
+from transformers import Qwen3Config, PretrainedConfig
+from .modeling_navit_siglip import SiglipVisionConfig
+
+logger = logging.get_logger(__name__)
+
+
+class MiniCPMVSliceConfig(PretrainedConfig):
+    model_type = "minicpmv"
+
+    def __init__(
+        self,
+        patch_size=14,
+        max_slice_nums=9,
+        scale_resolution=448,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.patch_size = patch_size
+        self.max_slice_nums = max_slice_nums
+        self.scale_resolution = scale_resolution
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if config_dict.get("model_type") == "minicpmv":
+            config_dict = config_dict["slice_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+
+class MiniCPMVConfig(Qwen3Config):
+    model_type = "minicpmv"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    default_vision_config = {
+        "hidden_size": 1152,
+        "image_size": 980,
+        "intermediate_size": 4304,
+        "model_type": "siglip",
+        "num_attention_heads": 16,
+        "num_hidden_layers": 27,
+        "patch_size": 14,
+    }
+
+    def __init__(
+        self,
+        use_cache=True,
+        query_num=64,
+        image_size=448,
+        drop_vision_last_layer=True,
+        batch_vision_input=True,
+        slice_config=None,
+        vision_config=None,
+        use_image_id=True,
+        vision_batch_size=16,
+        batch_3d_resampler=True,
+        **kwargs,
+    ):
+        self.use_cache = use_cache
+        self.query_num = query_num
+        self.image_size = image_size
+        self.drop_vision_last_layer = drop_vision_last_layer
+        self.batch_vision_input = batch_vision_input
+        self.use_image_id = use_image_id
+        self.vision_batch_size = vision_batch_size
+        self.batch_3d_resampler = batch_3d_resampler
+
+        if slice_config is None:
+            self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
+        else:
+            self.slice_config = MiniCPMVSliceConfig(**slice_config)
+        self.slice_mode = True
+
+        # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
+        if vision_config is None:
+            self.vision_config = SiglipVisionConfig(**self.default_vision_config)
+        elif isinstance(vision_config, dict):
+            self.vision_config = SiglipVisionConfig(**vision_config)
+        elif isinstance(vision_config, SiglipVisionConfig):
+            self.vision_config = vision_config
+
+        self.patch_size = self.vision_config.patch_size
+
+        super().__init__(**kwargs)

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "chat_template_kwargs": {"enable_thinking": false},
+  "transformers_version": "4.51.0"
+}

image_processing_minicpmv.py

@@ -0,0 +1,501 @@
+from typing import Optional, Union, Dict, Any, List
+from itertools import chain
+
+import torch
+import math
+import PIL.Image
+import PIL.ImageSequence
+import numpy as np
+import PIL
+from PIL import Image
+
+from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers import AutoImageProcessor
+from transformers.image_transforms import to_channel_dimension_format
+from transformers.image_utils import (
+    ImageInput, 
+    make_list_of_images, 
+    valid_images, 
+    is_torch_tensor, 
+    is_batched,
+    to_numpy_array, 
+    infer_channel_dimension_format,
+    ChannelDimension
+)
+
+
+def recursive_converter(converter, value):
+    if isinstance(value, list):
+        new_value = []
+        for v in value:
+            new_value += [recursive_converter(converter, v)]
+        return new_value
+    else:
+        return converter(value)
+
+def list_depth(lst):
+    if not isinstance(lst, list) and not isinstance(lst, np.ndarray):
+        return 0
+    # if not lst:  # 空列表
+    #     return 1
+    return 1 + max(list_depth(item) for item in lst)
+
+class MiniCPMVBatchFeature(BatchFeature):
+    r"""
+    Extend from BatchFeature for supporting various image size
+    """
+    def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
+        super().__init__(data)
+        self.convert_to_tensors(tensor_type=tensor_type)
+
+    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
+        if tensor_type is None:
+            return self
+        
+        is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type)
+
+        def converter(value):
+            try:
+                if not is_tensor(value):
+                    tensor = as_tensor(value)
+                    return tensor
+            except:  # noqa E722
+                if key == "overflowing_values":
+                    raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
+                raise ValueError(
+                    "Unable to create tensor, you should probably activate padding "
+                    "with 'padding=True' to have batched tensors with the same length."
+                )
+
+
+        for key, value in self.items():
+            self[key] = recursive_converter(converter, value)
+        return self
+            
+    def to(self, *args, **kwargs) -> "MiniCPMVBatchFeature":
+        requires_backends(self, ["torch"])
+        import torch
+
+        def cast_tensor(v):
+            # check if v is a floating point
+            if torch.is_floating_point(v):
+                # cast and send to device
+                return v.to(*args, **kwargs)
+            elif device is not None:
+                return v.to(device=device)
+            else:
+                return v
+
+        new_data = {}
+        device = kwargs.get("device")
+        # Check if the args are a device or a dtype
+        if device is None and len(args) > 0:
+            # device should be always the first argument
+            arg = args[0]
+            if is_torch_dtype(arg):
+                # The first argument is a dtype
+                pass
+            elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
+                device = arg
+            else:
+                # it's something else
+                raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
+        # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
+        for k, v in self.items():
+            new_data[k] = recursive_converter(cast_tensor, v)
+        self.data = new_data
+        return self
+
+
+class MiniCPMVImageProcessor(BaseImageProcessor):
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+            self, 
+            max_slice_nums=9,
+            scale_resolution=448,
+            patch_size=14,
+            **kwargs):
+        super().__init__(**kwargs)
+        self.max_slice_nums = max_slice_nums
+        self.scale_resolution = scale_resolution
+        self.patch_size = patch_size
+        self.use_image_id = kwargs.pop("use_image_id", False)
+        self.image_feature_size = kwargs.pop("image_feature_size", 64)
+        self.im_start_token = kwargs.pop("im_start", "<image>")
+        self.im_end_token = kwargs.pop("im_end", "</image>")
+        self.slice_start_token = kwargs.pop("slice_start", "<slice>")
+        self.slice_end_token = kwargs.pop("slice_end", "</slice>")
+        self.unk_token = kwargs.pop("unk", "<unk>")
+        self.im_id_start = kwargs.pop("im_id_start", "<image_id>")
+        self.im_id_end = kwargs.pop("im_id_end", "</image_id>")
+        self.slice_mode = kwargs.pop("slice_mode", True)
+        self.mean = np.array(kwargs.pop("norm_mean", [0.5, 0.5, 0.5]))
+        self.std = np.array(kwargs.pop("norm_std", [0.5, 0.5, 0.5]))
+        self.version = kwargs.pop("version", 2.0)
+
+    def ensure_divide(self, length, patch_size):
+        return max(round(length / patch_size) * patch_size, patch_size)
+
+    def find_best_resize(self,
+                         original_size,
+                         scale_resolution,
+                         patch_size,
+                         allow_upscale=False):
+        width, height = original_size
+        if (width * height >
+                scale_resolution * scale_resolution) or allow_upscale:
+            r = width / height
+            height = int(scale_resolution / math.sqrt(r))
+            width = int(height * r)
+        best_width = self.ensure_divide(width, patch_size)
+        best_height = self.ensure_divide(height, patch_size)
+        return (best_width, best_height)
+
+    def get_refine_size(self,
+                        original_size,
+                        grid,
+                        scale_resolution,
+                        patch_size,
+                        allow_upscale=False):
+        width, height = original_size
+        grid_x, grid_y = grid
+
+        refine_width = self.ensure_divide(width, grid_x)
+        refine_height = self.ensure_divide(height, grid_y)
+
+        grid_width = refine_width / grid_x
+        grid_height = refine_height / grid_y
+
+        best_grid_size = self.find_best_resize((grid_width, grid_height),
+                                               scale_resolution,
+                                               patch_size,
