diff --git a/ms-swift/silence_overlaps/only_overlap/.ipynb_checkpoints/overlap5s_isoverlap_train-checkpoint.json b/ms-swift/silence_overlaps/only_overlap/.ipynb_checkpoints/overlap5s_isoverlap_train-checkpoint.json
new file mode 100644
index 0000000000000000000000000000000000000000..5aa2e4b4a35d1b8aafa37dbd5b07255db7d0b585
--- /dev/null
+++ b/ms-swift/silence_overlaps/only_overlap/.ipynb_checkpoints/overlap5s_isoverlap_train-checkpoint.json
@@ -0,0 +1,3522 @@
+[
+  {
+    "key": "SODA_PROCESSED--train--69960",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--69960.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--500115",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--500115.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--706051",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--706051.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--868975",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--868975.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:22-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1149932",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1149932.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:28-00:34"
+  },
+  {
+    "key": "SODA_PROCESSED--train--449689",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--449689.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:37-00:44"
+  },
+  {
+    "key": "SODA_PROCESSED--train--245101",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--245101.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--12047",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--12047.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--640494",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--640494.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--787791",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--787791.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--290375",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--290375.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--374749",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--374749.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--56287",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--56287.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:33-00:38"
+  },
+  {
+    "key": "SODA_PROCESSED--train--497264",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--497264.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:20-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--484021",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--484021.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--583537",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--583537.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1097575",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1097575.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--403051",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--403051.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--960193",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--960193.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--771154",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--771154.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--137471",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--137471.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:52-00:58"
+  },
+  {
+    "key": "SODA_PROCESSED--train--575640",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--575640.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--89079",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--89079.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--844892",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--844892.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--737726",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--737726.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--867206",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--867206.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--810624",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--810624.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--748043",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--748043.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--610118",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--610118.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--964867",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--964867.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--189460",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--189460.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:30-00:34"
+  },
+  {
+    "key": "SODA_PROCESSED--train--277828",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--277828.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--115967",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--115967.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1084325",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1084325.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--536240",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--536240.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--370577",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--370577.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1087984",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1087984.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--489920",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--489920.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1168213",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1168213.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--845126",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--845126.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--338555",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--338555.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--934782",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--934782.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:45-00:56"
+  },
+  {
+    "key": "SODA_PROCESSED--train--546350",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--546350.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:02-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1132437",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1132437.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--82144",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--82144.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--96343",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--96343.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--102351",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--102351.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--742899",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--742899.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--501206",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--501206.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--680740",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--680740.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1128813",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1128813.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1794",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1794.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--211161",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--211161.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1120331",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1120331.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--413405",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--413405.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--583204",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--583204.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--486716",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--486716.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--806479",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--806479.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--126313",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--126313.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1171436",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1171436.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--674707",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--674707.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1047480",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1047480.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--649949",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--649949.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--489519",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--489519.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1080121",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1080121.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--730163",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--730163.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--775640",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--775640.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--403013",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--403013.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--468603",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--468603.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--834165",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--834165.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--691830",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--691830.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:24-00:29"
+  },
+  {
+    "key": "SODA_PROCESSED--train--115322",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--115322.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--805432",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--805432.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--906513",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--906513.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--162201",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--162201.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--121129",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--121129.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:26-00:33"
+  },
+  {
+    "key": "SODA_PROCESSED--train--47850",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--47850.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:32-00:39"
+  },
+  {
+    "key": "SODA_PROCESSED--train--931585",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--931585.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--978945",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--978945.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--13635",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--13635.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1080787",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1080787.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--860269",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--860269.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--265976",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--265976.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1106201",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1106201.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--525710",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--525710.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--671614",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--671614.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1017701",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1017701.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1122880",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1122880.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1151383",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1151383.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1180469",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1180469.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--615914",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--615914.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--992151",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--992151.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--150383",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--150383.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--303336",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--303336.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--946489",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--946489.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--725089",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--725089.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--809183",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--809183.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--375003",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--375003.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1076439",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1076439.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--162106",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--162106.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--766234",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--766234.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--185307",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--185307.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--146504",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--146504.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--932676",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--932676.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--242753",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--242753.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--686928",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--686928.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--321447",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--321447.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--942720",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--942720.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1020192",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1020192.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--735517",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--735517.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--421778",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--421778.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:51-01:00"
+  },
+  {
+    "key": "SODA_PROCESSED--train--556060",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--556060.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--578986",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--578986.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--637306",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--637306.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--397434",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--397434.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:02-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--636981",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--636981.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1083511",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1083511.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--328835",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--328835.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--677647",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--677647.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--734147",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--734147.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--231222",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--231222.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--708040",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--708040.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1059254",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1059254.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--852467",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--852467.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--819618",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--819618.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--65367",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--65367.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--541347",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--541347.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--994948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--994948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1049130",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1049130.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--991838",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--991838.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1070688",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1070688.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--385362",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--385362.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--934119",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--934119.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--31287",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--31287.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:31-00:37"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1073790",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1073790.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1066203",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1066203.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--895260",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--895260.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--596356",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--596356.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--385591",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--385591.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--481276",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--481276.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--304811",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--304811.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--692789",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--692789.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--41116",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--41116.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--58199",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--58199.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--951661",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--951661.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--181032",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--181032.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--58019",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--58019.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--90720",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--90720.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--389182",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--389182.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--315768",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--315768.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--860576",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--860576.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--649458",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--649458.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:20-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1117467",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1117467.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--967919",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--967919.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:29-00:34"
+  },
+  {
+    "key": "SODA_PROCESSED--train--154340",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--154340.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--105890",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--105890.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--660884",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--660884.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1020087",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1020087.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--604161",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--604161.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--674832",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--674832.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--672051",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--672051.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1011922",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1011922.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--228608",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--228608.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--943593",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--943593.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1130331",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1130331.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1188975",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1188975.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--27446",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--27446.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--748822",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--748822.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--221358",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--221358.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1182464",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1182464.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--720445",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--720445.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--206574",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--206574.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1174912",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1174912.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--609826",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--609826.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--781246",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--781246.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--876226",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--876226.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--187120",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--187120.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--287169",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--287169.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--49462",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--49462.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--29858",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--29858.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--371354",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--371354.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--28766",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--28766.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:27-00:32"
+  },
+  {
+    "key": "SODA_PROCESSED--train--596349",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--596349.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--596068",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--596068.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--366333",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--366333.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--717045",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--717045.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--4948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--4948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--835488",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--835488.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--466262",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--466262.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--535368",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--535368.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--831005",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--831005.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:33-00:41"
+  },
+  {
+    "key": "SODA_PROCESSED--train--419812",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--419812.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--674667",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--674667.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--731416",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--731416.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--341205",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--341205.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--962013",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--962013.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--338268",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--338268.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--923496",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--923496.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--263928",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--263928.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--179972",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--179972.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:29-00:36"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1050427",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1050427.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--406352",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--406352.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--791383",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--791383.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--604536",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--604536.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--984123",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--984123.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--690063",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--690063.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--201044",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--201044.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--565509",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--565509.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1123711",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1123711.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--322914",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--322914.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--679971",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--679971.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--334612",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--334612.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--316554",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--316554.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--511668",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--511668.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--526278",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--526278.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--821468",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--821468.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--412543",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--412543.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:26-00:35"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1010433",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1010433.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:29"
+  },
+  {
+    "key": "SODA_PROCESSED--train--299895",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--299895.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--645254",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--645254.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:30-00:34"
+  },
+  {
+    "key": "SODA_PROCESSED--train--677446",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--677446.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1107765",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1107765.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--346041",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--346041.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1146016",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1146016.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1123691",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1123691.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:24-00:32"
+  },
+  {
