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README.md

+# EmoCaliber
+## 论文
+[EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration](https://arxiv.org/abs/2512.15528)
+## 模型简介
+EmoCaliber基于Qwen2.5-VL-7B实现，能够以相同的方式进行推理和训练。给定一张图像，EmoCaliber经过训练，能够按照以下流程生成结构化的情感推理：(1) 识别图像中显著的视觉元素；(2) 如果图像中有人物，则提供对人物的详细描述；(3) 描述除人物之外的上下文元素；(4) 探讨这些元素之间的相互作用；以及 (5) 基于前述观察得出情感结论。最终的情感预测整合了这些视觉线索。在输出预测结果后，EmoCaliber还会输出一个包裹在标签中的置信度分数，该分数反映了模型对其答案的自我评估确定性。
+
+<div align=center>
+    <img src="./doc/perf.jpg"/>
+</div>
+
+## 环境依赖
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 26.04 |
+| python | 3.10.12 |
+| torch | 2.9.0+das.opt1.dtk2604.20260206.g275d08c2 |
+| transformers | 5.0.0.dev0 |
+| vllm | 0.15.1+das.opt1.alpha.dtk2604.20260220.g2799735a |
+
+推荐使用镜像: harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 200g \
+    --network=host \
+    --name EmoCaliber \
+    --privileged \
+    --device=/dev/kfd \
+    --device=/dev/dri \
+    --device=/dev/mkfd \
+    --group-add video \
+    --cap-add=SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    -u root \
+    -v /opt/hyhal/:/opt/hyhal/:ro \
+    -v /path/your_code_data/:/path/your_code_data/ \
+    harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220 bash
+```
+
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用，其它包安装：
+```
+pip install pycountry
+```
+
+## 数据集
+`暂无`
+
+## 训练
+`暂无`
+
+## 推理
+### vllm
+#### 单机推理
+```bash
+## serve启动
+ vllm serve wudq/EmoCaliber  --trust-remote-code -tp 1   --port 8010  --disable-custom-all-reduce --enforce-eager 
+
+## client访问
+curl -X POST "http://localhost:8010/v1/chat/completions" \
+        -H "Content-Type: application/json" \
+        --data '{
+                "model": "wudq/EmoCaliber",
+                "messages": [
+                        {
+                                "role": "user",
+                                "content": [
+                                        {
+                                                "type": "text",
+                                                "text": "Describe this image in one sentence."
+                                        },
+                                        {
+                                                "type": "image_url",
+                                                "image_url": {
+                                                        "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                                                }
+                                        }
+                                ]
+                        }
+                ]
+        }'
+
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+### 精度
+`DCU与GPU精度一致，推理框架：vllm。`
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| EmoCaliber | 8B | BW1000 | 1 | [HuggingFace](https://huggingface.co/wudq/EmoCaliber) |
+
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/emocaliber_vllm
+
+## 参考资料
+- https://github.com/wdqqdw/EmoCaliber
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=2339
+# 模型名称
+modelName=EmoCaliber_vllm
+# 模型描述
+modelDescription=EmoCaliber基于Qwen2.5-VL-7B实现，能够以相同的方式进行推理和训练。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=多模态
+# 框架类型
+frameType=vllm
+# 加速卡类型
+accelerateType=BW1000