+                                               allow_upscale=allow_upscale)
+        refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
+        return refine_size
+
+    def split_to_patches(self, image, grid):
+        patches = []
+        width, height = image.size
+        grid_x = int(width / grid[0])
+        grid_y = int(height / grid[1])
+        for i in range(0, height, grid_y):
+            images = []
+            for j in range(0, width, grid_x):
+                box = (j, i, j + grid_x, i + grid_y)
+                patch = image.crop(box)
+                images.append(patch)
+            patches.append(images)
+        return patches
+
+    def slice_image(
+        self, image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
+    ):
+        original_size = image.size
+        source_image = None
+        best_grid = self.get_sliced_grid(original_size, max_slice_nums, never_split)
+        patches = []
+
+        if best_grid is None:
+            # dont need to slice, upsample
+            best_size = self.find_best_resize(
+                original_size, scale_resolution, patch_size, allow_upscale=True
+            )
+            source_image = image.resize(best_size, resample=Image.Resampling.BICUBIC)
+        else:
+            # source image, down-sampling and ensure divided by patch_size
+            best_resize = self.find_best_resize(original_size, scale_resolution, patch_size)
+            source_image = image.copy().resize(best_resize, resample=Image.Resampling.BICUBIC)
+            refine_size = self.get_refine_size(
+                original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
+            )
+            refine_image = image.resize(refine_size, resample=Image.Resampling.BICUBIC)
+            patches = self.split_to_patches(refine_image, best_grid)
+
+        return source_image, patches, best_grid
+
+    def get_grid_placeholder(self, grid):
+        if grid is None:
+            return ""
+        slice_image_placeholder = (
+            self.slice_start_token 
+            + self.unk_token * self.image_feature_size
+            + self.slice_end_token
+        )
+
+        cols = grid[0]
+        rows = grid[1]
+        slices = []
+        for i in range(rows):
+            lines = []
+            for j in range(cols):
+                lines.append(slice_image_placeholder)
+            slices.append("".join(lines))
+            
+        slice_placeholder = "\n".join(slices)
+        return slice_placeholder
+
+    def get_image_id_placeholder(self, idx=0):
+        return f"{self.im_id_start}{idx}{self.im_id_end}"
+    
+    def get_sliced_images(self, image, max_slice_nums=None):
+        slice_images = []
+
+        if not self.slice_mode:
+            return [image]
+
+        max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
+        assert max_slice_nums > 0 
+        source_image, patches, sliced_grid = self.slice_image(
+            image,
+            max_slice_nums,  # default: 9
+            self.scale_resolution,  # default: 448
+            self.patch_size  # default: 14
+        )
+
+        slice_images.append(source_image)
+        if len(patches) > 0:
+            for i in range(len(patches)):
+                for j in range(len(patches[0])):
+                    slice_images.append(patches[i][j])
+        return slice_images
+
+    def get_sliced_grid(self, image_size, max_slice_nums, nerver_split=False):
+        original_width, original_height = image_size
+        log_ratio = math.log(original_width / original_height)
+        ratio = original_width * original_height / (self.scale_resolution * self.scale_resolution)
+        multiple = min(math.ceil(ratio), max_slice_nums)
+        if multiple <= 1 or nerver_split:
+            return None
+        candidate_split_grids_nums = []
+        for i in [multiple - 1, multiple, multiple + 1]:
+            if i == 1 or i > max_slice_nums:
+                continue
+            candidate_split_grids_nums.append(i)
+        
+        candidate_grids = []
+        for split_grids_nums in candidate_split_grids_nums:
+            m = 1
+            while m <= split_grids_nums:
+                if split_grids_nums % m == 0:
+                    candidate_grids.append([m, split_grids_nums // m])
+                m += 1
+
+        best_grid = [1, 1]
+        min_error = float("inf")
+        for grid in candidate_grids:
+            error = abs(log_ratio - math.log(grid[0] / grid[1]))
+            if error < min_error:
+                best_grid = grid
+                min_error = error
+        
+        return best_grid
+    
+    def get_slice_image_placeholder(self, image_size, image_idx=0, max_slice_nums=None, use_image_id=None):
+        max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
+        assert max_slice_nums > 0        
+        grid = self.get_sliced_grid(image_size=image_size, max_slice_nums=max_slice_nums)
+
+        image_placeholder = (
+            self.im_start_token 
+            + self.unk_token * self.image_feature_size 
+            + self.im_end_token
+        )
+        use_image_id = self.use_image_id if use_image_id is None else bool(use_image_id)
+        if use_image_id:
+            final_placeholder = self.get_image_id_placeholder(image_idx) + image_placeholder
+        else:
+            final_placeholder = image_placeholder
+
+        if self.slice_mode:
+            final_placeholder = final_placeholder + self.get_grid_placeholder(grid=grid)
+        return final_placeholder
+        
+    def to_pil_image(self, image, rescale=None) -> PIL.Image.Image:
+        """
+        Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
+        needed.
+
+        Args:
+            image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):
+                The image to convert to the PIL Image format.
+            rescale (`bool`, *optional*):
+                Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
+                default to `True` if the image type is a floating type, `False` otherwise.
+        """
+        if isinstance(image, PIL.Image.Image):
+            return image
+        if is_torch_tensor(image):
+            image = image.numpy()
+
+        if isinstance(image, np.ndarray):
+            if rescale is None:
+                # rescale default to the array being of floating type.
+                rescale = isinstance(image.flat[0], np.floating)
+            # If the channel as been moved to first dim, we put it back at the end.
+            if image.ndim == 3 and image.shape[0] in [1, 3]:
+                image = image.transpose(1, 2, 0)
+            if rescale:
+                image = image * 255
+            image = image.astype(np.uint8)
+            return PIL.Image.fromarray(image)
+        return image
+
+    def reshape_by_patch(self, image):
+        """
+        :param image: shape [3, H, W]
+        :param patch_size:
+        :return: [3, patch_size, HW/patch_size]
+        """
+        image = torch.from_numpy(image)
+        patch_size = self.patch_size
+        patches = torch.nn.functional.unfold(
+            image,
+            (patch_size, patch_size),
+            stride=(patch_size, patch_size)
+        )
+
+        patches = patches.reshape(image.size(0), patch_size, patch_size, -1)
+        patches = patches.permute(0, 1, 3, 2).reshape(image.size(0), patch_size, -1)
+        return patches.numpy()
+
+    def preprocess(
+            self, 
+            images: Union[Image.Image, List[Image.Image], List[List[Image.Image]]],
+            do_pad: Optional[bool] = True, # TODO: add pad for MiniCPM-Llama3-V-2_5
+            max_slice_nums: int = None,
+            temporal_ids: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
+            return_tensors: Optional[Union[str, TensorType]] = None,
+            **kwargs
+        ) -> MiniCPMVBatchFeature:
+        if isinstance(images, Image.Image):
+            images_list = [[images]]
+        elif isinstance(images[0], Image.Image):
+            images_list = [images]
+        else:
+            images_list = images
+
+        if temporal_ids is not None:
+            if list_depth(temporal_ids) == 2:
+                temporal_ids = [temporal_ids]
+
+        new_images_list = []
+        image_sizes_list = []
+        tgt_sizes_list = []
+        temporal_ids_list = []
+        skip_image_idx_list = []
+        
+        for batch_idx, _images in enumerate(images_list):
+            if _images is None or len(_images) == 0:
+                new_images_list.append([])
+                image_sizes_list.append([])
+                tgt_sizes_list.append([])
+                temporal_ids_list.append([])
+                skip_image_idx_list.append([])
+                continue             
+            if not valid_images(_images):
+                raise ValueError(
+                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                    "torch.Tensor, tf.Tensor or jax.ndarray."