+    "key": "SODA_PROCESSED--train--205413",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--205413.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--69581",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--69581.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--435477",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--435477.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--716169",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--716169.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1113559",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1113559.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--873625",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--873625.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:45-00:52"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1087723",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1087723.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--606362",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--606362.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--559369",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--559369.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--115102",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--115102.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--805520",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--805520.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--180962",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--180962.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--249372",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--249372.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--824468",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--824468.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1175954",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1175954.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:02-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--571406",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--571406.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--439119",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--439119.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1051467",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1051467.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1140564",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1140564.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--619961",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--619961.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--41123",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--41123.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--928982",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--928982.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--142496",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--142496.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--616846",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--616846.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1079940",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1079940.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--896752",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--896752.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--233660",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--233660.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--564472",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--564472.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--977126",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--977126.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--142004",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--142004.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1113674",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1113674.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--16779",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--16779.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--98947",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--98947.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1080342",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1080342.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--29039",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--29039.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--488132",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--488132.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--650705",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--650705.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--562877",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--562877.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--25283",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--25283.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--787888",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--787888.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1001071",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1001071.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--635774",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--635774.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--282928",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--282928.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--463322",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--463322.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--148115",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--148115.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--746047",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--746047.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--404560",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--404560.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--63072",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--63072.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--81191",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--81191.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--905170",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--905170.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:25-00:32"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1108323",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1108323.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--188417",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--188417.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--526216",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--526216.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--276032",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--276032.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1030006",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1030006.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--953260",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--953260.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--241883",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--241883.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--186110",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--186110.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--828152",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--828152.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1007617",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1007617.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--254497",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--254497.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--999278",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--999278.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--627283",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--627283.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--217814",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--217814.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--352805",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--352805.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--714054",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--714054.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1004891",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1004891.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--954404",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--954404.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--32633",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--32633.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--715956",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--715956.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--447088",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--447088.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--846507",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--846507.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--840566",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--840566.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--22719",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--22719.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--727873",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--727873.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--556475",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--556475.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--762740",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--762740.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--368483",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--368483.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:43-00:50"
+  },
+  {
+    "key": "SODA_PROCESSED--train--921984",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--921984.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--813478",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--813478.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--158293",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--158293.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--468879",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--468879.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--277322",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--277322.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--57082",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--57082.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--798455",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--798455.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--559292",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--559292.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--990528",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--990528.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--27924",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--27924.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--193968",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--193968.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--857144",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--857144.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--776571",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--776571.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--980776",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--980776.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--188315",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--188315.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--471264",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--471264.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--876910",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--876910.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--608672",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--608672.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1084179",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1084179.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1101616",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1101616.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--233562",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--233562.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--273875",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--273875.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--296467",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--296467.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--193891",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--193891.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--986089",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--986089.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--39376",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--39376.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--316553",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--316553.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--630141",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--630141.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--531935",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--531935.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--690452",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--690452.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1052554",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1052554.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--538365",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--538365.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--7207",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--7207.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--729972",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--729972.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--966021",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--966021.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--514047",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--514047.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--891432",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--891432.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:02-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--465565",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--465565.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--208819",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--208819.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--903126",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--903126.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--719833",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--719833.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--514230",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--514230.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--297496",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--297496.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--713730",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--713730.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--271652",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--271652.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--456190",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--456190.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--778927",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--778927.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--391765",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--391765.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--905725",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--905725.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--571413",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--571413.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--120698",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--120698.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--655306",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--655306.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--8155",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--8155.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--957212",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--957212.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--477747",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--477747.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--115625",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--115625.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1177869",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1177869.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1051089",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1051089.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:30"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1169363",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1169363.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--19531",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--19531.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--156618",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--156618.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--769683",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--769683.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1092370",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1092370.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--778704",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--778704.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--908191",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--908191.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1112132",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1112132.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--417260",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--417260.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--994009",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--994009.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--415948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--415948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--30336",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--30336.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--269886",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--269886.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--49720",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--49720.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--82238",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--82238.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--231370",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--231370.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--277060",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--277060.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--421624",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--421624.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--191104",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--191104.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:38-00:41"
+  },
+  {
+    "key": "SODA_PROCESSED--train--262975",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--262975.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--104948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--104948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--475793",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--475793.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1129671",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1129671.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--803822",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--803822.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:24-00:30"
+  },
+  {
+    "key": "SODA_PROCESSED--train--638647",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--638647.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:59-01:05"
+  },
+  {
+    "key": "SODA_PROCESSED--train--460378",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--460378.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--98281",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--98281.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--590679",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--590679.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--449068",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--449068.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--581770",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--581770.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--437038",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--437038.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1076109",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1076109.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--132857",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--132857.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1041859",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1041859.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--580598",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--580598.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--43736",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--43736.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--12295",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--12295.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--84371",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--84371.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--40901",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--40901.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--811940",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--811940.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:20-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--698246",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--698246.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1188579",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1188579.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--752118",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--752118.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--251891",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--251891.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--601727",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--601727.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--437704",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--437704.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--938056",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--938056.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--952835",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--952835.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--648176",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--648176.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--311173",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--311173.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--24058",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--24058.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--220023",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--220023.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--475039",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--475039.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--875462",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--875462.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--156744",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--156744.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--471036",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--471036.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--82370",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--82370.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--538753",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--538753.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--520823",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--520823.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--933257",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--933257.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--494198",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--494198.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--33171",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--33171.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--631363",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--631363.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--246434",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--246434.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--420707",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--420707.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--104984",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--104984.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--324306",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--324306.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1180329",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1180329.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--8820",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--8820.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:20-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--578559",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--578559.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:55-01:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--76363",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--76363.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--872171",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--872171.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--761001",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--761001.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--927063",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--927063.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--431708",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--431708.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--244490",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--244490.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1186756",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1186756.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--338832",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--338832.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1050100",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1050100.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--566903",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--566903.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--258235",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--258235.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--412246",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--412246.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:27-00:32"
+  },
+  {
+    "key": "SODA_PROCESSED--train--860535",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--860535.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--584031",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--584031.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--925277",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--925277.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--243573",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--243573.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--633173",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--633173.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1088159",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1088159.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--706924",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--706924.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1166623",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1166623.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--366989",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--366989.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:36-00:40"
+  },
+  {
+    "key": "SODA_PROCESSED--train--153751",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--153751.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:28-00:34"
+  },
+  {
+    "key": "SODA_PROCESSED--train--101351",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--101351.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--514225",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--514225.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--194829",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--194829.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1085362",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1085362.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--419304",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--419304.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--207527",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--207527.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1131334",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1131334.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--214477",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--214477.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--203528",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--203528.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1010589",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1010589.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--956648",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--956648.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--559740",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--559740.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--603030",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--603030.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--934138",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--934138.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--387024",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--387024.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--44625",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--44625.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--833014",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--833014.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--243315",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--243315.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--108309",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--108309.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--595180",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--595180.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--556505",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--556505.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--727552",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--727552.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:34-00:39"
+  },
+  {
