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- MiniCPM-V-2_6-rk3588-w8a8-opt-0-hybrid-ratio-0.0.rkllm +3 -0
- MiniCPM-V-2_6-rk3588-w8a8-opt-0-hybrid-ratio-0.5.rkllm +3 -0
- MiniCPM-V-2_6-rk3588-w8a8-opt-1-hybrid-ratio-0.0.rkllm +3 -0
- MiniCPM-V-2_6-rk3588-w8a8-opt-1-hybrid-ratio-0.5.rkllm +3 -0
- README.md +365 -0
- added_tokens.json +25 -0
- config.json +54 -0
- generation_config.json +6 -0
- model.safetensors.index.json +796 -0
- preprocessor_config.json +24 -0
- special_tokens_map.json +172 -0
- tokenizer.json +0 -0
- tokenizer_config.json +235 -0
- vocab.json +0 -0
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| 1 |
+
---
|
| 2 |
+
datasets:
|
| 3 |
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- openbmb/RLAIF-V-Dataset
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| 4 |
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language:
|
| 5 |
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- multilingual
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| 6 |
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library_name: transformers
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| 7 |
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pipeline_tag: image-text-to-text
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| 8 |
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tags:
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| 9 |
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- minicpm-v
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| 10 |
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- vision
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| 11 |
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- ocr
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| 12 |
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- multi-image
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| 13 |
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- video
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| 14 |
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- custom_code
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| 15 |
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---
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| 16 |
+
# MiniCPM-V-2_6-RK3588-1.1.1
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| 17 |
+
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| 18 |
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This version of MiniCPM-V-2_6 has been converted to run on the RK3588 NPU using {'w8a8'} quantization.
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| 19 |
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| 20 |
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This model has been optimized with the following LoRA:
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| 21 |
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| 22 |
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Compatible with RKLLM version: 1.1.1
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| 23 |
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| 24 |
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###Useful links:
|
| 25 |
+
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm)
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| 26 |
+
|
| 27 |
+
[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU)
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| 28 |
+
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| 29 |
+
[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/)
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| 30 |
+
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| 31 |
+
Pretty much anything by these folks: (marty1885)[https://github.com/marty1885] and [happyme531](https://huggingface.co/happyme531)
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| 32 |
+
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| 33 |
+
# Original Model Card for base model, MiniCPM-V-2_6, below:
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| 34 |
+
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| 35 |
+
|
| 36 |
+
<h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
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| 37 |
+
|
| 38 |
+
[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](http://120.92.209.146:8887/)</a>
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| 39 |
+
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| 40 |
+
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| 41 |
+
## MiniCPM-V 2.6
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| 42 |
+
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| 43 |
+
**MiniCPM-V 2.6** is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:
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| 44 |
+
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| 45 |
+
- 🔥 **Leading Performance.**
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| 46 |
+
MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet** for single image understanding.
|
| 47 |
+
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| 48 |
+
- 🖼️ **Multi Image Understanding and In-context Learning.** MiniCPM-V 2.6 can also perform **conversation and reasoning over multiple images**. It achieves **state-of-the-art performance** on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability.
|
| 49 |
+
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| 50 |
+
- 🎬 **Video Understanding.** MiniCPM-V 2.6 can also **accept video inputs**, performing conversation and providing dense captions for spatial-temporal information. It outperforms **GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B** on Video-MME with/without subtitles.
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| 51 |
+
|
| 52 |
+
- 💪 **Strong OCR Capability and Others.**
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| 53 |
+
MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves **state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro**.
|
| 54 |
+
Based on the the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports **multilingual capabilities** on English, Chinese, German, French, Italian, Korean, etc.
|
| 55 |
+
|
| 56 |
+
- 🚀 **Superior Efficiency.**
|
| 57 |
+
In addition to its friendly size, MiniCPM-V 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support **real-time video understanding** on end-side devices such as iPad.
|
| 58 |
+
|
| 59 |
+
- 💫 **Easy Usage.**
|
| 60 |
+
MiniCPM-V 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.6) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](https://github.com/OpenBMB/MiniCPM-V/tree/main?tab=readme-ov-file#inference-with-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/tree/main?tab=readme-ov-file#chat-with-our-demo-on-gradio) and (6) online web [demo](http://120.92.209.146:8887).
|
| 61 |
+
|
| 62 |
+
### Evaluation <!-- omit in toc -->
|
| 63 |
+
<div align="center">
|
| 64 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/radar_final.png" width=66% />
|
| 65 |
+
</div>
|
| 66 |
+
|
| 67 |
+
Single image results on OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench:
|
| 68 |
+
<div align="center">
|
| 69 |
+
|
| 70 |
+
|
| 71 |
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|
| 72 |
+
</div>
|
| 73 |
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|
| 74 |
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<sup>*</sup> We evaluate this benchmark using chain-of-thought prompting.
|
| 75 |
+
|
| 76 |
+
<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens.
|
| 77 |
+
|
| 78 |
+
Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation.