+                )
+            
+            _images = [self.to_pil_image(image).convert("RGB") for image in _images]
+            input_data_format = infer_channel_dimension_format(np.array(_images[0]))
+
+            new_images = []
+            image_sizes = [image.size for image in _images]
+            tgt_sizes = []
+            tp_ids = []
+            skip_image_idx = []
+
+            # for image in _images:
+            #     image_patches = self.get_sliced_images(image, max_slice_nums)
+            #     image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches]
+            #     image_patches = [
+            #         self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format)
+            #             for image in image_patches
+            #     ]
+            #     image_patches = [
+            #         to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format) 
+            #             for image in image_patches
+            #     ]
+            #     for slice_image in image_patches:
+            #         new_images.append(self.reshape_by_patch(slice_image))
+            #         tgt_sizes.append(np.array((slice_image.shape[1] // self.patch_size, slice_image.shape[2] // self.patch_size)))
+
+            if temporal_ids is None:
+                # no temporal ids
+                for image in _images:
+                    image_patches = self.get_sliced_images(image, max_slice_nums)
+                    image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches]
+                    image_patches = [
+                        self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format)
+                            for image in image_patches
+                    ]
+                    image_patches = [
+                        to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format)
+                            for image in image_patches
+                    ]
+                    for slice_image in image_patches:
+                        new_images.append(self.reshape_by_patch(slice_image))
+                        tgt_sizes.append(np.array((slice_image.shape[1] // self.patch_size, slice_image.shape[2] // self.patch_size)))
+                    
+                    tp_ids.extend([[-1]] * len(image_patches))
+            else:
+                temporal_ids_flatten = list(chain.from_iterable(temporal_ids[batch_idx]))
+                assert len(temporal_ids_flatten) == len(_images)
+                frame_groups = []
+                s = 0
+                for group in temporal_ids[batch_idx]:
+                    frame_groups.append(_images[s:s+len(group)])
+                    s += len(group)
+
+                skip_start = 0
+                for frame_group, tp_id in zip(frame_groups, temporal_ids[batch_idx]):
+                    image_patches_group = []
+                    for frame in frame_group:
+                        image_patches = self.get_sliced_images(frame, max_slice_nums)
+                        image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches]
+                        image_patches = [
+                            self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format)
+                                for image in image_patches
+                        ]
+                        image_patches = [
+                            to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format)
+                                for image in image_patches
+                        ]
+                        image_patches_group.append(image_patches)
+                    
+                    group_cnt = len(image_patches_group[0])
+                    for gidx in range(group_cnt):
+                        group_images = [s[gidx] for s in  image_patches_group]
+                        tgt_sizes.extend([np.array((i.shape[1] // self.patch_size, i.shape[2] // self.patch_size)) for i in group_images])
+
+                        group_images = [self.reshape_by_patch(i) for i in group_images]
+                        new_images.extend(group_images)
+                        tp_ids.append(tp_id)
+                    skip_image_idx.extend(list(range(skip_start + 1, skip_start + len(frame_group))))
+                    skip_start += len(frame_group)
+
+            if tgt_sizes:
+                tgt_sizes = np.vstack(tgt_sizes)
+            
+            new_images_list.append(new_images)
+            image_sizes_list.append(image_sizes)
+            tgt_sizes_list.append(tgt_sizes)
+            temporal_ids_list.append(tp_ids)
+            skip_image_idx_list.append(skip_image_idx)
+
+        data = {
+            "pixel_values": new_images_list,
+            "image_sizes": image_sizes_list,
+            "tgt_sizes": tgt_sizes_list,
+            "temporal_ids": temporal_ids_list,
+            "skip_image_idx": skip_image_idx_list
+        }
+
+            
+        return MiniCPMVBatchFeature(data=data, tensor_type=return_tensors)
+
+AutoImageProcessor.register("MiniCPMVImageProcessor", MiniCPMVImageProcessor)

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+}

modeling_minicpmv.py

@@ -0,0 +1,485 @@
+import math
+from typing import List, Optional
+import json
+import torch
+import torchvision
+
+from threading import Thread
+from copy import deepcopy
+from PIL import Image
+from transformers import AutoProcessor, Qwen3PreTrainedModel, Qwen3ForCausalLM, TextIteratorStreamer
+
+from .configuration_minicpm import MiniCPMVConfig
+from .modeling_navit_siglip import SiglipVisionTransformer
+from .resampler import Resampler
+
+
+
+class MiniCPMVPreTrainedModel(Qwen3PreTrainedModel):
+    config_class = MiniCPMVConfig
+
+
+class MiniCPMV(MiniCPMVPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.llm = Qwen3ForCausalLM(config)
+        self.vpm = self.init_vision_module()
+        self.vision_dim = self.vpm.embed_dim
+        self.embed_dim = self.llm.config.hidden_size
+        self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
+        self.processor = None
+
+        self.terminators = ['<|im_end|>', '<|endoftext|>']
+
+    def init_vision_module(self):
+        # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
+        if self.config._attn_implementation == 'flash_attention_2':
+            self.config.vision_config._attn_implementation = 'flash_attention_2'
+        else:
+            # not suport sdpa
+            self.config.vision_config._attn_implementation = 'eager'
+        model = SiglipVisionTransformer(self.config.vision_config)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+
+        setattr(model, 'embed_dim', model.embeddings.embed_dim)
+        setattr(model, 'patch_size', model.embeddings.patch_size)
+
+        return model
+
+    def init_resampler(self, embed_dim, vision_dim):
+        return Resampler(
+            num_queries=self.config.query_num,
+            embed_dim=embed_dim,
+            num_heads=embed_dim // 128,
+            kv_dim=vision_dim,
+            adaptive=True,
+            batch_infer=self.config.batch_3d_resampler
+        )
+
+    def get_input_embeddings(self):
+        return self.llm.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.llm.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.llm.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.llm.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.llm = decoder
+
+    def get_decoder(self):
+        return self.llm
+
+    def get_vllm_embedding(self, data):
+        if 'vision_hidden_states' not in data:
+            dtype = self.llm.model.embed_tokens.weight.dtype
+            device = self.llm.model.embed_tokens.weight.device
+            tgt_sizes = data['tgt_sizes']
+            pixel_values_list = data['pixel_values']
+            temporal_ids = data.get('temporal_ids', None)
+            vision_hidden_states = []
+            all_pixel_values = []
+            img_cnt = []
+            all_temporal_ids = None
+
+            for pixel_values in pixel_values_list:
+                img_cnt.append(len(pixel_values))
+                all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values])
+
+            if temporal_ids is not None:
+                all_temporal_ids = []
+                for t in temporal_ids:
+                    all_temporal_ids.extend(t)
+
+            # exist image
+            if all_pixel_values:
+                tgt_sizes = [tgt_size for tgt_size in tgt_sizes if isinstance(tgt_size, torch.Tensor)]
+                tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
+
+                max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
+
+                all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True,
+                                                                   padding_value=0.0)
+                B, L, _ = all_pixel_values.shape
+                all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
+
+                patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
+                for i in range(B):
+                    patch_attn_mask[i, 0, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
+
+                vision_batch_size = self.config.vision_batch_size
+                all_pixel_values = all_pixel_values.type(dtype)
+                if B > vision_batch_size:
+                    hs = []
+                    for i in range(0, B, vision_batch_size):
