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Model: zpeng1989/Chinese_Food_Qwen25vl_3B_Model Source: Original Platform
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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SYSTEM """You are a helpful assistant."""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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README.md
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README.md
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# 中餐多模态识别大模型
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## 项目概述
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本项目旨在构建一食品识别&食品组成解析的多模态大模型，基于`qwen-vl2.5-3B`模型作为基座模型，使用LoRA（Low-Rank Adaptation）技术进行微调。模型训练数据使用中外食物及主要食品组成描述等多模态信息，并通过问答题目进行微调，完成模型对于食品及组成的解析能力。
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## 模型架构
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- **基座模型**: `qwen-vl2.5-3B`
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- **微调方法**: LoRA（Low-Rank Adaptation）
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- **训练数据**:
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- 餐品与对应食材内容解析的对应数据；
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## 数据集
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### 数据来源
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1. **中餐图像**: 相关食品照片及对应食材的主要成份解析结果；
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2. **外餐图像**: 相关食品照片及对应食材的主要成份解析结果;
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### 数据预处理
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- **图像处理**: 对食物图像进行标准化处理（如尺寸调整、归一化等）。
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- **文本处理**: 对问答对话进行清洗和标注，确保问题和答案的准确性。
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- **多模态对齐**: 将图像与对应的文本信息进行对齐，构建多模态训练样本。
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- **数据分割**: 将数据集分为训练集、验证集和测试集。
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## 模型训练
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### 微调方法
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使用LoRA技术对`qwen-vl2.5`模型进行微调。LoRA通过在预训练模型的权重矩阵中引入低秩矩阵来减少参数量，从而在保持模型性能的同时降低计算成本。
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### 训练步骤
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1. **加载预训练模型**: 加载`qwen-vl2.5`模型。
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2. **应用LoRA**: 在模型的关键层应用LoRA技术。
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3. **训练模型**: 使用准备好的多模态数据集进行微调。
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4. **验证与测试**: 在验证集和测试集上评估模型性能，调整超参数以优化结果。
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## 模型评估
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### 评估指标
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- **准确率**: 模型在问答任务中的准确率。
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- **召回率**: 模型能够正确回答的问题比例。
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- **F1分数**: 准确率和召回率的调和平均数。
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- **多模态理解能力**: 模型对图像和文本信息的综合理解能力。
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### 评估结果
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在测试集上的评估结果如下：
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待更新
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## 使用指南
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### 环境配置
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1. **Python版本**: 3.10+
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2. **依赖库**:
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- `transformers`
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- `torch`
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- `modelscope`
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### 下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('zpeng1989/Chinese_Food_Qwen25vl_3B_Model')
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```
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Git下载
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```
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#Git模型下载
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git clone https://oauth2:eDTzbKYiKrNCswNiDx1s@www.modelscope.cn/zpeng1989/Chinese_Food_Qwen25vl_3B_Model.git
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```
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### 模型推理
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以下是一个使用案例，展示如何加载模型并进行推理：
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# default: Load the model on the available device(s)
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained("qwen25_3b_vl_lora_sft_v20250511", torch_dtype="auto", device_map="auto"
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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# "Qwen/Qwen2.5-VL-7B-Instruct",
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# torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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# default processor
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#export_dir: /data/cgu/Model_Train/Model/qwen25_3b_vl_lora_sft_v20250511
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processor = AutoProcessor.from_pretrained("qwen25_3b_vl_lora_sft_v20250511")
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# The default range for the number of visual tokens per image in the model is 4-16384.