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
<details>
|
| 82 |
+
<summary>Click to view multi-image results on Mantis Eval, BLINK Val, Mathverse mv, Sciverse mv, MIRB.</summary>
|
| 83 |
+
<div align="center">
|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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</div>
|
| 88 |
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<sup>*</sup> We evaluate the officially released checkpoint by ourselves.
|
| 89 |
+
</details>
|
| 90 |
+
|
| 91 |
+
<details>
|
| 92 |
+
<summary>Click to view video results on Video-MME and Video-ChatGPT.</summary>
|
| 93 |
+
<div align="center">
|
| 94 |
+
|
| 95 |
+
<!--  -->
|
| 96 |
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| 97 |
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| 98 |
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</div>
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| 99 |
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| 100 |
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</details>
|
| 101 |
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|
| 102 |
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|
| 103 |
+
<details>
|
| 104 |
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<summary>Click to view few-shot results on TextVQA, VizWiz, VQAv2, OK-VQA.</summary>
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| 105 |
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<div align="center">
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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</div>
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| 111 |
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* denotes zero image shot and two additional text shots following Flamingo.
|
| 112 |
+
|
| 113 |
+
<sup>+</sup> We evaluate the pretraining ckpt without SFT.
|
| 114 |
+
</details>
|
| 115 |
+
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| 116 |
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### Examples <!-- omit in toc -->
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| 117 |
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| 118 |
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<div style="display: flex; flex-direction: column; align-items: center;">
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| 119 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-bike.png" alt="Bike" style="margin-bottom: -20px;">
|
| 120 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-menu.png" alt="Menu" style="margin-bottom: -20px;">
|
| 121 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-code.png" alt="Code" style="margin-bottom: -20px;">
|
| 122 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-Mem.png" alt="Mem" style="margin-bottom: -20px;">
|
| 123 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-medal.png" alt="medal" style="margin-bottom: 10px;">
|
| 124 |
+
</div>
|
| 125 |
+
<details>
|
| 126 |
+
<summary>Click to view more cases.</summary>
|
| 127 |
+
<div style="display: flex; flex-direction: column; align-items: center;">
|
| 128 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-elec.png" alt="elec" style="margin-bottom: -20px;">
|
| 129 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-olympic.png" alt="Menu" style="margin-bottom: 10px;">
|
| 130 |
+
</div>
|
| 131 |
+
</details>
|
| 132 |
+
|
| 133 |
+
We deploy MiniCPM-V 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition.
|
| 134 |
+
|
| 135 |
+
<div style="display: flex; justify-content: center;">
|
| 136 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ai.gif" width="48%" style="margin: 0 10px;"/>
|
| 137 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/beer.gif" width="48%" style="margin: 0 10px;"/>
|
| 138 |
+
</div>
|
| 139 |
+
<div style="display: flex; justify-content: center; margin-top: 20px;">
|
| 140 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ticket.gif" width="48%" style="margin: 0 10px;"/>
|
| 141 |
+
<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/wfh.gif" width="48%" style="margin: 0 10px;"/>
|
| 142 |
+
</div>
|
| 143 |
+
|
| 144 |
+
<div style="text-align: center;">
|
| 145 |
+
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/mXAEFQFqNd4nnvPk7r5eX.mp4"></video>
|
| 146 |
+
<!-- <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/fEWzfHUdKnpkM7sdmnBQa.mp4"></video> -->
|
| 147 |
+
|
| 148 |
+
</div>
|
| 149 |
+
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
## Demo
|
| 153 |
+
Click here to try the Demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/).