+                        start_idx = i
+                        end_idx = i + vision_batch_size
+                        tmp_hs = self.vpm(all_pixel_values[start_idx:end_idx], patch_attention_mask=patch_attn_mask[start_idx:end_idx], tgt_sizes=tgt_sizes[start_idx:end_idx]).last_hidden_state
+                        hs.append(tmp_hs)
+                    vision_embedding = torch.cat(hs, dim=0)
+                else:
+                    vision_embedding = self.vpm(all_pixel_values, patch_attention_mask=patch_attn_mask, tgt_sizes=tgt_sizes).last_hidden_state
+                vision_embedding = self.resampler(vision_embedding, tgt_sizes, all_temporal_ids)
+
+                start = 0
+                for pixel_values in pixel_values_list:
+                    img_cnt = len(pixel_values)
+                    if img_cnt > 0:
+                        vision_hidden_states.append(vision_embedding[start: start + img_cnt])
+                        start += img_cnt
+                    else:
+                        vision_hidden_states.append([])
+            else: # no image
+                if self.training:
+                    dummy_image = torch.zeros(
+                        (1, 3, 224, 224),
+                        device=device, dtype=dtype
+                    )
+                    tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.patch_size)]]).type(torch.int32)
+                    dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes)
+                else:
+                    dummy_feature = []
+                for _ in range(len(pixel_values_list)):
+                    vision_hidden_states.append(dummy_feature)
+
+        else:
+            vision_hidden_states = data['vision_hidden_states']
+
+        if hasattr(self.llm.config, 'scale_emb'):
+            vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb
+        else:
+            vllm_embedding = self.llm.model.embed_tokens(data['input_ids'])
+
+        vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance(
+            i, torch.Tensor) else i for i in vision_hidden_states]
+
+        bs = len(data['input_ids'])
+        device = vllm_embedding.device
+        embed_dim = vllm_embedding.shape[-1]
+
+        updated_vllm_embedding = torch.empty_like(vllm_embedding)
+        
+        for i in range(bs):
+            cur_vs_hs = vision_hidden_states[i]
+            cur_vllm_emb = vllm_embedding[i]
+
+            if len(cur_vs_hs) == 0:
+                updated_vllm_embedding[i] = cur_vllm_emb
+                continue
+                
+            cur_image_bound = data['image_bound'][i]
+
+            if len(cur_image_bound) > 0:
+                image_indices = torch.cat([
+                    torch.arange(r[0], r[1], dtype=torch.long) 
+                    for r in cur_image_bound
+                ]).to(device)
+
+                indices_expanded = image_indices.view(-1, 1).expand(-1, embed_dim)
+                vision_features = cur_vs_hs.view(-1, embed_dim)
+
+                updated_emb = cur_vllm_emb.clone()
+                vision_features = vision_features.to(cur_vllm_emb.device)
+
+                updated_emb.scatter_(0, indices_expanded, vision_features)
+                updated_vllm_embedding[i] = updated_emb
+            elif self.training:
+                if isinstance(cur_vs_hs, torch.Tensor) and cur_vs_hs.numel() > 0:
+                    dummy_gradient_term = cur_vs_hs.sum() * 0.0
+                    updated_vllm_embedding[i] = cur_vllm_emb + dummy_gradient_term
+                else:
+                    updated_vllm_embedding[i] = cur_vllm_emb
+            else:
+                updated_vllm_embedding[i] = cur_vllm_emb
+
+        vllm_embedding = updated_vllm_embedding
+
+        return vllm_embedding, vision_hidden_states
+
+
+    def forward(self, data, **kwargs):
+        if isinstance(data, torch.Tensor):
+            attention_mask = torch.ones_like(data, dtype=torch.bool)
+            kwargs = {'attention_mask': attention_mask}
+            return self.llm(
+                input_ids=data,
+                **kwargs
+            )
+    
+        if data is None:
+            data = {
+                "input_ids": kwargs.pop("input_ids", None),
+                "pixel_values": kwargs.pop("pixel_values", None),
+                "image_bound": kwargs.pop("image_bound", None),
+                "tgt_sizes": kwargs.pop("tgt_sizes", None),
+                "position_ids": kwargs.pop("position_ids", None),
+            }
+        else:
+            kwargs.pop("input_ids", None)
+            kwargs.pop("pixel_values", None)
+            kwargs.pop("image_bound", None)
+            kwargs.pop("tgt_sizes", None)
+            kwargs.pop("position_ids", None)
+        kwargs.pop("inputs_embeds", None)
+    
+        vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data)
+
+        position_ids = data["position_ids"]
+        if position_ids.dtype != torch.int64:
+            position_ids = position_ids.long()
+
+        # compatible with llama factory
+        for key in ["input_ids", "inputs_embeds", "position_ids"]:
+            if key in kwargs:
+                del kwargs[key]
+
+        return self.llm(
+            input_ids=None,
+            position_ids=position_ids,
+            inputs_embeds=vllm_embedding,
+            **kwargs
+        )
+    
+    def _decode(self, inputs_embeds, tokenizer, attention_mask, decode_text=False, **kwargs):
+        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
+        output = self.llm.generate(
+            inputs_embeds=inputs_embeds,
+            pad_token_id=0,
+            eos_token_id=terminators,
+            attention_mask=attention_mask,
+            **kwargs
+        )
+        if decode_text:
+            return self._decode_text(output, tokenizer)
+        return output
+
+    def _decode_stream(self, inputs_embeds, tokenizer, **kwargs):
+        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
+        streamer = TextIteratorStreamer(tokenizer=tokenizer)
+        generation_kwargs = {
+            'inputs_embeds': inputs_embeds,
+            'pad_token_id': 0,
+            'eos_token_id': terminators,
+            'streamer': streamer
+        }
+        generation_kwargs.update(kwargs)
+
+        thread = Thread(target=self.llm.generate, kwargs=generation_kwargs)
+        thread.start()
+    
+        return streamer
+
+    def _decode_text(self, result_ids, tokenizer):
+        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
+        result_text = []
+        for result in result_ids:
+            result = result[result != 0]
+            if result[0] == tokenizer.bos_id:
+                result = result[1:]
+            if result[-1] in terminators:
+                result = result[:-1]
+            result_text.append(tokenizer.decode(result).strip())
+        return result_text
+
+    def generate(
+        self,
+        input_ids=None,
+        pixel_values=None,
+        tgt_sizes=None,
+        image_bound=None,
+        temporal_ids=None,
+        attention_mask=None,
+        tokenizer=None,
+        vision_hidden_states=None,
+        return_vision_hidden_states=False,
+        stream=False,
+        decode_text=False,
+        **kwargs
+    ):
+        assert input_ids is not None
+        assert len(input_ids) == len(pixel_values)
+
+        model_inputs = {
+            "input_ids": input_ids,
+            "image_bound": image_bound,
+            "temporal_ids": temporal_ids,
+        }
+
+        if vision_hidden_states is None:
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs['tgt_sizes'] = tgt_sizes
+        else:
+            model_inputs["vision_hidden_states"] = vision_hidden_states
+
+        with torch.inference_mode():
+            (
+                model_inputs["inputs_embeds"],
+                vision_hidden_states,
+            ) = self.get_vllm_embedding(model_inputs)
+
+            if stream:
+                result = self._decode_stream(model_inputs["inputs_embeds"], tokenizer, **kwargs)
+            else:
+                result = self._decode(model_inputs["inputs_embeds"], tokenizer, attention_mask, decode_text=decode_text, **kwargs)
+
+        if return_vision_hidden_states:
+            return result, vision_hidden_states
+        
+        return result
+
+    def chat(
+        self,
+        image=None,
+        msgs=None,
+        tokenizer=None,
+        processor=None,
+        vision_hidden_states=None,
+        max_new_tokens=2048,
+        min_new_tokens=0,
+        sampling=True,
+        max_inp_length=16384,
+        system_prompt='',
+        stream=False,
+        max_slice_nums=None,
+        use_image_id=None,
+        temporal_ids=None,
+        enable_thinking=False,
+        **kwargs
+    ):
+        if isinstance(msgs[0], list):
+            batched = True
+        else:
+            batched = False
+        msgs_list = msgs
+        images_list = image
+        
+        if batched is False:
+            images_list, msgs_list = [images_list], [msgs_list]
+        else:
+            assert images_list is None, "Please integrate image to msgs when using batch inference."
+            images_list = [None] * len(msgs_list)
+        assert len(images_list) == len(msgs_list), "The batch dim of images_list and msgs_list should be the same."