+    "key": "SODA_PROCESSED--train--524306",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--524306.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--762905",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--762905.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--757426",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--757426.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--935998",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--935998.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--179376",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--179376.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--951487",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--951487.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--289733",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--289733.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--676747",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--676747.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:24-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--974933",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--974933.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:25-00:33"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1074077",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1074077.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--740576",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--740576.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--413649",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--413649.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--209436",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--209436.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:36-00:44"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1028263",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1028263.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1574",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1574.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--977434",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--977434.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1096861",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1096861.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1029082",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1029082.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1020068",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1020068.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--937978",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--937978.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--985415",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--985415.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--476626",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--476626.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:40-00:45"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1022590",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1022590.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--153526",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--153526.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--560596",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--560596.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--41171",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--41171.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--926731",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--926731.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--15103",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--15103.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1124666",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1124666.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--737676",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--737676.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--682132",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--682132.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--310941",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--310941.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--617278",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--617278.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--836018",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--836018.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--894199",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--894199.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--218746",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--218746.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--218634",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--218634.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--508884",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--508884.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:30-00:37"
+  },
+  {
+    "key": "SODA_PROCESSED--train--667784",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--667784.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--700208",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--700208.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--767328",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--767328.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--571155",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--571155.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--633328",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--633328.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:24-00:32"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1001007",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1001007.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--843172",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--843172.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--100933",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--100933.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--883174",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--883174.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--252183",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--252183.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--634390",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--634390.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1031234",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1031234.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--876642",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--876642.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--55746",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--55746.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--171463",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--171463.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--159893",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--159893.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--535550",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--535550.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--237231",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--237231.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1185164",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1185164.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--719372",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--719372.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--726133",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--726133.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--730476",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--730476.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1025535",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1025535.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:15, 00:42-00:47"
+  },
+  {
+    "key": "SODA_PROCESSED--train--302394",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--302394.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--967872",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--967872.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--356688",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--356688.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--169891",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--169891.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--486837",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--486837.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1104244",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1104244.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--760966",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--760966.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--453454",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--453454.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1031152",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1031152.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1097788",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1097788.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--532492",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--532492.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--331396",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--331396.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--330048",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--330048.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1095086",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1095086.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--286623",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--286623.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--295395",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--295395.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--302689",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--302689.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:30-00:35"
+  },
+  {
+    "key": "SODA_PROCESSED--train--826028",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--826028.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--179580",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--179580.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--219689",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--219689.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--4955",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--4955.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1007416",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1007416.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--28408",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--28408.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--888611",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--888611.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1013581",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1013581.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--472748",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--472748.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--473100",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--473100.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1022129",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1022129.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--285043",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--285043.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--168398",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--168398.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:49-00:59"
+  },
+  {
+    "key": "SODA_PROCESSED--train--774546",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--774546.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1055385",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1055385.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--42187",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--42187.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:01-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--125527",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--125527.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--419679",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--419679.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--193134",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--193134.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--903704",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--903704.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--848051",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--848051.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--797399",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--797399.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1050248",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1050248.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--810606",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--810606.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:43-00:51"
+  },
+  {
+    "key": "SODA_PROCESSED--train--420178",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--420178.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--748077",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--748077.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--911067",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--911067.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--901379",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--901379.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--144310",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--144310.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--280675",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--280675.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--378283",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--378283.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:25"
+  },
+  {
+    "key": "SODA_PROCESSED--train--187351",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--187351.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--327822",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--327822.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--68508",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--68508.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:22-00:29"
+  },
+  {
+    "key": "SODA_PROCESSED--train--609913",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--609913.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--258272",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--258272.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--662948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--662948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--140026",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--140026.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1131940",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1131940.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--317784",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--317784.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--109817",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--109817.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--588465",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--588465.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--126878",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--126878.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--770919",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--770919.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--73764",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--73764.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--605295",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--605295.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--258280",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--258280.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--165189",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--165189.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--70224",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--70224.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--708343",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--708343.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--214621",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--214621.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--205208",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--205208.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--1142459",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1142459.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--164800",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--164800.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--76986",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--76986.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--940934",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--940934.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--640525",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--640525.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--869455",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--869455.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:42-00:51"
+  },
+  {
+    "key": "SODA_PROCESSED--train--836452",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--836452.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--842885",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--842885.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1182226",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1182226.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--456887",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--456887.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--118976",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--118976.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--748910",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--748910.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--152375",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--152375.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:30-00:35"
+  },
+  {
+    "key": "SODA_PROCESSED--train--815822",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--815822.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--766305",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--766305.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--65045",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--65045.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--268426",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--268426.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--221517",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--221517.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--869501",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--869501.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:22-00:29"
+  },
+  {
+    "key": "SODA_PROCESSED--train--718092",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--718092.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--201813",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--201813.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:48-00:55"
+  },
+  {
+    "key": "SODA_PROCESSED--train--915430",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--915430.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1107341",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1107341.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--903574",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--903574.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--741600",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--741600.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--836740",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--836740.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--463700",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--463700.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--414445",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--414445.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--617839",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--617839.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--846418",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--846418.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:20-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--11862",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--11862.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--146698",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--146698.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--743500",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--743500.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--185821",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--185821.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--585968",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--585968.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--487317",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--487317.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:23-00:30"
+  },
+  {
+    "key": "SODA_PROCESSED--train--545009",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--545009.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--75062",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--75062.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--343780",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--343780.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--900948",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--900948.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1144076",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1144076.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:19-00:28"
+  },
+  {
+    "key": "SODA_PROCESSED--train--719199",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--719199.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--78189",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--78189.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--490986",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--490986.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--218430",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--218430.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--782660",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--782660.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--14913",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--14913.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--861085",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--861085.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--908444",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--908444.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:09"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1007774",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1007774.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--399572",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--399572.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--930750",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--930750.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--4814",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--4814.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--532510",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--532510.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--61606",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--61606.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--303363",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--303363.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:17"
+  },
+  {
+    "key": "SODA_PROCESSED--train--999053",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--999053.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--33760",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--33760.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:24"
+  },
+  {
+    "key": "SODA_PROCESSED--train--226853",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--226853.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:13-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--913166",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--913166.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--795559",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--795559.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--933043",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--933043.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:15"
+  },
+  {
+    "key": "SODA_PROCESSED--train--932576",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--932576.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--775321",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--775321.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--795181",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--795181.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:16-00:22"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1030451",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1030451.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1023331",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1023331.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:26"
+  },
+  {
+    "key": "SODA_PROCESSED--train--961025",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--961025.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--962210",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--962210.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:14-00:20"
+  },
+  {
+    "key": "SODA_PROCESSED--train--906269",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--906269.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--385442",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--385442.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--109428",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--109428.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--197137",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--197137.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--523646",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--523646.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--98673",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--98673.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:12"
+  },
+  {
+    "key": "SODA_PROCESSED--train--317167",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--317167.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:04-00:07"
+  },
+  {
+    "key": "SODA_PROCESSED--train--90368",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--90368.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:18"
+  },
+  {
+    "key": "SODA_PROCESSED--train--20810",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--20810.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:10"
+  },
+  {
+    "key": "SODA_PROCESSED--train--301485",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--301485.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:12-00:19"
+  },
+  {
+    "key": "SODA_PROCESSED--train--372789",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--372789.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:03-00:08"
+  },
+  {
+    "key": "SODA_PROCESSED--train--744708",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--744708.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--874990",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--874990.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--369090",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--369090.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--894939",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--894939.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1186623",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1186623.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:06-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1092169",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--1092169.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:18-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--113543",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--113543.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--888565",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--888565.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:11-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--322068",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--322068.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:14"
+  },
+  {
+    "key": "SODA_PROCESSED--train--970059",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--970059.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:09-00:13"
+  },
+  {
+    "key": "SODA_PROCESSED--train--334902",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--334902.wav",
+    "model_output": "No significant overlaps found."
+  },
+  {
+    "key": "SODA_PROCESSED--train--152172",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--152172.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:10-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--855653",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--855653.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:05-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--202914",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--202914.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:07-00:16"
+  },
+  {
+    "key": "SODA_PROCESSED--train--527126",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--527126.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:21-00:27"
+  },
+  {
+    "key": "SODA_PROCESSED--train--480349",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--480349.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:11"
+  },
+  {
+    "key": "SODA_PROCESSED--train--784738",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--784738.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:15-00:21"
+  },
+  {
+    "key": "SODA_PROCESSED--train--418520",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--418520.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:17-00:23"
+  },
+  {
+    "key": "SODA_PROCESSED--train--159547",
+    "audio_url": "/root/autodl-tmp/output_overlapslong/newoverlapjson/overlap5s700/SODA_PROCESSED--train--159547.wav",
+    "model_output": "Multiple speakers talk simultaneously from 00:08-00:13"
+  }
+]
\ No newline at end of file
diff --git a/ms-swift/swift/tuners/__pycache__/lora.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/lora.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a84171d197e6e2b2939e5e82d99cb82d1d3881d
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/lora.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/mapping.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/mapping.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e525518b983fa4eb731f8aaf027ebe45f24cbc8
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/mapping.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/part.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/part.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..95b0f18b0197792a6df6536f3195526013dff0b5
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/part.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/restuning.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/restuning.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..099e3a469d2d1f9218c31e7d099c1841e7f6a034
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/restuning.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/restuning_components.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/restuning_components.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d6cecb67629246f39dd0443f9b84f20891bff238
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/restuning_components.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/side.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/side.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..01bbb3202a3c31d85c27b67fa37703698402a1c9
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/side.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/__pycache__/utils.cpython-310.pyc b/ms-swift/swift/tuners/__pycache__/utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bcb409f3892ea7f10fbdebf336ecd749f593f023
Binary files /dev/null and b/ms-swift/swift/tuners/__pycache__/utils.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/adapter.py b/ms-swift/swift/tuners/adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..290040b551b5e969eeb7b59bcc7dfd63536b57e3
--- /dev/null
+++ b/ms-swift/swift/tuners/adapter.py
@@ -0,0 +1,189 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import inspect
+import re
+import types
+from dataclasses import dataclass, field
+from typing import List, Union
+
+import torch
+from torch import nn
+from transformers.activations import ACT2CLS
+
+from swift.utils.torch_utils import find_sub_module, get_logger
+from .utils import ActivationMixin, SwiftAdapter, SwiftConfig, SwiftOutput
+
+logger = get_logger()
+
+
+@dataclass
+class AdapterConfig(SwiftConfig):
+    """
+    The configuration class for the adapter module.