|
| 154 |
+
|
| 155 |
+
|
| 156 |
+
## Usage
|
| 157 |
+
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
|
| 158 |
+
```
|
| 159 |
+
Pillow==10.1.0
|
| 160 |
+
torch==2.1.2
|
| 161 |
+
torchvision==0.16.2
|
| 162 |
+
transformers==4.40.0
|
| 163 |
+
sentencepiece==0.1.99
|
| 164 |
+
decord
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
```python
|
| 168 |
+
# test.py
|
| 169 |
+
import torch
|
| 170 |
+
from PIL import Image
|
| 171 |
+
from transformers import AutoModel, AutoTokenizer
|
| 172 |
+
|
| 173 |
+
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
| 174 |
+
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
| 175 |
+
model = model.eval().cuda()
|
| 176 |
+
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
| 177 |
+
|
| 178 |
+
image = Image.open('xx.jpg').convert('RGB')
|
| 179 |
+
question = 'What is in the image?'
|
| 180 |
+
msgs = [{'role': 'user', 'content': [image, question]}]
|
| 181 |
+
|
| 182 |
+
res = model.chat(
|
| 183 |
+
image=None,
|
| 184 |
+
msgs=msgs,
|
| 185 |
+
tokenizer=tokenizer
|
| 186 |
+
)
|
| 187 |
+
print(res)
|
| 188 |
+
|
| 189 |
+
## if you want to use streaming, please make sure sampling=True and stream=True
|
| 190 |
+
## the model.chat will return a generator
|
| 191 |
+
res = model.chat(
|
| 192 |
+
image=None,
|
| 193 |
+
msgs=msgs,
|
| 194 |
+
tokenizer=tokenizer,
|
| 195 |
+
sampling=True,
|
| 196 |
+
stream=True
|
| 197 |
+
)
|
| 198 |
+
|
| 199 |
+
generated_text = ""
|
| 200 |
+
for new_text in res:
|
| 201 |
+
generated_text += new_text
|
| 202 |
+
print(new_text, flush=True, end='')
|
| 203 |
+
```
|
| 204 |
+
|
| 205 |
+
### Chat with multiple images
|
| 206 |
+
<details>
|
| 207 |
+
<summary> Click to show Python code running MiniCPM-V 2.6 with multiple images input. </summary>
|
| 208 |
+
|
| 209 |
+
```python
|
| 210 |
+
import torch
|
| 211 |
+
from PIL import Image
|
| 212 |
+
from transformers import AutoModel, AutoTokenizer
|
| 213 |
+
|
| 214 |
+
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
| 215 |
+
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
| 216 |
+
model = model.eval().cuda()
|
| 217 |
+
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
| 218 |
+
|
| 219 |
+
image1 = Image.open('image1.jpg').convert('RGB')
|
| 220 |
+
image2 = Image.open('image2.jpg').convert('RGB')
|
| 221 |
+
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
|
| 222 |
+
|
| 223 |
+
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
|
| 224 |
+
|
| 225 |
+
answer = model.chat(
|
| 226 |
+
image=None,
|
| 227 |
+
msgs=msgs,
|
| 228 |
+
tokenizer=tokenizer
|
| 229 |
+
)
|
| 230 |
+
print(answer)
|
| 231 |
+
```
|
| 232 |
+
</details>
|
| 233 |
+
|
| 234 |
+
### In-context few-shot learning
|
| 235 |
+
<details>
|
| 236 |
+
<summary> Click to view Python code running MiniCPM-V 2.6 with few-shot input. </summary>
|
| 237 |
+
|
| 238 |
+
```python
|
| 239 |
+
import torch
|
| 240 |
+
from PIL import Image
|
| 241 |
+
from transformers import AutoModel, AutoTokenizer
|
| 242 |
+
|
| 243 |
+
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
| 244 |
+
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
| 245 |
+
model = model.eval().cuda()
|
| 246 |
+
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
| 247 |
+
|
| 248 |
+
question = "production date"
|
| 249 |
+
image1 = Image.open('example1.jpg').convert('RGB')
|
| 250 |
+
answer1 = "2023.08.04"
|
| 251 |
+
image2 = Image.open('example2.jpg').convert('RGB')
|
| 252 |
+
answer2 = "2007.04.24"
|
| 253 |
+
image_test = Image.open('test.jpg').convert('RGB')
|
| 254 |
+
|
| 255 |
+
msgs = [
|
| 256 |
+
{'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
|
| 257 |
+