+
+        if processor is None:
+            if self.processor is None:
+                self.processor = AutoProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True)
+            processor = self.processor
+        
+        assert self.config.query_num == processor.image_processor.image_feature_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
+        assert self.config.patch_size == processor.image_processor.patch_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
+        assert self.config.use_image_id == processor.image_processor.use_image_id, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
+        assert self.config.slice_config.max_slice_nums == processor.image_processor.max_slice_nums, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
+        assert self.config.slice_mode == processor.image_processor.slice_mode, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
+
+
+        prompts_lists = []
+        input_images_lists = []
+        for image, msgs in zip(images_list, msgs_list):
+            if isinstance(msgs, str):
+                msgs = json.loads(msgs)
+            copy_msgs = deepcopy(msgs)
+
+            assert len(msgs) > 0, "msgs is empty"
+            assert sampling or not stream, "if use stream mode, make sure sampling=True"
+
+            if image is not None and isinstance(copy_msgs[0]["content"], str):
+                copy_msgs[0]["content"] = [image, copy_msgs[0]["content"]]
+
+            images = []
+            for i, msg in enumerate(copy_msgs):
+                role = msg["role"]
+                content = msg["content"]
+                assert role in ["system", "user", "assistant"]
+                if isinstance(content, str):
+                    content = [content]
+                cur_msgs = []
+                for c in content:
+                    if isinstance(c, Image.Image):
+                        images.append(c)
+                        cur_msgs.append("(<image>./</image>)")
+                    elif isinstance(c, str):
+                        cur_msgs.append(c)
+                msg["content"] = "\n".join(cur_msgs)
+
+            if system_prompt:
+                sys_msg = {'role': 'system', 'content': system_prompt}
+                copy_msgs = [sys_msg] + copy_msgs        
+
+
+            prompts_lists.append(processor.tokenizer.apply_chat_template(copy_msgs, tokenize=False, add_generation_prompt=True, enable_thinking=enable_thinking))
+            input_images_lists.append(images)
+
+        if enable_thinking:
+            prefill_answer = '<think>\n'
+        else:
+            prefill_answer = ''
+
+        inputs = processor(
+            prompts_lists, 
+            input_images_lists, 
+            max_slice_nums=max_slice_nums,
+            use_image_id=use_image_id,
+            temporal_ids=temporal_ids,
+            return_tensors="pt", 
+            max_length=max_inp_length
+        ).to(self.device)
+
+        if sampling:
+            generation_config = {
+                "temperature": 0.7,
+                "do_sample": True,
+            }
+            if not enable_thinking:
+                generation_config.update(
+                    {
+                        "top_p": 0.8,
+                        "top_k": 100,
+                        "repetition_penalty": 1.03
+                    }
+                )
+        else:
+            generation_config = {
+                "num_beams": 3,
+                "repetition_penalty": 1.2,
+            }
+            
+        if min_new_tokens > 0:
+            generation_config['min_new_tokens'] = min_new_tokens
+
+        generation_config.update(
+            (k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
+        )
+
+        inputs.pop("image_sizes")
+        with torch.inference_mode():
+            res = self.generate(
+                **inputs,
+                tokenizer=tokenizer,
+                max_new_tokens=max_new_tokens,
+                vision_hidden_states=vision_hidden_states,
+                stream=stream,
+                decode_text=True,
+                **generation_config
+            )
+        
+        if stream:
+            def stream_gen():
+                for text in prefill_answer:
+                    yield text
+                for text in res:
+                    for term in self.terminators:
+                        text = text.replace(term, '')
+                    yield text
+            return stream_gen()
+
+        else:
+            if batched:
+                answer = [prefill_answer + i if prefill_answer else i for i in res]
+            else:
+                answer = prefill_answer + res[0] if prefill_answer else '' + res[0]
+            return answer

modeling_navit_siglip.py

@@ -0,0 +1,937 @@
+# coding=utf-8
+# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Siglip model. """
+# Copied from  HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
+
+
+import os
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+from transformers.activations import ACT2FN
+from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from transformers.modeling_utils import PreTrainedModel
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+class SiglipVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
+    Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
+    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of channels in the input images.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    Example:
+    ```python
+    >>> from transformers import SiglipVisionConfig, SiglipVisionModel
+    >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
+    >>> configuration = SiglipVisionConfig()
+    >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
+    >>> model = SiglipVisionModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "siglip_vision_model"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        image_size=224,
+        patch_size=16,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the vision config dict if we are loading from SiglipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["vision_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+        
+
+_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
+
+SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "google/siglip-base-patch16-224",
+    # See all SigLIP models at https://huggingface.co/models?filter=siglip
+]
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    if tensor.dtype in [torch.float16, torch.bfloat16]:
+        # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
+        og_dtype = tensor.dtype
+        tensor = tensor.to(torch.float32)
+        tensor.erfinv_()
+        tensor = tensor.to(og_dtype)
+    else:
+        tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.0))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    if tensor.dtype == torch.float16:
+        # The `clamp_` op is not (yet?) defined in float16+cpu
+        tensor = tensor.to(torch.float32)
+        tensor.clamp_(min=a, max=b)
+        tensor = tensor.to(torch.float16)
+    else:
+        tensor.clamp_(min=a, max=b)
+
+
+def trunc_normal_tf_(
+    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
+) -> torch.Tensor:
+    """Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \\leq \text{mean} \\leq b`.
+    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
+    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
+    and the result is subsquently scaled and shifted by the mean and std args.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    """
+    with torch.no_grad():
+        _trunc_normal_(tensor, 0, 1.0, a, b)
+        tensor.mul_(std).add_(mean)
+
+
+def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == "fan_in":
+        denom = fan_in
+    elif mode == "fan_out":
+        denom = fan_out
+    elif mode == "fan_avg":
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
+    elif distribution == "normal":
+        with torch.no_grad():
+            tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        with torch.no_grad():
+            tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
+
+
+def default_flax_embed_init(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="normal")
+
+
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
+class SiglipVisionModelOutput(ModelOutput):
+    """
+    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
+    Args:
+        image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+            The image embeddings obtained by applying the projection layer to the pooler_output.
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    image_embeds: Optional[torch.FloatTensor] = None
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+class SiglipVisionEmbeddings(nn.Module):
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches_per_side = self.image_size // self.patch_size
+        self.num_patches = self.num_patches_per_side**2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+
+    def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor, tgt_sizes: Optional[torch.IntTensor]=None) -> torch.Tensor:
+        batch_size = pixel_values.size(0)
+
+        patch_embeds = self.patch_embedding(pixel_values)
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+        max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
+        max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
+        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
+        position_ids = torch.full(
+            size=(
+                batch_size,
+                max_nb_patches_h * max_nb_patches_w,
+            ),
+            fill_value=0,
+        )
+
+        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
+            if tgt_sizes is not None:
+                nb_patches_h = tgt_sizes[batch_idx][0]
+                nb_patches_w = tgt_sizes[batch_idx][1]
+            else:
+                nb_patches_h = p_attn_mask[:, 0].sum()
+                nb_patches_w = p_attn_mask[0].sum()
+
+            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
+            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
+
+            bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
+            bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
+
+            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
+            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
+
+        position_ids = position_ids.to(self.position_embedding.weight.device)
+
+        embeddings = embeddings + self.position_embedding(position_ids)
+        return embeddings
+
+
+class SiglipAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        batch_size, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        k_v_seq_len = key_states.shape[-2]
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
+
+        if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class SiglipFlashAttention2(SiglipAttention):
+    """
+    Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.is_causal = False  # Hack to make sure we don't use a causal mask
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        # if past_key_value is not None:
+        #     cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+        #     key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (LlamaRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                "The input hidden states seems to be silently casted in float32, this might be related to the fact"
+                " you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+
+        # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+        causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
+class SiglipMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
+class SiglipEncoderLayer(nn.Module):
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.self_attn = (
+            SiglipAttention(config)
+            if not self._use_flash_attention_2
+            else SiglipFlashAttention2(config)
+        )
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`.
+            attention_mask (`torch.FloatTensor`):
+                Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+        """
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class SiglipPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = SiglipVisionConfig
+    base_model_prefix = "siglip"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+
+        if isinstance(module, SiglipVisionEmbeddings):
+            width = self.config.hidden_size
+            nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
+        elif isinstance(module, nn.Embedding):
+            default_flax_embed_init(module.weight)
+        elif isinstance(module, SiglipAttention):
+            nn.init.normal_(module.q_proj.weight)
+            nn.init.normal_(module.k_proj.weight)
+            nn.init.normal_(module.v_proj.weight)
+            nn.init.normal_(module.out_proj.weight)
+            nn.init.zeros_(module.q_proj.bias)
+            nn.init.zeros_(module.k_proj.bias)
+            nn.init.zeros_(module.v_proj.bias)
+            nn.init.zeros_(module.out_proj.bias)
+        elif isinstance(module, SiglipMLP):
+            nn.init.normal_(module.fc1.weight)
+            nn.init.normal_(module.fc2.weight)
+            nn.init.normal_(module.fc1.bias, std=1e-6)
+            nn.init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            lecun_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+SIGLIP_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+    Parameters:
+        config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+SIGLIP_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
+class SiglipEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`SiglipEncoderLayer`].