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# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
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# min_pixels = 256*28*28
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# max_pixels = 1280*28*28
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# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "*.jpg",
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},
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{"type": "text", "text": "图上有什么？"},
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],
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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## 案例
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### Case 01
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输入问题：
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```
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messages = [
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{
|
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"role": "user",
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"content": [
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{
|
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"type": "image",
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"image": "A.jpg",
|
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},
|
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{"type": "text", "text": "图上有什么？"},
|
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],
|
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}
|
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]
|
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```
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输出结果：
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```
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{
|
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"食物名称": {
|
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"炒菜": {
|
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"食品组成": [
|
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"鸡肉",
|
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"蘑菇",
|
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"青豆",
|
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"胡萝卜",
|
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"玉米",
|
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"辣椒"
|
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]
|
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}
|
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}
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}e
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```
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|
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### Case 02
|
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|
||||
|
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|
||||
|
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输入问题：
|
||||
```
|
||||
messages = [
|
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{
|
||||
"role": "user",
|
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"content": [
|
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{
|
||||
"type": "image",
|
||||
"image": "B.jpg",
|
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},
|
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{"type": "text", "text": "图上有什么？"},
|
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],
|
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}
|
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]
|
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```
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输出结果：
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|
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```
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{
|
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"食物名称": {
|
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"咖喱肉": {
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"食品组成": [
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"牛肉",
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"咖喱酱",
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"米饭"
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]
|
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}
|
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}
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}
|
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|
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```
|
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|
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### Case 03
|
||||
|
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|
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输入问题：
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|
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```
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messages = [
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{
|
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"role": "user",
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"content": [
|
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{
|
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"type": "image",
|
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"image": "E.png",
|
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},
|
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{"type": "text", "text": "图上有什么？"},
|
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],
|
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}
|
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]
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```
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输出结果：
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```
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{
|
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"食物名称": {
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"红油火锅": {
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"食品组成": [
|
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"红油",
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"辣椒",
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"花椒",
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"蔬菜",
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"肉类",
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"配菜"
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]
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},
|
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"蔬菜": {
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"食品组成": [
|
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"胡萝卜",
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"青菜"
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]
|
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},
|
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"肉类": {
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"食品组成": [
|
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"牛肉",
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"鸡肉",
|
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"猪肉"
|
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]
|
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},
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"配菜": {
|
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"食品组成": [
|
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"蘑菇",
|
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"香菇",
|
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"玉米"
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]
|
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}
|
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}
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}
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|
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```
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|
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## 致谢
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- 感谢 `qwen-vl2.5` 模型的开发者。
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||||
|
||||
## 联系方式
|
||||
如有任何问题，请联系 [592392714@qq.com]。
|
||||
|
||||
---
|
||||
|
||||
**注意**: 本项目仅供学术研究使用，不构成医疗建议。
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24
added_tokens.json
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added_tokens.json
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{
|
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"</tool_call>": 151658,
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"<tool_call>": 151657,
|
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"<|box_end|>": 151649,
|
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"<|box_start|>": 151648,
|
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"<|endoftext|>": 151643,
|
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"<|file_sep|>": 151664,
|
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"<|fim_middle|>": 151660,
|
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"<|fim_pad|>": 151662,
|
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"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
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"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
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"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
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"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
|
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}
|
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3
chat_template.json
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chat_template.json
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{
|
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
||||
}
|
||||
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config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"image_token_id": 151655,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
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"rope_theta": 1000000.0,
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|
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"visual.blocks.5.attn.qkv.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.5.mlp.down_proj.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.5.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.5.mlp.gate_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.5.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.5.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.5.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.5.norm1.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.5.norm2.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.6.attn.proj.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.attn.proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.attn.qkv.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.attn.qkv.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.mlp.down_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.6.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.mlp.gate_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.6.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.6.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.6.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.6.norm1.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.6.norm2.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.7.attn.proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.7.attn.proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.7.attn.qkv.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.7.attn.qkv.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.7.mlp.down_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.7.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.7.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.7.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.7.norm1.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.7.norm2.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.8.attn.proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.8.attn.proj.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.8.attn.qkv.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.8.attn.qkv.weight": "model-00001-of-00004.safetensors",
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"visual.blocks.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.8.mlp.gate_proj.bias": "model-00001-of-00004.safetensors",
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"visual.blocks.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.8.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.8.norm1.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.8.norm2.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.attn.proj.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.9.attn.proj.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.9.attn.qkv.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.9.attn.qkv.weight": "model-00001-of-00004.safetensors",
|
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"visual.blocks.9.mlp.down_proj.bias": "model-00001-of-00004.safetensors",
|
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"visual.blocks.9.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.mlp.gate_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.mlp.up_proj.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.norm1.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.blocks.9.norm2.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.merger.ln_q.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.merger.mlp.0.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.merger.mlp.0.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.merger.mlp.2.bias": "model-00001-of-00004.safetensors",
|
||||
"visual.merger.mlp.2.weight": "model-00001-of-00004.safetensors",
|
||||
"visual.patch_embed.proj.weight": "model-00001-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_processor_type": "Qwen2VLImageProcessor",
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"max_pixels": 12845056,
|
||||
"merge_size": 2,
|
||||
"min_pixels": 3136,
|
||||
"patch_size": 14,
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"longest_edge": 12845056,
|
||||
"shortest_edge": 3136
|
||||
},
|
||||
"temporal_patch_size": 2
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"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
|
||||
}
|
||||
}
|
||||
757444
tokenizer.json
Normal file
757444
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
210
tokenizer_config.json
Normal file
210
tokenizer_config.json
Normal file
@@ -0,0 +1,210 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user