{'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
|
| 258 |
+
{'role': 'user', 'content': [image_test, question]}
|
| 259 |
+
]
|
| 260 |
+
|
| 261 |
+
answer = model.chat(
|
| 262 |
+
image=None,
|
| 263 |
+
msgs=msgs,
|
| 264 |
+
tokenizer=tokenizer
|
| 265 |
+
)
|
| 266 |
+
print(answer)
|
| 267 |
+
```
|
| 268 |
+
</details>
|
| 269 |
+
|
| 270 |
+
### Chat with video
|
| 271 |
+
<details>
|
| 272 |
+
<summary> Click to view Python code running MiniCPM-V 2.6 with video input. </summary>
|
| 273 |
+
|
| 274 |
+
```python
|
| 275 |
+
import torch
|
| 276 |
+
from PIL import Image
|
| 277 |
+
from transformers import AutoModel, AutoTokenizer
|
| 278 |
+
from decord import VideoReader, cpu # pip install decord
|
| 279 |
+
|
| 280 |
+
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
|
| 281 |
+
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
|
| 282 |
+
model = model.eval().cuda()
|
| 283 |
+
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
|
| 284 |
+
|
| 285 |
+
MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number
|
| 286 |
+
|
| 287 |
+
def encode_video(video_path):
|
| 288 |
+
def uniform_sample(l, n):
|
| 289 |
+
gap = len(l) / n
|
| 290 |
+
idxs = [int(i * gap + gap / 2) for i in range(n)]
|
| 291 |
+
return [l[i] for i in idxs]
|
| 292 |
+
|
| 293 |
+
vr = VideoReader(video_path, ctx=cpu(0))
|
| 294 |
+
sample_fps = round(vr.get_avg_fps() / 1) # FPS
|
| 295 |
+
frame_idx = [i for i in range(0, len(vr), sample_fps)]
|
| 296 |
+
if len(frame_idx) > MAX_NUM_FRAMES:
|
| 297 |
+
frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
|
| 298 |
+
frames = vr.get_batch(frame_idx).asnumpy()
|
| 299 |
+
frames = [Image.fromarray(v.astype('uint8')) for v in frames]
|
| 300 |
+
print('num frames:', len(frames))
|
| 301 |
+
return frames
|
| 302 |
+
|
| 303 |
+
video_path ="video_test.mp4"
|
| 304 |
+
frames = encode_video(video_path)
|
| 305 |
+
question = "Describe the video"
|
| 306 |
+
msgs = [
|
| 307 |
+
{'role': 'user', 'content': frames + [question]},
|
| 308 |
+
]
|
| 309 |
+
|
| 310 |
+
# Set decode params for video
|
| 311 |
+
params={}
|
| 312 |
+
params["use_image_id"] = False
|
| 313 |
+
params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution > 448*448
|
| 314 |
+
|
| 315 |
+
answer = model.chat(
|
| 316 |
+
image=None,
|
| 317 |
+
msgs=msgs,
|
| 318 |
+
tokenizer=tokenizer,
|
| 319 |
+
**params
|
| 320 |
+
)
|
| 321 |
+
print(answer)
|
| 322 |
+
```
|
| 323 |
+
</details>
|
| 324 |
+
|
| 325 |
+
|
| 326 |
+
Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage.
|
| 327 |
+
|
| 328 |
+
|
| 329 |
+
## Inference with llama.cpp<a id="llamacpp"></a>
|
| 330 |
+
MiniCPM-V 2.6 can run with llama.cpp. See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail.
|
| 331 |
+
|
| 332 |
+
|
| 333 |
+
## Int4 quantized version
|
| 334 |
+
Download the int4 quantized version for lower GPU memory (7GB) usage: [MiniCPM-V-2_6-int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4).
|
| 335 |
+
|
| 336 |
+
|
| 337 |
+
## License
|
| 338 |
+
#### Model License
|
| 339 |
+
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
|
| 340 |
+
* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
|
| 341 |
+
* The models and weights of MiniCPM are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-V 2.6 weights are also available for free commercial use.
|
| 342 |
+
|
| 343 |
+
|
| 344 |
+
#### Statement
|
| 345 |
+
* As an LMM, MiniCPM-V 2.6 generates contents by learning a large mount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 2.6 does not represent the views and positions of the model developers
|
| 346 |
+
* 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.