+    Args:
+        config: SiglipConfig
+    """
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    # Ignore copy
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+                [What are attention masks?](../glossary#attention-mask)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+        )
+
+@add_start_docstrings(
+    """The vision model from SigLIP without any head or projection on top.""",
+    SIGLIP_START_DOCSTRING
+)
+class SiglipVisionTransformer(SiglipPreTrainedModel):
+    config_class = SiglipVisionConfig
+    main_input_name = "pixel_values"
+    _supports_flash_attn_2 = True
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__(config)
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.embeddings.patch_embedding
+
+    @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
+    def forward(
+        self,
+        pixel_values,
+        patch_attention_mask: Optional[torch.BoolTensor] = None,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        batch_size = pixel_values.size(0)
+        if patch_attention_mask is None:
+            patch_attention_mask = torch.ones(
+                size=(
+                    batch_size,
+                    pixel_values.size(2) // self.config.patch_size,
+                    pixel_values.size(3) // self.config.patch_size,
+                ),
+                dtype=torch.bool,
+                device=pixel_values.device,
+            )
+
+        hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, tgt_sizes=tgt_sizes)
+
+        patch_attention_mask = patch_attention_mask.view(batch_size, -1)
+        # The call to `_upad_input` in `_flash_attention_forward` is expensive
+        # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
+        # avoiding passing the attention_mask, which is equivalent to attending to the full sequence
+        if not torch.any(~patch_attention_mask):
+            attention_mask=None
+        else:
+            attention_mask = (
+                _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
+                if not self._use_flash_attention_2
+                else patch_attention_mask
+            )
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        if not return_dict:
+            return (last_hidden_state, None) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=None,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

preprocessor_config.json

@@ -0,0 +1,24 @@
+{
+    "image_processor_type": "MiniCPMVImageProcessor",
+    "auto_map": {
+        "AutoProcessor": "processing_minicpmv.MiniCPMVProcessor",
+        "AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor"
+      },
+    "processor_class": "MiniCPMVProcessor",
+    "max_slice_nums": 9,
+    "scale_resolution": 448,
+    "patch_size": 14,
+    "use_image_id": true,
+    "image_feature_size": 64,
+    "im_start": "<image>",
+    "im_end": "</image>",
+    "slice_start": "<slice>",
+    "slice_end": "</slice>",
+    "unk": "<unk>",
+    "im_id_start": "<image_id>",
+    "im_id_end": "</image_id>",
+    "slice_mode": true,
+    "norm_mean": [0.5, 0.5, 0.5],
+    "norm_std": [0.5, 0.5, 0.5],
+    "version": 2.6
+}

processing_minicpmv.py

@@ -0,0 +1,255 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for MiniCPMV.
+"""
+
+from typing import List, Optional, Union, Dict, Any
+import torch
+import re
+
+from transformers.image_processing_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
+
+from .image_processing_minicpmv import MiniCPMVBatchFeature
+
+
+class MiniCPMVProcessor(ProcessorMixin):
+    r"""
+    Constructs a MiniCPMV processor which wraps a MiniCPMV image processor and a MiniCPMV tokenizer into a single processor.
+
+    [`MiniCPMVProcessor`] offers all the functionalities of [`MiniCPMVImageProcessor`] and [`LlamaTokenizerWrapper`]. See the
+    [`~MiniCPMVProcessor.__call__`] and [`~MiniCPMVProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`MiniCPMVImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerWrapper`], *optional*):
+            The tokenizer is a required input.
+    """
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, image_processor=None, tokenizer=None):
+        super().__init__(image_processor, tokenizer)
+        self.version = image_processor.version
+    
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: ImageInput = None,
+        max_length: Optional[int] = None,
+        do_pad: Optional[bool] = True,
+        max_slice_nums: int = None,
+        use_image_id: bool = None,
+        temporal_ids: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+        **kwargs
+    ) -> MiniCPMVBatchFeature:
+
+        if images is not None:
+            # image_inputs = self.image_processor(images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors)
+            image_inputs = self.image_processor(images, do_pad=do_pad, max_slice_nums=max_slice_nums, temporal_ids=temporal_ids, return_tensors=return_tensors)
+        # return self._convert_images_texts_to_inputs(image_inputs, text, max_slice_nums=max_slice_nums, use_image_id=use_image_id, max_length=max_length, **kwargs)
+        return self._convert_images_texts_to_inputs(image_inputs, text, max_slice_nums=max_slice_nums, use_image_id=use_image_id, max_length=max_length, temporal_ids=temporal_ids, **kwargs)
+    
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        output_ids = args[0]
+        result_text = []
+        for result in output_ids:
+            result = result[result != 0]
+            if result[0] == self.tokenizer.bos_id:
+                result = result[1:]
+            if result[-1] == self.tokenizer.eos_id:
+                result = result[:-1]
+            result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
+        return result_text
+        # return self.tokenizer.batch_decode(*args, **kwargs)
+    
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        result = args[0]
+        result = result[result != 0]
+        if result[0] == self.tokenizer.bos_id:
+            result = result[1:]
+        if result[-1] == self.tokenizer.eos_id or (hasattr(self.tokenizer, "eot_id") and result[-1] == self.tokenizer.eot_id):
+            result = result[:-1]
+        return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
+
+    def _convert(
+        self, input_str, max_inp_length: Optional[int] = None
+    ):
+        if self.version > 2.5 or not getattr(self.tokenizer, "add_bos_token", False):
+            input_ids = self.tokenizer.encode(input_str)
+        else:
+            input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
+        if max_inp_length is not None:
+            input_ids = input_ids[:max_inp_length]
+        input_ids = torch.tensor(input_ids, dtype=torch.int32)
+
+        start_cond = (input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id)
+        end_cond = (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
+
+        image_start_tokens = torch.where(start_cond)[0]
+        image_start_tokens += 1
+        image_end_tokens = torch.where(end_cond)[0]
+
+        valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
+
+        image_bounds = torch.hstack(
+            [
+                image_start_tokens[:valid_image_nums].unsqueeze(-1),
+                image_end_tokens[:valid_image_nums].unsqueeze(-1),
+            ]
+        )
+        return input_ids, image_bounds
+
+    def _convert_images_texts_to_inputs(
+            self, 
+            images, 
+            texts: Union[str, List[str]], 
+            truncation=None, 
+            max_length=None,
+            max_slice_nums=None,
+            use_image_id=None, 
+            return_tensors=None,
+            **kwargs
+        ):
+        if images is None or not len(images):
+            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, truncation=truncation, max_length=max_length, **kwargs)
+            return MiniCPMVBatchFeature(data={**model_inputs})
+        
+        pattern = "(<image>./</image>)"
+        # images, image_sizes, tgt_sizes = images["pixel_values"], images["image_sizes"], images["tgt_sizes"]
+        images, image_sizes, tgt_sizes, temporal_ids, skip_image_idx = images["pixel_values"], images["image_sizes"], images["tgt_sizes"], images["temporal_ids"], images["skip_image_idx"]
+        
+        if isinstance(texts, str):
+            texts = [texts]
+        input_ids_list = []
+        image_bounds_list = []
+        for index, (text, skip_idx) in enumerate(zip(texts, skip_image_idx)):
+            image_tags = re.findall(pattern, text)
+            assert len(image_tags) == len(image_sizes[index])
+            text_chunks = text.split(pattern)
+            final_text = ""
+
+            for i in range(len(image_tags)):
+                if i in skip_idx:
+                    image_placeholder = ''
+                    text_chunk = text_chunks[i].strip()
+                    
+                else:
+                    image_placeholder = self.image_processor.get_slice_image_placeholder(
+                        image_sizes[index][i], 
+                        i,
+                        max_slice_nums,
+                        use_image_id
+                    )
+                    text_chunk = text_chunks[i]
+                