+
+    Adapters project input tokens by an MLP layer.
+    'Parameter-Efficient Transfer Learning for NLP' by Houlsby et al.(2019)
+    See http://arxiv.org/abs/1902.00751
+
+    Args:
+        dim(`int`): The dimension of the hidden states
+        target_modules(`Union[str, List[str]]`): The feedforward module to be replaced.
+            in regex format if this argument is str, else will match with `end with` if List[str].
+        hidden_pos(`Union[str, int]`): The position of the hidden state to be passed into the adapter,
+            can be int (args) or str (kwargs)
+        method_name(`str`): The method to be replaced, default is `forward`
+        adapter_length: The length of the adapter length (intermediate length)
+        act_layer: The activation layer of the adapter
+    """
+
+    dim: int = field(default=None, metadata={'help': 'The dimension of the hidden states'})
+
+    target_modules: Union[str, List[str]] = field(
+        default=None,
+        metadata={
+            'help':
+            'The feedforward module to be replaced. in regex format if this argument is str, '
+            'else will match with `end with` if List[str].'
+        })
+
+    hidden_pos: Union[str, int] = field(
+        default=None,
+        metadata={
+            'help': 'The position of the hidden state to be passed into the adapter, can be int (args) or str (kwargs)'
+        })
+
+    method_name: str = field(default='forward', metadata={'help': 'The method to be replaced, default is `forward`'})
+
+    adapter_length: int = field(
+        default=128, metadata={'help': 'The length of the adapter length (intermediate length)'})
+
+    act_layer: str = field(default='gelu', metadata={'help': 'The activation layer of the adapter'})
+
+    def __post_init__(self):
+        from .mapping import SwiftTuners
+        self.swift_type = SwiftTuners.ADAPTER
+
+
+class Adapter(SwiftAdapter):
+
+    @staticmethod
+    def prepare_model(model: nn.Module, config: AdapterConfig, adapter_name: str) -> SwiftOutput:
+        """Prepare a model with `AdapterConfig`"""
+        module_keys = [key for key, _ in model.named_modules()]
+
+        for module_key in module_keys:
+            if isinstance(config.target_modules, str):
+                target_module_found = re.fullmatch(config.target_modules, module_key)
+            else:
+                target_module_found = any(module_key.endswith(target_key) for target_key in config.target_modules)
+
+            if target_module_found:  # noqa
+                module = model.get_submodule(module_key)
+
+                def _forward(self, *args, **kwargs):
+                    args = getattr(self, f'forward_origin_{adapter_name}')(*args, **kwargs)
+                    if isinstance(args, (tuple, list, dict)):
+                        if isinstance(config.hidden_pos, int):
+                            _type = type(args)
+                            args = list(args)
+                            args[config.hidden_pos] = getattr(self, f'adapter_{adapter_name}')(args[config.hidden_pos])
+                            args = _type(args)
+                        else:
+                            args[config.hidden_pos] = getattr(self, f'adapter_{adapter_name}')(args[config.hidden_pos])
+                    elif isinstance(args, torch.Tensor):
+                        args = getattr(self, f'adapter_{adapter_name}')(args)
+                    return args
+
+                def _feed_forward_chunk(self, attention_output):
+                    return _forward(self, attention_output)
+
+                # TODO The `config.method_name` method should not be replaced twice.
+
+                setattr(module, f'forward_origin_{adapter_name}', getattr(module, config.method_name))
+                num_args_in_forward_chunk_fn = len(
+                    inspect.signature(getattr(module, f'forward_origin_{adapter_name}')).parameters)
+                if config.method_name == 'feed_forward_chunk' and num_args_in_forward_chunk_fn == 1:
+                    setattr(module, config.method_name, types.MethodType(_feed_forward_chunk, module))
+                else:
+                    setattr(module, config.method_name, types.MethodType(_forward, module))
+                adapter_module = AdapterModule(config.dim, adapter_name, module_key, config.adapter_length,
+                                               ACT2CLS[config.act_layer])
+                setattr(module, f'adapter_{adapter_name}', adapter_module)
+                logger.info(f'Adapter modules(module_key): {module_key}.adapter_{adapter_name}')
+
+        def state_dict_callback(state_dict, adapter_name: str, **kwargs):
+            return {key: value for key, value in state_dict.items() if f'adapter_{adapter_name}' in key}
+
+        def mark_trainable_callback(model):
+            return
+
+        return SwiftOutput(
+            config=config, state_dict_callback=state_dict_callback, mark_trainable_callback=mark_trainable_callback)
+
+    @staticmethod
+    def activate_adapter(module: torch.nn.Module, adapter_name: str, activate: bool, offload: str = None):
+        modules = find_sub_module(module, f'adapter_{adapter_name}')
+        for _module in modules:
+            _module: ActivationMixin
+            _module: nn.Module
+            _module.set_activation(adapter_name, activate)
+            SwiftAdapter.save_memory(_module, adapter_name, _module.module_key, activate, offload)
+
+
+class AdapterModule(nn.Module, ActivationMixin):
+    """The implementation of adapter tuning method.
+
+    Adapters project input tokens by an MLP layer.
+    'Parameter-Efficient Transfer Learning for NLP' by Houlsby et al.(2019)
+    See http://arxiv.org/abs/1902.00751
+
+    Args:
+        dim: An integer indicating the embedding dimension.
+        adapter_length: An integer indicating the length of adapter tuning.
+    """
+
+    def __init__(
+        self,
+        dim,
+        adapter_name,
+        module_key,
+        adapter_length=None,
+        act_layer=nn.GELU,
+    ):
+        super(AdapterModule, self).__init__()
+        super(nn.Module, self).__init__(module_key)
+        self.dim = dim
+        self.adapter_name = adapter_name
+        self.adapter_length = adapter_length
+        self.linear1 = nn.Linear(dim, adapter_length)
+        self.act = act_layer()
+        self.linear2 = nn.Linear(adapter_length, dim)
+        self.init_weights()
+        self._prepared = False
+        self.mark_all_sub_modules_as_plugin()
+
+    def init_weights(self):
+
+        def _init_weights(m):
+            if isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight)
+                nn.init.normal_(m.bias, std=1e-6)
+
+        self.apply(_init_weights)
+
+    def forward(self, x, identity=None):
+        if not self.is_activated(self.adapter_name):
+            return x
+        if not self._prepared:
+            self.linear1.to(x.device)
+            self.act.to(x.device)
+            self.linear2.to(x.device)
+            self._prepared = True
+
+        x_dtype = x.dtype
+        x = x.to(self.linear1.weight.dtype)
+        out = self.linear2(self.act(self.linear1(x)))
+        if identity is None:
+            identity = x
+        identity = identity.to(out.dtype)
+        out = identity + out
+        return out.to(x_dtype)
diff --git a/ms-swift/swift/tuners/longlora/__pycache__/longlora.cpython-310.pyc b/ms-swift/swift/tuners/longlora/__pycache__/longlora.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6d22a0f14ce27e7780dc8dcda96504d484a60b41
Binary files /dev/null and b/ms-swift/swift/tuners/longlora/__pycache__/longlora.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/peft.py b/ms-swift/swift/tuners/peft.py
new file mode 100644
index 0000000000000000000000000000000000000000..f561db4fc049d167f87c56bfae28b201dc967b6d
--- /dev/null
+++ b/ms-swift/swift/tuners/peft.py
@@ -0,0 +1,392 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+# Copyright 2023-present the HuggingFace Inc. team.