|
| 347 |
+
|
| 348 |
+
## Key Techniques and Other Multimodal Projects
|
| 349 |
+
|
| 350 |
+
👏 Welcome to explore key techniques of MiniCPM-V 2.6 and other multimodal projects of our team:
|
| 351 |
+
|
| 352 |
+
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [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)
|
| 353 |
+
|
| 354 |
+
## Citation
|
| 355 |
+
|
| 356 |
+
If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
|
| 357 |
+
|
| 358 |
+
```bib
|
| 359 |
+
@article{yao2024minicpm,
|
| 360 |
+
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
|
| 361 |
+
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},
|
| 362 |
+
journal={arXiv preprint arXiv:2408.01800},
|
| 363 |
+
year={2024}
|
| 364 |
+
}
|
| 365 |
+
```
|
added_tokens.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"</box>": 151651,
|
| 3 |
+
"</image>": 151647,
|
| 4 |
+
"</image_id>": 151659,
|
| 5 |
+
"</point>": 151655,
|
| 6 |
+
"</quad>": 151653,
|
| 7 |
+
"</ref>": 151649,
|
| 8 |
+
"</slice>": 151657,
|
| 9 |
+
"<box>": 151650,
|
| 10 |
+
"<image>": 151646,
|
| 11 |
+
"<image_id>": 151658,
|
| 12 |
+
"<point>": 151654,
|
| 13 |
+
"<quad>": 151652,
|
| 14 |
+
"<ref>": 151648,
|
| 15 |
+
"<slice>": 151656,
|
| 16 |
+
"<|endoftext|>": 151643,
|
| 17 |
+
"<|im_end|>": 151645,
|
| 18 |
+
"<|im_start|>": 151644,
|
| 19 |
+
"<|reserved_special_token_0|>": 151660,
|
| 20 |
+
"<|reserved_special_token_1|>": 151661,
|
| 21 |
+
"<|reserved_special_token_2|>": 151662,
|
| 22 |
+
"<|reserved_special_token_3|>": 151663,
|
| 23 |
+
"<|reserved_special_token_4|>": 151664,
|
| 24 |
+
"<|reserved_special_token_5|>": 151665
|
| 25 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "openbmb/MiniCPM-V-2_6",
|
| 3 |
+
"version": 2.6,
|
| 4 |
+
"architectures": [
|
| 5 |
+
"MiniCPMV"
|
| 6 |
+
],
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_minicpm.MiniCPMVConfig",
|
| 9 |
+
"AutoModel": "modeling_minicpmv.MiniCPMV",
|
| 10 |
+
"AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
|
| 11 |
+
},
|
| 12 |
+
"attention_dropout": 0.0,
|
| 13 |
+
"bos_token_id": 151643,
|
| 14 |
+
"eos_token_id": 151645,
|
| 15 |
+
"hidden_act": "silu",
|
| 16 |
+
"hidden_size": 3584,
|
| 17 |
+
"initializer_range": 0.02,
|
| 18 |
+
"intermediate_size": 18944,
|
| 19 |
+
"max_position_embeddings": 32768,
|
| 20 |
+
"max_window_layers": 28,
|
| 21 |
+
"num_attention_heads": 28,
|
| 22 |
+
"num_hidden_layers": 28,
|
| 23 |
+
"num_key_value_heads": 4,
|
| 24 |
+
"rms_norm_eps": 1e-06,
|
| 25 |
+
"rope_theta": 1000000.0,
|
| 26 |
+
"sliding_window": 131072,
|
| 27 |
+
"tie_word_embeddings": false,
|
| 28 |
+
"torch_dtype": "bfloat16",
|
| 29 |
+
"transformers_version": "4.40.0",
|
| 30 |
+
"use_cache": true,
|
| 31 |
+
"use_sliding_window": false,
|
| 32 |
+
"vocab_size": 151666,
|
| 33 |
+
"batch_vision_input": true,
|
| 34 |
+
"drop_vision_last_layer": false,
|
| 35 |
+
"image_size": 448,
|
| 36 |
+
"model_type": "minicpmv",
|
| 37 |
+
"patch_size": 14,
|
| 38 |
+
"query_num": 64,
|
| 39 |
+
"slice_config": {
|
| 40 |
+
"max_slice_nums": 9,
|
| 41 |
+
"patch_size": 14,
|
| 42 |
+
"model_type": "minicpmv"
|
| 43 |
+
},
|
| 44 |
+
"slice_mode": true,
|
| 45 |
+
"vision_config": {
|
| 46 |
+
"hidden_size": 1152,
|
| 47 |
+
"image_size": 980,
|
| 48 |
+
"intermediate_size": 4304,
|
| 49 |
+
"model_type": "siglip",
|
| 50 |
+
"num_attention_heads": 16,
|
| 51 |
+
"num_hidden_layers": 27,
|
| 52 |
+
"patch_size": 14
|
| 53 |
+
}
|
| 54 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
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| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 151643,
|
| 4 |
+
"eos_token_id": 151645,
|
| 5 |
+
"transformers_version": "4.40.0"
|
| 6 |
+
}
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,796 @@
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|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 16198350304
|
| 4 |
+
},
|
| 5 |
+
"weight_map": {
|
| 6 |
+
"llm.lm_head.weight": "model-00004-of-00004.safetensors",