+                final_text = final_text + text_chunk + image_placeholder
+                        
+            final_text += text_chunks[-1]
+
+            input_ids, image_bounds = self._convert(final_text, max_length)
+            input_ids_list.append(input_ids)
+            image_bounds_list.append(image_bounds)
+        padded_input_ids, padding_lengths = self.pad(
+            input_ids_list,
+            padding_side="left"
+        )
+        for i, length in enumerate(padding_lengths):
+            image_bounds_list[i] = image_bounds_list[i] + length
+        attention_mask = padded_input_ids.ne(0)
+
+        return MiniCPMVBatchFeature(data={
+            "input_ids": padded_input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": images,
+            "image_sizes": image_sizes,
+            "image_bound": image_bounds_list,
+            "tgt_sizes": tgt_sizes,
+            "temporal_ids": temporal_ids
+        })
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+    def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"):
+        items = []
+        if isinstance(inputs[0], list):
+            assert isinstance(inputs[0][0], torch.Tensor)
+            for it in inputs:
+                for tr in it:
+                    items.append(tr)
+        else:
+            assert isinstance(inputs[0], torch.Tensor)
+            items = inputs
+
+        batch_size = len(items)
+        shape = items[0].shape
+        dim = len(shape)
+        assert dim <= 2
+        if max_length is None:
+            max_length = 0
+        max_length = max(max_length, max(item.shape[-1] for item in items))
+        min_length = min(item.shape[-1] for item in items)
+        dtype = items[0].dtype
+
+        if dim == 0:
+            return torch.stack([item for item in items], dim=0), [0]
+        elif dim == 1:
+            if max_length == min_length:
+                return torch.stack([item for item in items], dim=0), [0] * batch_size
+            tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
+        else:
+            tensor = (
+                torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
+                + padding_value
+            )
+
+        padding_length = []
+        for i, item in enumerate(items):
+            if dim == 1:
+                if padding_side == "left":
+                    tensor[i, -len(item) :] = item.clone()
+                else:
+                    tensor[i, : len(item)] = item.clone()
+            elif dim == 2:
+                if padding_side == "left":
+                    tensor[i, -len(item) :, :] = item.clone()
+                else:
+                    tensor[i, : len(item), :] = item.clone()
+            padding_length.append(tensor.shape[-1] - len(item))
+
+        return tensor, padding_length

resampler.py

@@ -0,0 +1,318 @@
+from functools import partial
+from itertools import chain
+from typing import Optional, Tuple, List
+import numpy as np
+
+import torch
+from torch import nn
+from torch.nn.init import trunc_normal_
+
+from transformers.integrations import is_deepspeed_zero3_enabled
+
+def get_2d_sincos_pos_embed(embed_dim, image_size):
+    """
+    image_size: image_size or (image_height, image_width)
+    return:
+    pos_embed: [image_height, image_width, embed_dim]
+    """
+    if isinstance(image_size, int):
+        grid_h_size, grid_w_size = image_size, image_size
+    else:
+        grid_h_size, grid_w_size = image_size[0], image_size[1]
+
+    grid_h = np.arange(grid_h_size, dtype=np.float32)
+    grid_w = np.arange(grid_w_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0])  # (H, W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1])  # (H, W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=-1)  # (H, W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (H, W)
+    out: (H, W, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000 ** omega  # (D/2,)
+
+    out = np.einsum('hw,d->hwd', pos, omega)  # (H, W, D/2), outer product
+
+    emb_sin = np.sin(out)  # (H, W, D/2)
+    emb_cos = np.cos(out)  # (H, W, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=-1)  # (H, W, D)
+    return emb
+
+def get_1d_sincos_pos_embed_from_temporal_size(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+class Resampler(nn.Module):
+    """
+    A 2D perceiver-resampler network with one cross attention layers by
+       given learnable queries and 2d sincos pos_emb
+    Outputs:
+        A tensor with the shape of (batch_size, num_queries, embed_dim)
+    """
+
+    def __init__(
+            self,
+            num_queries,
+            embed_dim,
+            num_heads,
+            kv_dim=None,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6),
+            adaptive=False,
+            max_size=(70, 70),
+            max_temporal_size=72000,
+            batch_infer=False 
+    ):
+        super().__init__()
+        self.num_queries = num_queries
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.adaptive = adaptive
+        self.max_size = max_size
+        self.max_temporal_size = max_temporal_size
+        self.batch_infer = batch_infer
+
+        self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
+        trunc_normal_(self.query, std=.02)
+
+        if kv_dim is not None and kv_dim != embed_dim:
+            self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
+        else:
+            self.kv_proj = nn.Identity()
+
+        self.attn = nn.MultiheadAttention(embed_dim, num_heads)
+        self.ln_q = norm_layer(embed_dim)
+        self.ln_kv = norm_layer(embed_dim)
+
+        self.ln_post = norm_layer(embed_dim)
+        self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
+
+        self._set_2d_pos_cache(self.max_size)
+        self._set_temporal_pos_cache(self.max_temporal_size)
+        self.apply(self._init_weights)
+
+    def _set_2d_pos_cache(self, max_size, device='cpu'):
+        if is_deepspeed_zero3_enabled():
+            device='cuda'
+        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
+        self.register_buffer("pos_embed", pos_embed, persistent=False)
+
+    def _adjust_pos_cache(self, tgt_sizes, device):
+        max_h = torch.max(tgt_sizes[:, 0])
+        max_w = torch.max(tgt_sizes[:, 1])
+        if max_h > self.max_size[0] or max_w > self.max_size[1]:
+            self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
+            self._set_2d_pos_cache(self.max_size, device)
+    
+    def _set_temporal_pos_cache(self, max_temporal_size, device='cpu'):
+        temporal_size = np.arange(max_temporal_size, dtype=np.float32)
+        pos_embed = torch.from_numpy(get_1d_sincos_pos_embed_from_temporal_size(self.embed_dim, temporal_size)).float().to(device)
+        self.register_buffer("temporal_pos_embed", pos_embed, persistent=False)
+    
+    def _adjust_temporal_pos_cache(self, max_temporal_size, device):
+        if max_temporal_size > self.max_temporal_size:
+            self.max_temporal_size = max_temporal_size
+            self._set_temporal_pos_cache(self.max_temporal_size, device)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def _initialize_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x, tgt_sizes=None, temporal_ids=None):
+        assert x.shape[0] == tgt_sizes.shape[0]
+        bs = x.shape[0]
+
+        device = x.device
+        dtype = x.dtype
+
+        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+
+        self._adjust_pos_cache(tgt_sizes, device=device)
+
+        temporal_pos_emb = False
+        temporal_ids_flatten = None
+        if temporal_ids is not None:
+            # example: [[-1], [-1], [2, 6, 9]]
+            temporal_ids_flatten = list(chain.from_iterable(temporal_ids))
+            max_temporal_size = max(temporal_ids_flatten) + 1
+            if max_temporal_size > -1:
+                temporal_pos_emb = True
+            if max_temporal_size > self.max_temporal_size:
+                self._adjust_temporal_pos_cache(max_temporal_size, device)
+
+
+        max_patch_len = torch.max(patch_len)
+        key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
+
+        pos_embed = []
+        for i in range(bs):
+            tgt_h, tgt_w = tgt_sizes[i]
+            pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype))  # patches * D
+            key_padding_mask[i, patch_len[i]:] = True
+
+        pos_embed = torch.nn.utils.rnn.pad_sequence(
+            pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2)  # BLD => L * B * D
+
+        x = self.kv_proj(x)  # B * L * D
+        x = self.ln_kv(x).permute(1, 0, 2)  # L * B * D
+
+        q = self.ln_q(self.query)  # Q * D
+
+        pos_embed_2d = []
+        pos_embed_temporal = []
+        for i in range(bs):
+            tgt_h, tgt_w = tgt_sizes[i]
+            if temporal_pos_emb:
+                if temporal_ids_flatten[i] == -1:
+                    pos_embed_temporal.append(torch.zeros(self.embed_dim, dtype=dtype, device=device))
+                else:
+                    pos_embed_temporal.append(self.temporal_pos_embed[temporal_ids_flatten[i]].to(dtype)) # D
+
+            pos_embed_2d.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype))  # patches * D
+            key_padding_mask[i, patch_len[i]:] = True
+
+        pos_embed_2d = torch.nn.utils.rnn.pad_sequence(