+import os.path
+from dataclasses import asdict, dataclass, field
+from functools import partial, reduce
+from types import MethodType
+from typing import Dict, Optional
+
+import json
+import peft
+import torch
+import torch.nn
+import transformers
+from modelscope import snapshot_download
+from peft import (AdaLoraConfig, BOFTConfig, BOFTModel, LoftQConfig, LoHaConfig, LoKrConfig, LoraModel, OFTConfig,
+                  PeftConfig, PeftModel, PeftModelForCausalLM, PeftModelForSeq2SeqLM,
+                  PeftModelForSequenceClassification, PeftModelForTokenClassification, PrefixTuningConfig,
+                  PromptEncoderConfig, PromptLearningConfig, PromptTuningConfig, VeraConfig, VeraModel, get_peft_config,
+                  get_peft_model, get_peft_model_state_dict)
+from peft.config import PeftConfigMixin
+from peft.tuners import lora
+from peft.tuners.adalora import AdaLoraModel, RankAllocator
+from peft.tuners.lora import Embedding
+from transformers import Trainer
+
+from swift.utils import get_logger
+
+try:
+    from peft import FourierFTModel
+except ImportError:
+    FourierFTModel = None
+
+try:
+    from peft import BoneModel
+except ImportError:
+    BoneModel = None
+
+logger = get_logger()
+dispatchers = []
+
+
+@dataclass
+class LoraConfig(peft.LoraConfig):
+    lora_dtype: Optional[str] = field(
+        default=None, metadata={'help': 'The lora dtype, default None means following the original layer\'s dtype'})
+
+    lorap_lr_ratio: Optional[float] = field(default=None, metadata={'help': 'The lr ratio of lora_B in lora+'})
+
+    lorap_emb_lr: float = field(default=1e-6, metadata={'help': 'The lr for embedding in lora+'})
+
+    def to_peft_config(self) -> peft.LoraConfig:
+        _dict = asdict(self)
+        _dict.pop('lora_dtype')
+        _dict.pop('lorap_lr_ratio')
+        _dict.pop('lorap_emb_lr')
+        return peft.LoraConfig(**_dict)
+
+    def save_pretrained(self, save_directory: str, **kwargs) -> None:
+        self.to_peft_config().save_pretrained(save_directory, **kwargs)
+        additional_args = {
+            'lora_dtype': self.lora_dtype,
+            'lorap_lr_ratio': self.lorap_lr_ratio,
+            'lorap_emb_lr': self.lorap_emb_lr,
+        }
+        with open(os.path.join(save_directory, 'additional_config.json'), 'w', encoding='utf-8') as f:
+            json.dump(additional_args, f)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, subfolder: Optional[str] = None, **kwargs):
+        if hasattr(PeftConfigMixin, 'from_pretrained_origin'):
+            self = PeftConfigMixin.from_pretrained_origin(pretrained_model_name_or_path, subfolder, **kwargs)
+        else:
+            self = super(LoraConfig, cls).from_pretrained(pretrained_model_name_or_path, subfolder, **kwargs)
+
+        if type(self) == peft.LoraConfig:
+            self = LoraConfig(**self.to_dict())
+
+        if os.path.isfile(os.path.join(pretrained_model_name_or_path, 'additional_config.json')):
+            with open(
+                    os.path.join(pretrained_model_name_or_path, 'additional_config.json'), 'r', encoding='utf-8') as f:
+                _json = json.load(f)
+                for key, value in _json.items():
+                    setattr(self, key, value)
+
+        return self
+
+
+def _create_and_replace_hook(self, peft_config, adapter_name, target, *args, **kwargs):
+    all_supported_names = ('linear', )
+    all_supported_types = (torch.nn.Embedding, torch.nn.Conv2d, transformers.pytorch_utils.Conv1D, lora.Linear)
+    target_modules = getattr(peft_config, 'target_modules', None)
+    if target is None:
+        return
+
+    if isinstance(target_modules, str) and not any(
+        [name in target.__class__.__name__.lower()
+         for name in all_supported_names]) and not any([isinstance(target, type_) for type_ in all_supported_types]):
+        return
+
+    if target.__class__.__name__ == 'NonDynamicallyQuantizableLinear':
+        return
+
+    return self._create_and_replace_origin(peft_config, adapter_name, target, *args, **kwargs)
+
+
+def _convert_dtype(target: torch.nn.Module, adapter_name: str, lora_dtype: str):
+    if lora_dtype is not None:
+        torch_dtype = eval(f'torch.{lora_dtype}')
+        if hasattr(target, 'lora_A') and adapter_name in target.lora_A:
+            target.lora_A[adapter_name].to(torch_dtype)
+            target.lora_B[adapter_name].to(torch_dtype)
+        if hasattr(target, 'lora_embedding_A') and adapter_name in target.lora_embedding_A:
+            target.lora_embedding_A[adapter_name].to(torch_dtype)
+            target.lora_embedding_B[adapter_name].to(torch_dtype)
+
+
+def create_optimizer_param_groups(self: PeftModel, **defaults):
+    if not isinstance(self.peft_config[self.active_adapter],
+                      LoraConfig) or self.peft_config[self.active_adapter].lorap_lr_ratio is None:
+        return None
+
+    def get_module(name):
+        parent_idx = 2 if 'lora' in name else 1
+        module_names = name.split(sep='.')[:-parent_idx]
+        module = reduce(getattr, module_names, self.base_model)
+        return module
+
+    param_groups = {
+        'groupA': {},
+        'groupB': {},
+        'groupB_no_decay': {},
+        'embedding': {},
+    }
+
+    decay_parameters = Trainer.get_decay_parameter_names(None, self.base_model)
+    for name, param in self.base_model.named_parameters():
+        if not param.requires_grad:
+            continue
+
+        module = get_module(name)
+        if isinstance(module, Embedding):
+            param_groups['embedding'][name] = param
+        elif 'lora_B' in name or param.ndim == 1:
+            if name in decay_parameters:
+                param_groups['groupB'][name] = param
+            else:
+                param_groups['groupB_no_decay'][name] = param
+        else:
+            param_groups['groupA'][name] = param
+
+    lr = defaults['lr']
+    weight_decay = defaults.get('weight_decay', 0.0)
+
+    param_groups = [
+        {
+            'params': list(param_groups['groupA'].values()),
+            'weight_decay': weight_decay,
+            'lr': lr,
+        },
+        {
+            'params': list(param_groups['embedding'].values()),
+            'weight_decay': weight_decay,
+            'lr': self.peft_config[self.active_adapter].lorap_emb_lr,
+        },
+        {
+            'params': list(param_groups['groupB'].values()),
+            'weight_decay': weight_decay,
+            'lr': lr * self.peft_config[self.active_adapter].lorap_lr_ratio,
+        },
+        {
+            'params': list(param_groups['groupB_no_decay'].values()),
+            'weight_decay': 0.0,
+            'lr': lr * self.peft_config[self.active_adapter].lorap_lr_ratio,
+        },
+    ]
+    return param_groups
+
+
+def adalora_forward(self, *args, **kwargs):
+    from peft.utils.integrations import gather_params_ctx
+    outputs = self.model.forward(*args, **kwargs)
+
+    if (getattr(outputs, 'loss', None) is not None) and isinstance(outputs.loss, torch.Tensor):
+        # Calculate the orthogonal regularization
+        orth_reg_weight = self.peft_config[self.trainable_adapter_name].orth_reg_weight
+
+        if orth_reg_weight <= 0:
+            raise ValueError('orth_reg_weight should be greater than 0. ')
+
+        regu_loss = 0
+        num_param = 0
+        for n, p in self.model.named_parameters():
+            if ('lora_A' in n or 'lora_B' in n) and self.trainable_adapter_name in n:
+                if p.shape == torch.Size([0]):
+                    with gather_params_ctx(p, fwd_module=self):
+                        para_cov = p @ p.T if 'lora_A' in n else p.T @ p
+                else:
+                    para_cov = p @ p.T if 'lora_A' in n else p.T @ p
+                I = torch.eye(*para_cov.size(), out=torch.empty_like(para_cov))  # noqa: E741
+                I.requires_grad = False
+                num_param += 1
+                if isinstance(regu_loss, torch.Tensor):
+                    regu_loss = regu_loss.to(para_cov.device)
+                regu_loss += torch.norm(para_cov - I, p='fro')
+        if num_param > 0:
+            regu_loss = regu_loss / num_param
+        else:
+            regu_loss = 0
+        if isinstance(regu_loss, torch.Tensor) and isinstance(outputs.loss, torch.Tensor):
+            regu_loss = regu_loss.to(outputs.loss.device)
+        outputs.loss += orth_reg_weight * regu_loss
+    return outputs
+
+
+def adalora_mask_to_budget(self, model, budget):
+    value_ipt = {}
+    vector_ipt = {}
+    triplet_ipt = {}
+    # Get the importance score for A, E, B
+    for n, p in model.named_parameters():
+        if f'lora_A.{self.adapter_name}' in n:
+            entry_ipt = self._element_score(n)
+            comb_ipt = torch.mean(entry_ipt, dim=1, keepdim=True)
+            name_m = n.replace('lora_A', '%s')
+            if name_m not in vector_ipt:
+                vector_ipt[name_m] = [comb_ipt]
+            else:
+                vector_ipt[name_m].append(comb_ipt)
+        if f'lora_B.{self.adapter_name}' in n:
+            entry_ipt = self._element_score(n)
+            comb_ipt = torch.mean(entry_ipt, dim=0, keepdim=False).view(-1, 1)
+            name_m = n.replace('lora_B', '%s')
+            if name_m not in vector_ipt:
+                vector_ipt[name_m] = [comb_ipt]
+            else:
+                vector_ipt[name_m].append(comb_ipt)
+        if f'lora_E.{self.adapter_name}' in n:
+            entry_ipt = self._element_score(n)
+            name_m = n.replace('lora_E', '%s')
+            value_ipt[name_m] = entry_ipt
+
+    all_score = []
+    # Calculate the score for each triplet
+    for name_m in vector_ipt:
+        ipt_E = value_ipt[name_m]
+        ipt_AB = torch.cat(vector_ipt[name_m], dim=1)
+        sum_ipt = self._combine_ipt(ipt_E, ipt_AB)
+        name_E = name_m % 'lora_E'
+        triplet_ipt[name_E] = sum_ipt.view(-1, 1)
+        sum_ipt = sum_ipt.view(-1)
+        if all_score:
+            sum_ipt = sum_ipt.to(all_score[0].device)
+        all_score.append(sum_ipt)
+
+    # Get the threshold by ranking ipt
+    mask_threshold = torch.kthvalue(
+        torch.cat(all_score),
+        k=self.init_bgt - budget,
+    )[0].item()
+
+    rank_pattern = {}
+    # Mask the unimportant triplets
+    with torch.no_grad():
+        for n, p in model.named_parameters():
+            if f'lora_E.{self.adapter_name}' in n:
+                p.masked_fill_(triplet_ipt[n] <= mask_threshold, 0.0)
+                rank_pattern[n] = (~(triplet_ipt[n] <= mask_threshold)).view(-1).tolist()
+    return rank_pattern
+
+
+def keep_device_forward(self, *args, **kwargs):
+    x = args[0]
+    if self.weight.device != x.device:
+        return self.forward_origin(x.to(self.weight.device), *args[1:], **kwargs)
+    else:
+        return self.forward_origin(*args, **kwargs)
+
+
+def hot_patch_peft_module():
+    from peft.tuners.lora import LoraLayer
+    if hasattr('LoraModel', '_create_and_replace_origin'):
+        return
+
+    # Fix Lora does not support NonDynamicallyQuantizableLinear
+    LoraModel._create_and_replace_origin = LoraModel._create_and_replace
+    LoraModel._create_and_replace = _create_and_replace_hook
+    AdaLoraModel._create_and_replace_origin = AdaLoraModel._create_and_replace
+    AdaLoraModel._create_and_replace = _create_and_replace_hook
+    VeraModel._create_and_replace_origin = VeraModel._create_and_replace
+    VeraModel._create_and_replace = _create_and_replace_hook
+    BOFTModel._create_and_replace_origin = BOFTModel._create_and_replace
+    BOFTModel._create_and_replace = _create_and_replace_hook
+    if FourierFTModel is not None:
+        FourierFTModel._create_and_replace_origin = FourierFTModel._create_and_replace
+        FourierFTModel._create_and_replace = _create_and_replace_hook
+    if BoneModel is not None:
+        BoneModel._create_and_replace_origin = BoneModel._create_and_replace
+        BoneModel._create_and_replace = _create_and_replace_hook
+
+    # Support type conversion
+    def __new_init__(self, model: torch.nn.Module, config: Dict[str, LoraConfig], adapter_name: str):
+
+        self.__init_origin__(model, config, adapter_name)
+        active_adapters = self.active_adapter
+        if isinstance(active_adapters, str):
+            active_adapters = [active_adapters]
+        for active_adapter in active_adapters:
+            active_config = config[active_adapter] if isinstance(config, dict) else config
+            if hasattr(active_config, 'lora_dtype'):
+                for name, module in model.named_modules():
+                    if isinstance(module, LoraLayer):
+                        _convert_dtype(module, active_adapter, active_config.lora_dtype)
+                        for lora in list(module.lora_A.values()) + list(module.lora_B.values()):
+                            if not hasattr(lora, 'forward_origin'):
+                                lora.forward_origin = lora.forward
+                                lora.forward = MethodType(keep_device_forward, lora)
+
+    LoraModel.__init_origin__ = LoraModel.__init__
+    LoraModel.__init__ = __new_init__
+
+    # Support LoRA+
+    PeftModel.create_optimizer_param_groups = create_optimizer_param_groups
+
+    PeftConfigMixin.from_pretrained_origin = PeftConfigMixin.from_pretrained
+    PeftConfigMixin.from_pretrained = LoraConfig.from_pretrained
+
+    # Compatible with SwiftModel
+    def dummy_function(*args, **kwargs):
+        logger.warn(f'The function {kwargs["func"]} has no effects, consider using other functions.')
+
+    PeftModel.activate_adapter = PeftModel.set_adapter
+    PeftModel.deactivate_adapter = partial(dummy_function, func='deactivate_adapter')
+    PeftModel.set_active_adapters = partial(dummy_function, func='set_active_adapters')
+
+    # Fix adalora does not support device_map
+    AdaLoraModel.forward = adalora_forward
+    RankAllocator.mask_to_budget = adalora_mask_to_budget
+
+
+def get_wrapped_class(module_class):
+    """Get a custom wrapper class for peft classes to download the models from the ModelScope hub
+
+    Args:
+        module_class: The actual module class
+
+    Returns:
+        The wrapper
+    """
+
+    class PeftWrapper(module_class):
+
+        @classmethod
+        def from_pretrained(cls, model, model_id, *args, revision: Optional[str] = None, **kwargs):
+            if not os.path.exists(model_id):
+                model_id = snapshot_download(model_id, revision=revision)
+            return module_class.from_pretrained(model, model_id, *args, **kwargs)
+
+    PeftWrapper.__name__ = module_class.__name__
+    PeftWrapper.__qualname__ = module_class.__qualname__
+    return PeftWrapper
+
+
+def wrap_module(module):
+    if not hasattr(module, 'from_pretrained'):
+        return module
+
+    return get_wrapped_class(module)
+
+
+hot_patch_peft_module()
+PeftModel = wrap_module(PeftModel)
+PeftConfig = wrap_module(PeftConfig)
+PeftModelForSeq2SeqLM = wrap_module(PeftModelForSeq2SeqLM)
+PeftModelForSequenceClassification = wrap_module(PeftModelForSequenceClassification)
+PeftModelForTokenClassification = wrap_module(PeftModelForTokenClassification)
+PeftModelForCausalLM = wrap_module(PeftModelForCausalLM)
+PromptEncoderConfig = wrap_module(PromptEncoderConfig)
+PromptTuningConfig = wrap_module(PromptTuningConfig)
+PrefixTuningConfig = wrap_module(PrefixTuningConfig)
+PromptLearningConfig = wrap_module(PromptLearningConfig)
+LoraConfig = wrap_module(LoraConfig)
+AdaLoraConfig = wrap_module(AdaLoraConfig)
+LoHaConfig = wrap_module(LoHaConfig)
+LoKrConfig = wrap_module(LoKrConfig)
+LoftQConfig = wrap_module(LoftQConfig)
+OFTConfig = wrap_module(OFTConfig)
+BOFTConfig = wrap_module(BOFTConfig)
+VeraConfig = wrap_module(VeraConfig)
+OFTConfig = wrap_module(OFTConfig)
+get_peft_config = get_peft_config
+get_peft_model_state_dict = get_peft_model_state_dict
+get_peft_model = get_peft_model
diff --git a/ms-swift/swift/tuners/scetuning/__pycache__/__init__.cpython-310.pyc b/ms-swift/swift/tuners/scetuning/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0e5dc98d94efce8a4e3544841ab9de516a71ec66
Binary files /dev/null and b/ms-swift/swift/tuners/scetuning/__pycache__/__init__.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/scetuning/__pycache__/scetuning.cpython-310.pyc b/ms-swift/swift/tuners/scetuning/__pycache__/scetuning.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cf62e3719597b6eb8631f90ec6db4a738bcfb52a
Binary files /dev/null and b/ms-swift/swift/tuners/scetuning/__pycache__/scetuning.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/scetuning/__pycache__/scetuning_components.cpython-310.pyc b/ms-swift/swift/tuners/scetuning/__pycache__/scetuning_components.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b94f9a73b5e1f73f663562fa90c4f344054a6c54
Binary files /dev/null and b/ms-swift/swift/tuners/scetuning/__pycache__/scetuning_components.cpython-310.pyc differ
diff --git a/ms-swift/swift/tuners/scetuning/scetuning_components.py b/ms-swift/swift/tuners/scetuning/scetuning_components.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b7b981d15bc394710f504ffb630fd08cb061d75
--- /dev/null
+++ b/ms-swift/swift/tuners/scetuning/scetuning_components.py
@@ -0,0 +1,127 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import math
+
+import torch
+import torch.nn as nn
+
+from swift.utils.logger import get_logger
+
+logger = get_logger()
+
+
+def detach_tensors(feats):
+    if type(feats) in [list, tuple]:
+        feats = [detach_tensors(feat) if feat is not None else None for feat in feats]
+    elif isinstance(feats, dict):
+        feats = {key: detach_tensors(val) for key, val in feats.items()}
+    elif isinstance(feats, torch.Tensor):
+        feats = feats.detach()
+    else:
+        feats = feats.detach()
+    return feats
+
+
+def probe_tensors(module, feats, name):
+    feats = detach_tensors(feats)
+    setattr(module, name, feats)
+
+