|
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|
| 795 |
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| 796 |
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preprocessor_config.json
ADDED
|
@@ -0,0 +1,24 @@
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|
| 1 |
+
{
|
| 2 |
+
"image_processor_type": "MiniCPMVImageProcessor",
|
| 3 |
+
"auto_map": {
|
| 4 |
+
"AutoProcessor": "processing_minicpmv.MiniCPMVProcessor",
|
| 5 |
+
"AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor"
|
| 6 |
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},
|
| 7 |
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"processor_class": "MiniCPMVProcessor",
|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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"image_feature_size": 64,
|
| 13 |
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"im_start": "<image>",
|
| 14 |
+
"im_end": "</image>",
|
| 15 |
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"slice_start": "<slice>",
|
| 16 |
+
"slice_end": "</slice>",
|
| 17 |
+
"unk": "<unk>",
|
| 18 |
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"im_id_start": "<image_id>",
|
| 19 |
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"im_id_end": "</image_id>",
|
| 20 |
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"slice_mode": true,
|
| 21 |
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"norm_mean": [0.5, 0.5, 0.5],
|
| 22 |
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"norm_std": [0.5, 0.5, 0.5],
|
| 23 |
+
"version": 2.6
|
| 24 |
+
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|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,172 @@
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|
| 1 |
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{
|
| 2 |
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"additional_special_tokens": [
|
| 3 |
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{
|
| 4 |
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"content": "<image>",
|
| 5 |
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"lstrip": false,
|
| 6 |
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"normalized": false,
|
| 7 |
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"rstrip": false,
|
| 8 |
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"single_word": false
|
| 9 |
+
},
|
| 10 |
+
{
|
| 11 |
+
"content": "</image>",
|
| 12 |
+
"lstrip": false,
|
| 13 |
+
"normalized": false,
|
| 14 |
+
"rstrip": false,
|
| 15 |
+
"single_word": false
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"content": "<ref>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
},
|
| 24 |
+
{
|
| 25 |
+
"content": "</ref>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
},
|
| 31 |
+
{
|
| 32 |
+
"content": "<box>",
|
| 33 |
+
"lstrip": false,
|
| 34 |
+
"normalized": false,
|
| 35 |
+
"rstrip": false,
|
| 36 |
+
"single_word": false
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"content": "</box>",
|
| 40 |
+
"lstrip": false,
|
| 41 |
+
"normalized": false,
|
| 42 |
+
"rstrip": false,
|
| 43 |
+
"single_word": false
|
| 44 |
+
},
|
| 45 |
+
{
|
| 46 |
+
"content": "<quad>",
|
| 47 |
+
"lstrip": false,
|
| 48 |
+
"normalized": false,
|
| 49 |
+
"rstrip": false,
|
| 50 |
+
"single_word": false
|
| 51 |
+
},
|
| 52 |
+
{
|
| 53 |
+
"content": "</quad>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"content": "<point>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"content": "</point>",
|
| 68 |
+
"lstrip": false,
|
| 69 |
+
"normalized": false,
|
| 70 |
+
"rstrip": false,
|
| 71 |
+
"single_word": false
|
| 72 |
+
},
|
| 73 |
+
{
|
| 74 |
+
"content": "<slice>",
|
| 75 |
+
"lstrip": false,
|
| 76 |
+
"normalized": false,
|
| 77 |
+
"rstrip": false,
|
| 78 |
+
"single_word": false
|
| 79 |
+
},
|
| 80 |
+
{
|
| 81 |
+
"content": "</slice>",
|
| 82 |
+
"lstrip": false,
|
| 83 |
+
"normalized": false,
|
| 84 |
+
"rstrip": false,
|
| 85 |