+            pos_embed_2d, batch_first=True, padding_value=0.0).permute(1, 0, 2)  # BLD => L * B * D
+        
+        v = x
+        k = x + pos_embed_2d
+        
+        if self.batch_infer:
+            out = self.batch_attn_forward(q, k, v, pos_embed_temporal, temporal_ids, key_padding_mask)
+        else: # save gpu memory
+            out = self.foreach_attn_forward(q, k, v, pos_embed_temporal, temporal_ids, key_padding_mask)
+        
+        #  out: Q * B * D
+        x = out.permute(1, 0, 2)  # B * Q * D
+
+        x = self.ln_post(x)
+        x = x @ self.proj
+        return x
+
+
+    def _repeat(self, query, N: int):
+        return query.unsqueeze(1).repeat(1, N, 1)
+    
+
+    def batch_attn_forward(self, q, k, v, pos_embed_temporal, temporal_ids, key_padding_mask):
+        bs = k.shape[1]
+
+        if pos_embed_temporal:
+            # temporal 维度折叠
+            # 时序 embedding
+            k += torch.stack(pos_embed_temporal, dim=0)
+            bs = len(temporal_ids)
+            merge_k = []
+            merge_v = []
+            merge_key_padding_mask = []
+
+            start = 0
+            for tp in temporal_ids:
+                end = start + len(tp)
+                # # L * (end-start) * D -> (end-start) * L * D -> 1 * L*(end-start) * D
+                merge_k.append(k[:, start: end, :].permute(1, 0, 2).reshape(-1, self.embed_dim))
+                merge_v.append(v[:, start: end, :].permute(1, 0, 2).reshape(-1, self.embed_dim))
+                merge_key_padding_mask.append(key_padding_mask[start: end, :].reshape(-1, 1))
+
+                start = end
+                            
+            k = torch.nn.utils.rnn.pad_sequence(merge_k, batch_first=True, padding_value=0.0).permute(1, 0, 2)  # L*(end-start)
+            v = torch.nn.utils.rnn.pad_sequence(merge_v, batch_first=True, padding_value=0.0).permute(1, 0, 2)  # L*(end-start)
+            key_padding_mask = torch.nn.utils.rnn.pad_sequence(merge_key_padding_mask, batch_first=True, padding_value=True).squeeze(-1)
+
+        out = self.attn(
+            self._repeat(q, bs),  # Q * B * D
+            k,  # L * B * D +  L * B * D
+            v,
+            key_padding_mask=key_padding_mask)[0]
+
+        return out
+
+
+    def foreach_attn_forward(self, q, k, v, pos_embed_temporal, temporal_ids, key_padding_mask):
+        bs = k.shape[1]
+
+        if pos_embed_temporal:
+            k += torch.stack(pos_embed_temporal, dim=0)
+            # bs = len(temporal_ids)
+            out_list = []
+
+            start = 0
+            for tp in temporal_ids:
+                end = start + len(tp)
+                # 处理每个序列而不padding
+                curr_k = k[:, start:end, :].reshape(-1, self.embed_dim)
+                curr_v = v[:, start:end, :].reshape(-1, self.embed_dim)
+                curr_key_padding_mask = key_padding_mask[start: end, :].reshape(-1)
+                curr_out = self.attn(
+                    q,
+                    curr_k,
+                    curr_v,
+                    key_padding_mask=curr_key_padding_mask,
+                )[0]
+
+                out_list.append(curr_out)
+                start = end
+
+            # 合并所有序列的结果
+            out = torch.stack(out_list, dim=1)
+
+        else:
+            out = self.attn(
+                self._repeat(q, bs),  # Q * B * D
+                k,  # L * B * D +  L * B * D
+                v,
+                key_padding_mask=key_padding_mask)[0]
+            
+        return out

special_tokens_map.json

@@ -0,0 +1,578 @@
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+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|reserved_62|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_minicpmv_fast.py

@@ -0,0 +1,66 @@
+from transformers import Qwen2TokenizerFast
+
+
+class MiniCPMVTokenizerFast(Qwen2TokenizerFast):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.im_start = "<image>"
+        self.im_end = "</image>"
+        self.ref_start = "<ref>"
+        self.ref_end = "</ref>"
+        self.box_start = "<box>"
+        self.box_end = "</box>"
+        self.quad_start = "<quad>"
+        self.quad_end = "</quad>"
+        self.slice_start = "<slice>"
+        self.slice_end = "</slice>"
+        self.im_id_start = "<image_id>"
+        self.im_id_end = "</image_id>"
+
+    @property
+    def eos_id(self):
+        return self.eos_token_id
+
+    @property
+    def bos_id(self):
+        return self.bos_token_id
+
+    @property
+    def unk_id(self):
+        return self.unk_token_id
+
+    @property
+    def im_start_id(self):
+        return self.convert_tokens_to_ids(self.im_start)
+
+    @property
+    def im_end_id(self):
+        return self.convert_tokens_to_ids(self.im_end)
+
+    @property
+    def slice_start_id(self):
+        return self.convert_tokens_to_ids(self.slice_start)
+    
+    @property
+    def slice_end_id(self):
+        return self.convert_tokens_to_ids(self.slice_end)
+
+    @property
+    def im_id_start_id(self):
+        return self.convert_tokens_to_ids(self.im_id_start)
+    
+    @property
+    def im_id_end_id(self):
+        return self.convert_tokens_to_ids(self.im_id_end)
+    
+    @property
+    def newline_id(self):
+        return self.convert_tokens_to_ids('\n')
+
+    @staticmethod
+    def escape(text: str) -> str:
+        return text
+
+    @staticmethod
+    def unescape(text: str) -> str:
+        return text

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a94a2c3913b8aa2175fffb5fd6cf4301958f323d06475bfd91037c13bdd74b
+size 11437868

tokenizer_config.json

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+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "128244": {
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151734": {
+      "content": "<|reserved_49|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151735": {
+      "content": "<|reserved_50|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151736": {
+      "content": "<|reserved_51|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151737": {
+      "content": "<|reserved_52|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151738": {
+      "content": "<|reserved_53|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151739": {
+      "content": "<|reserved_54|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151740": {
+      "content": "<|reserved_55|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151741": {
+      "content": "<|reserved_56|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151742": {
+      "content": "<|reserved_57|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151743": {
+      "content": "<|reserved_58|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151744": {
+      "content": "<|reserved_59|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151745": {
+      "content": "<|reserved_60|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151746": {
+      "content": "<|reserved_61|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151747": {
+      "content": "<|reserved_62|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<unk>",
+    "<image>",
+    "</image>",
+    "<ref>",
+    "</ref>",
+    "<box>",
+    "</box>",
+    "<quad>",
+    "</quad>",
+    "<point>",
+    "</point>",
+    "<slice>",
+    "</slice>",
+    "<image_id>",
+    "</image_id>",
+    "<unit>",
+    "</unit>",
+    "<|reserved_0|>",
+    "<|reserved_1|>",
+    "<|reserved_2|>",
+    "<|reserved_3|>",
+    "<|reserved_4|>",
+    "<|reserved_5|>",
+    "<|reserved_6|>",
+    "<|reserved_7|>",
+    "<|reserved_8|>",
+    "<|reserved_9|>",
+    "<|reserved_10|>",
+    "<|reserved_11|>",
+    "<|reserved_12|>",
+    "<|reserved_13|>",
+    "<|reserved_14|>",
+    "<|reserved_15|>",
+    "<|reserved_16|>",
+    "<|reserved_17|>",
+    "<|reserved_18|>",
+    "<|reserved_19|>",
+    "<|reserved_20|>",
+    "<|reserved_21|>",
+    "<|reserved_22|>",
+    "<|reserved_23|>",
+    "<|reserved_24|>",
+    "<|reserved_25|>",
+    "<|reserved_26|>",
+    "<|reserved_27|>",
+    "<|reserved_28|>",
+    "<|reserved_29|>",
+    "<|reserved_30|>",
+    "<|reserved_31|>",
+    "<|reserved_32|>",
+    "<|reserved_33|>",
+    "<|reserved_34|>",
+    "<|reserved_35|>",
+    "<|reserved_36|>",
+    "<|reserved_37|>",
+    "<|reserved_38|>",
+    "<|reserved_39|>",
+    "<|reserved_40|>",
+    "<|reserved_41|>",
+    "<|reserved_42|>",
+    "<|reserved_43|>",
+    "<|reserved_44|>",
+    "<|reserved_45|>",
+    "<|reserved_46|>",
+    "<|reserved_47|>",
+    "<|reserved_48|>",
+    "<|reserved_49|>",
+    "<|reserved_50|>",
+    "<|reserved_51|>",
+    "<|reserved_52|>",
+    "<|reserved_53|>",
+    "<|reserved_54|>",
+    "<|reserved_55|>",
+    "<|reserved_56|>",
+    "<|reserved_57|>",
+    "<|reserved_58|>",
+    "<|reserved_59|>",
+    "<|reserved_60|>",
+    "<|reserved_61|>",
+    "<|reserved_62|>"
+  ],
+  "bos_token": "<|im_start|>",
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n    {%- if enable_thinking is defined and enable_thinking is true %}\n        {{- '<think>\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "unk_token": "<unk>",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_minicpmv_fast.MiniCPMVTokenizerFast",
+      null
+    ]
+  },
+  "tokenizer_class": "MiniCPMVTokenizerFast"
+}