+def probe_input_pre_hook(self, args):
+    input = args[0]
+    probe_tensors(self, input, 'probe_input_data')
+    return args
+
+
+def probe_output_hook(self, args, result):
+    output = result
+    probe_tensors(self, output, 'probe_output_data')
+    return output
+
+
+def choose_weight_type(weight_type, dim):
+    if weight_type == 'gate':
+        scaling = nn.Linear(dim, 1)
+    elif weight_type == 'scale':
+        scaling = nn.Parameter(torch.Tensor(1))
+        scaling.data.fill_(1)
+    elif weight_type == 'scale_channel':
+        scaling = nn.Parameter(torch.Tensor(dim))
+        scaling.data.fill_(1)
+    elif weight_type and weight_type.startswith('scalar'):
+        scaling = float(weight_type.split('_')[-1])
+    else:
+        scaling = None
+    return scaling
+
+
+def get_weight_value(weight_type, scaling, x):
+    if weight_type in ['gate']:
+        scaling = torch.mean(torch.sigmoid(scaling(x)), dim=1).view(-1, 1, 1)
+    elif weight_type in ['scale', 'scale_channel'] or weight_type.startswith('scalar'):
+        scaling = scaling
+    else:
+        scaling = None
+    return scaling
+
+
+class SCEAdapter(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 adapter_length,
+                 adapter_type=None,
+                 adapter_weight=None,
+                 act_layer=nn.GELU,
+                 zero_init_last=True,
+                 use_bias=True):
+        super(SCEAdapter, self).__init__()
+        self.dim = dim
+        self.adapter_length = adapter_length
+        self.adapter_type = adapter_type
+        self.adapter_weight = adapter_weight
+        self.zero_init_last = zero_init_last
+        self.ln1 = nn.Linear(dim, adapter_length, bias=use_bias)
+        self.activate = act_layer()
+        self.ln2 = nn.Linear(adapter_length, dim, bias=use_bias)
+        self.init_weights()
+        self.init_scaling()
+
+    def _zero_init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.zeros_(m.weight)
+            nn.init.zeros_(m.bias)
+
+    def _kaiming_init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
+
+    def init_weights(self):
+        self._kaiming_init_weights(self.ln1)
+        if self.zero_init_last:
+            self._zero_init_weights(self.ln2)
+        else:
+            self._kaiming_init_weights(self.ln2)
+
+    def init_scaling(self):
+        if self.adapter_weight:
+            self.scaling = choose_weight_type(self.adapter_weight, self.dim)
+        else:
+            self.scaling = None
+
+    def forward(self, x, x_shortcut=None, use_shortcut=True, **kwargs):
+        if x_shortcut is None:
+            x_shortcut = x
+        x_shape = x.shape
+        if len(x_shape) == 4:
+            b, d, h, w = x_shape
+            x = x.permute(0, 2, 3, 1).reshape(b, h * w, d)
+        out = self.ln2(self.activate(self.ln1(x)))
+        if self.adapter_weight:
+            scaling = get_weight_value(self.adapter_weight, self.scaling, out)
+            out = out * scaling if scaling is not None else out
+        if len(x_shape) == 4:
+            b, d, h, w = x_shape
+            out = out.reshape(b, h, w, -1).permute(0, 3, 1, 2).contiguous()
+        if use_shortcut:
+            out = x_shortcut + out
+        return out
diff --git a/ms-swift/swift/tuners/side.py b/ms-swift/swift/tuners/side.py
new file mode 100644
index 0000000000000000000000000000000000000000..a315bcd3a9527c38d96ac34a9da59cf04e01c91c
--- /dev/null
+++ b/ms-swift/swift/tuners/side.py
@@ -0,0 +1,245 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
+import re
+import types
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from functools import partial
+from itertools import repeat
+from typing import Union
+
+import torch
+from torch import nn
+
+from swift.utils.logger import get_logger
+from swift.utils.torch_utils import find_sub_module
+from .utils import ActivationMixin, SwiftAdapter, SwiftConfig, SwiftOutput
+
+logger = get_logger()
+
+
+@dataclass
+class SideConfig(SwiftConfig):
+    """
+    The configuration class for the side module.
+
+    Side-Tuning only needs to train one side network and
+    weights the output of pre-trained model and side network.
+    'Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks'
+    by Zhang et al.(2019)
+    See https://arxiv.org/abs/1912.13503
+
+    Args:
+        target_modules: The feedforward module to be replaced, in regex format
+    """
+
+    dim: int = field(default=None, metadata={'help': 'The dimension of the hidden states'})
+
+    target_modules: str = field(
+        default=None, metadata={'help': 'The target module to be replaced, in full match format'})
+
+    side_module_name: str = field(default='fcn4', metadata={'help': 'The name of the additive side networks'})
+
+    source_hidden_pos: Union[str, int] = field(
+        default=0,
+        metadata={
+            'help': 'The position of the hidden state input to the target module, can be int (args) or str (kwargs)'
+        })
+
+    target_hidden_pos: Union[str, int] = field(
+        default=0,
+        metadata={
+            'help': 'The position of the hidden state output from the target module, can be int (args) or str (kwargs)'
+        })
+
+    def __post_init__(self):
+        from .mapping import SwiftTuners
+        self.swift_type = SwiftTuners.SIDE
+
+
+class Side(SwiftAdapter):
+
+    @staticmethod
+    def prepare_model(model: nn.Module, config: SideConfig, adapter_name: str) -> SwiftOutput:
+        """Prepare a model with `SideConfig`"""
+        module_keys = [key for key, _ in model.named_modules()]
+
+        for module_key in module_keys:
+            if re.fullmatch(config.target_modules, module_key):  # noqa
+                tgt_module = model.get_submodule(module_key)
+                logger.info(f'Matching target module [{module_key}] of type {type(tgt_module)}')
+                if isinstance(tgt_module, (nn.ModuleList, nn.ModuleDict)):
+                    raise Exception(
+                        f'Type of {type(tgt_module)} may not be supported because of its customized forward')
+
+                def _forward(self, *args, **kwargs):
+                    args_main = getattr(self, f'forward_origin_{adapter_name}')(*args, **kwargs)
+
+                    if isinstance(config.source_hidden_pos, int):
+                        x = args[config.source_hidden_pos]
+                    else:
+                        x = kwargs[config.source_hidden_pos]
+
+                    x_main = args_main[config.target_hidden_pos] \
+                        if isinstance(args_main, (tuple, list, dict)) else args_main
+                    out = getattr(self, f'side_{adapter_name}')(x, x_main)
+                    if isinstance(args_main, (tuple, list, dict)):
+                        args_main[config.target_hidden_pos] = out
+                    else:
+                        args_main = out
+                    return args_main
+
+                if isinstance(tgt_module, nn.Sequential) and not hasattr(tgt_module, 'tgt_module_keys'):
+                    tgt_module.tgt_module_keys = copy.deepcopy(list(tgt_module._modules.keys()))
+
+                    def forward_seq(self, input, *args, **kwargs):
+                        for idx, module in enumerate(self):
+                            if idx >= len(tgt_module.tgt_module_keys):
+                                continue
+                            input = module(input)
+                        return input
+
+                    setattr(tgt_module, f'forward_origin_{adapter_name}', types.MethodType(forward_seq, tgt_module))
+                else:
+                    setattr(tgt_module, f'forward_origin_{adapter_name}', tgt_module.forward)
+                tgt_module.forward = types.MethodType(_forward, tgt_module)
+                side_module = SideModule(config.dim, adapter_name, module_key, config.side_module_name)
+                setattr(tgt_module, f'side_{adapter_name}', side_module)
+                logger.info(f'Side modules(module_key): {module_key}.side_{adapter_name}')
+
+        def state_dict_callback(state_dict, adapter_name, **kwargs):
+            return {key: value for key, value in state_dict.items() if f'side_{adapter_name}' in key}
+
+        def mark_trainable_callback(model):
+            return
+
+        return SwiftOutput(
+            config=config, state_dict_callback=state_dict_callback, mark_trainable_callback=mark_trainable_callback)
+
+    @staticmethod
+    def activate_adapter(module: torch.nn.Module, adapter_name: str, activate: bool, offload: str = None):
+        modules = find_sub_module(module, f'side_{adapter_name}')
+        for _module in modules:
+            _module: ActivationMixin
+            _module: nn.Module
+            _module.set_activation(adapter_name, activate)
+            SwiftAdapter.save_memory(_module, adapter_name, _module.module_key, activate, offload)
+
+
+class SideModule(nn.Module, ActivationMixin):
+    """The implementation of vision side-tuning method.