+
"single_word": false
|
| 86 |
+
},
|
| 87 |
+
{
|
| 88 |
+
"content": "<image_id>",
|
| 89 |
+
"lstrip": false,
|
| 90 |
+
"normalized": false,
|
| 91 |
+
"rstrip": false,
|
| 92 |
+
"single_word": false
|
| 93 |
+
},
|
| 94 |
+
{
|
| 95 |
+
"content": "</image_id>",
|
| 96 |
+
"lstrip": false,
|
| 97 |
+
"normalized": false,
|
| 98 |
+
"rstrip": false,
|
| 99 |
+
"single_word": false
|
| 100 |
+
},
|
| 101 |
+
{
|
| 102 |
+
"content": "<|reserved_special_token_0|>",
|
| 103 |
+
"lstrip": false,
|
| 104 |
+
"normalized": false,
|
| 105 |
+
"rstrip": false,
|
| 106 |
+
"single_word": false
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"content": "<|reserved_special_token_1|>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false
|
| 114 |
+
},
|
| 115 |
+
{
|
| 116 |
+
"content": "<|reserved_special_token_2|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false
|
| 121 |
+
},
|
| 122 |
+
{
|
| 123 |
+
"content": "<|reserved_special_token_3|>",
|
| 124 |
+
"lstrip": false,
|
| 125 |
+
"normalized": false,
|
| 126 |
+
"rstrip": false,
|
| 127 |
+
"single_word": false
|
| 128 |
+
},
|
| 129 |
+
{
|
| 130 |
+
"content": "<|reserved_special_token_4|>",
|
| 131 |
+
"lstrip": false,
|
| 132 |
+
"normalized": false,
|
| 133 |
+
"rstrip": false,
|
| 134 |
+
"single_word": false
|
| 135 |
+
},
|
| 136 |
+
{
|
| 137 |
+
"content": "<|reserved_special_token_5|>",
|
| 138 |
+
"lstrip": false,
|
| 139 |
+
"normalized": false,
|
| 140 |
+
"rstrip": false,
|
| 141 |
+
"single_word": false
|
| 142 |
+
}
|
| 143 |
+
],
|
| 144 |
+
"bos_token": {
|
| 145 |
+
"content": "<|im_start|>",
|
| 146 |
+
"lstrip": false,
|
| 147 |
+
"normalized": false,
|
| 148 |
+
"rstrip": false,
|
| 149 |
+
"single_word": false
|
| 150 |
+
},
|
| 151 |
+
"eos_token": {
|
| 152 |
+
"content": "<|im_end|>",
|
| 153 |
+
"lstrip": false,
|
| 154 |
+
"normalized": false,
|
| 155 |
+
"rstrip": false,
|
| 156 |
+
"single_word": false
|
| 157 |
+
},
|
| 158 |
+
"pad_token": {
|
| 159 |
+
"content": "<|endoftext|>",
|
| 160 |
+
"lstrip": false,
|
| 161 |
+
"normalized": false,
|
| 162 |
+
"rstrip": false,
|
| 163 |
+
"single_word": false
|
| 164 |
+
},
|
| 165 |
+
"unk_token": {
|
| 166 |
+
"content": "<unk>",
|
| 167 |
+
"lstrip": false,
|
| 168 |
+
"normalized": false,
|
| 169 |
+
"rstrip": false,
|
| 170 |
+
"single_word": false
|
| 171 |
+
}
|
| 172 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,235 @@
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"128244": {
|
| 5 |
+
"content": "<unk>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"151643": {
|
| 13 |
+
"content": "<|endoftext|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"151644": {
|
| 21 |
+
"content": "<|im_start|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"151645": {
|
| 29 |
+
"content": "<|im_end|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"151646": {
|
| 37 |
+
"content": "<image>",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
},
|
| 44 |
+
"151647": {
|
| 45 |
+
"content": "</image>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": true
|
| 51 |
+
},
|
| 52 |
+
"151648": {
|
| 53 |
+
"content": "<ref>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": true
|
| 59 |
+
},
|
| 60 |
+
"151649": {
|
| 61 |
+
"content": "</ref>",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": false,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": true
|
| 67 |
+
},
|
| 68 |
+
"151650": {
|
| 69 |
+
"content": "<box>",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": false,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": true
|
| 75 |
+
},
|
| 76 |
+
"151651": {
|
| 77 |
+
"content": "</box>",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": false,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": true
|
| 83 |
+
},
|
| 84 |
+
"151652": {
|
| 85 |
+
"content": "<quad>",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": false,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": true
|
| 91 |
+
},
|
| 92 |
+
"151653": {
|
| 93 |
+
"content": "</quad>",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": false,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": true
|
| 99 |
+
},
|
| 100 |
+
"151654": {
|
| 101 |
+
"content": "<point>",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": false,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": true
|
| 107 |
+
},
|
| 108 |
+
"151655": {
|
| 109 |
+
"content": "</point>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": true
|
| 115 |
+
},
|
| 116 |
+
"151656": {
|
| 117 |
+
"content": "<slice>",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": false,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": true
|
| 123 |
+
},
|
| 124 |
+
"151657": {
|
| 125 |
+
"content": "</slice>",
|
| 126 |
+
"lstrip": false,
|
| 127 |
+
"normalized": false,
|
| 128 |
+
"rstrip": false,
|
| 129 |
+
"single_word": false,
|
| 130 |
+
"special": true
|
| 131 |
+
},
|
| 132 |
+
"151658": {
|
| 133 |
+
"content": "<image_id>",
|
| 134 |
+
"lstrip": false,
|
| 135 |
+
"normalized": false,
|
| 136 |
+
"rstrip": false,
|
| 137 |
+
"single_word": false,
|
| 138 |
+
"special": true
|
| 139 |
+
},
|
| 140 |
+
"151659": {
|
| 141 |
+
"content": "</image_id>",
|
| 142 |
+
"lstrip": false,
|
| 143 |
+
"normalized": false,
|
| 144 |
+
"rstrip": false,
|
| 145 |
+
"single_word": false,
|
| 146 |
+
"special": true
|
| 147 |
+
},
|
| 148 |
+
"151660": {
|
| 149 |
+
"content": "<|reserved_special_token_0|>",
|
| 150 |
+
"lstrip": false,
|
| 151 |
+
"normalized": false,
|
| 152 |
+
"rstrip": false,
|
| 153 |
+
"single_word": false,
|
| 154 |
+
"special": true
|
| 155 |
+
},
|
| 156 |
+
"151661": {
|
| 157 |
+
"content": "<|reserved_special_token_1|>",
|
| 158 |
+
"lstrip": false,
|
| 159 |
+
"normalized": false,
|
| 160 |
+
"rstrip": false,
|
| 161 |
+
"single_word": false,
|
| 162 |
+
"special": true
|
| 163 |
+
},
|
| 164 |
+
"151662": {
|
| 165 |
+
"content": "<|reserved_special_token_2|>",
|
| 166 |
+
"lstrip": false,
|
| 167 |
+
"normalized": false,
|
| 168 |
+
"rstrip": false,
|
| 169 |
+
"single_word": false,
|
| 170 |
+
"special": true
|
| 171 |
+
},
|
| 172 |
+
"151663": {
|
| 173 |
+
"content": "<|reserved_special_token_3|>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"151664": {
|
| 181 |
+
"content": "<|reserved_special_token_4|>",
|
| 182 |
+
"lstrip": false,
|
| 183 |
+
"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": true
|
| 187 |
+
},
|
| 188 |
+
"151665": {
|
| 189 |
+
"content": "<|reserved_special_token_5|>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": true
|
| 195 |
+
}
|
| 196 |
+
},
|
| 197 |
+
"additional_special_tokens": [
|
| 198 |
+
"<image>",
|
| 199 |
+
"</image>",
|
| 200 |
+
"<ref>",
|
| 201 |
+
"</ref>",
|
| 202 |
+
"<box>",
|
| 203 |
+
"</box>",
|
| 204 |
+
"<quad>",
|
| 205 |
+
"</quad>",
|
| 206 |
+
"<point>",
|
| 207 |
+
"</point>",
|
| 208 |
+
"<slice>",
|
| 209 |
+
"</slice>",
|
| 210 |
+
"<image_id>",
|
| 211 |
+
"</image_id>",
|
| 212 |
+
"<|reserved_special_token_0|>",
|
| 213 |
+
"<|reserved_special_token_1|>",
|
| 214 |
+
"<|reserved_special_token_2|>",
|
| 215 |
+
"<|reserved_special_token_3|>",
|
| 216 |
+
"<|reserved_special_token_4|>",
|
| 217 |
+
"<|reserved_special_token_5|>"
|
| 218 |
+
],
|
| 219 |
+
"bos_token": "<|im_start|>",
|
| 220 |
+
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 221 |
+
"clean_up_tokenization_spaces": false,
|
| 222 |
+
"eos_token": "<|im_end|>",
|
| 223 |
+
"errors": "replace",
|
| 224 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 225 |
+
"pad_token": "<|endoftext|>",
|
| 226 |
+
"split_special_tokens": false,
|
| 227 |
+
"auto_map": {
|
| 228 |
+
"AutoTokenizer": [
|
| 229 |
+
"tokenization_minicpmv_fast.MiniCPMVTokenizerFast",
|
| 230 |
+
null
|
| 231 |
+
]
|
| 232 |
+
},
|
| 233 |
+
"tokenizer_class": "MiniCPMVTokenizerFast",
|
| 234 |
+
"unk_token": "<unk>"
|
| 235 |
+
}
|
vocab.json
ADDED
|
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