+
+    Side-Tuning only needs to train one side network and
+    weights the output of pre-trained model and side network.
+    'Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks'
+    by Zhang et al.(2019)
+    See https://arxiv.org/abs/1912.13503
+
+    Args:
+        side_module_name: The name of the additive side networks.
+    """
+
+    def __init__(self, dim, adapter_name, module_key, side_module_name='fcn4'):
+        super(SideModule, self).__init__()
+        super(nn.Module, self).__init__(module_key)
+        self.adapter_name = adapter_name
+
+        side_module_name = side_module_name.lower()
+        if side_module_name == 'fcn4':
+            self.side_net = FCN4(out_dims=dim)
+        elif side_module_name == 'mlp':
+            self.side_net = Mlp(dim)
+        elif side_module_name == 'alexnet':
+            import torchvision
+            mm = torchvision.models.alexnet(pretrained=True)
+            self.side_net = nn.Sequential(
+                OrderedDict([('features', mm.features), ('avgpool', mm.avgpool), ('flatten', nn.Flatten()),
+                             ('fc', nn.Linear(9216, dim, bias=False))]))
+        else:
+            raise ValueError(f'Unsupported side_module_name: {side_module_name}')
+        self.alpha = nn.Parameter(torch.tensor(0.0))
+        self.mark_all_sub_modules_as_plugin()
+
+    def forward(self, x, x_main):
+        if not self.is_activated(self.adapter_name):
+            return x_main
+        alpha_squashed = torch.sigmoid(self.alpha)
+        x_side = self.side_net(x)
+        x_out = alpha_squashed * x_main + (1 - alpha_squashed) * x_side
+        return x_out
+
+
+class FCN4(nn.Module):
+    """The implementation of simple FCN4 network for side network.
+    """
+
+    def __init__(self, out_dims=-1, **kwargs):
+        super(FCN4, self).__init__(**kwargs)
+
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False, dilation=1), nn.GroupNorm(2, 16),
+            nn.ReLU())
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(16, 16, kernel_size=3, stride=2, padding=0, bias=False, dilation=1), nn.GroupNorm(2, 16),
+            nn.ReLU())
+        self.conv3 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=0, bias=False, dilation=1), nn.GroupNorm(2, 32),
+            nn.ReLU())
+        self.conv4 = nn.Sequential(
+            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=0, bias=False, dilation=1), nn.GroupNorm(2, 64),
+            nn.ReLU())
+        self.pool = nn.AdaptiveAvgPool2d((1, 1))
+        if out_dims > 0:
+            self.fc = nn.Linear(64, out_dims)
+        else:
+            self.fc = None
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = self.pool(x)
+        x = x.view(x.size(0), -1)
+        if self.fc is not None:
+            x = self.fc(x)
+        return x
+
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer.
+    """
+
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        norm_layer=None,
+        bias=True,
+        drop=0.,
+        use_conv=False,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        bias = tuple(repeat(bias, 2))
+        drop_probs = tuple(repeat(drop, 2))
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+
+        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
+        self.drop2 = nn.Dropout(drop_probs[1])
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.norm(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
diff --git a/ms-swift/swift/ui/app.py b/ms-swift/swift/ui/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..81df06f4ff32cf6e7af990980b6fd1f4a73373cb
--- /dev/null
+++ b/ms-swift/swift/ui/app.py
@@ -0,0 +1,92 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+from functools import partial
+from typing import List, Union
+
+import gradio as gr
+from packaging import version
+from transformers.utils import strtobool
+
+import swift
+from swift.llm import DeployArguments, EvalArguments, ExportArguments, RLHFArguments, SwiftPipeline, WebUIArguments
+from swift.ui.llm_eval.llm_eval import LLMEval
+from swift.ui.llm_export.llm_export import LLMExport
+from swift.ui.llm_infer.llm_infer import LLMInfer
+from swift.ui.llm_train.llm_train import LLMTrain
+
+locale_dict = {
+    'title': {
+        'zh': '🚀SWIFT: 轻量级大模型训练推理框架',
+        'en': '🚀SWIFT: Scalable lightWeight Infrastructure for Fine-Tuning and Inference'
+    },
+    'sub_title': {
+        'zh':
+        '请查看 <a href=\"https://github.com/modelscope/swift/tree/main/docs/source\" target=\"_blank\">'
+        'SWIFT 文档</a>来查看更多功能，使用SWIFT_UI_LANG=en环境变量来切换英文界面',
+        'en':
+        'Please check <a href=\"https://github.com/modelscope/swift/tree/main/docs/source_en\" target=\"_blank\">'
+        'SWIFT Documentation</a> for more usages, Use SWIFT_UI_LANG=zh variable to switch to Chinese UI',
+    },
+    'star_beggar': {
+        'zh':
+        '喜欢<a href=\"https://github.com/modelscope/swift\" target=\"_blank\">SWIFT</a>就动动手指给我们加个star吧🥺 ',
+        'en':
+        'If you like <a href=\"https://github.com/modelscope/swift\" target=\"_blank\">SWIFT</a>, '
+        'please take a few seconds to star us🥺 '
+    },
+}
+
+
+class SwiftWebUI(SwiftPipeline):
+
+    args_class = WebUIArguments
+    args: args_class
+
+    def run(self):
+        lang = os.environ.get('SWIFT_UI_LANG') or self.args.lang
+        share_env = os.environ.get('WEBUI_SHARE')
+        share = strtobool(share_env) if share_env else self.args.share
+        server = os.environ.get('WEBUI_SERVER') or self.args.server_name
+        port_env = os.environ.get('WEBUI_PORT')
+        port = int(port_env) if port_env else self.args.server_port
+        LLMTrain.set_lang(lang)
+        LLMInfer.set_lang(lang)
+        LLMExport.set_lang(lang)
+        LLMEval.set_lang(lang)
+        with gr.Blocks(title='SWIFT WebUI', theme=gr.themes.Base()) as app:
+            try:
+                _version = swift.__version__
+            except AttributeError:
+                _version = ''
+            gr.HTML(f"<h1><center>{locale_dict['title'][lang]}({_version})</center></h1>")
+            gr.HTML(f"<h3><center>{locale_dict['sub_title'][lang]}</center></h3>")
+            with gr.Tabs():
+                LLMTrain.build_ui(LLMTrain)
+                LLMInfer.build_ui(LLMInfer)
+                LLMExport.build_ui(LLMExport)
+                LLMEval.build_ui(LLMEval)
+
+            concurrent = {}
+            if version.parse(gr.__version__) < version.parse('4.0.0'):
+                concurrent = {'concurrency_count': 5}
+            app.load(
+                partial(LLMTrain.update_input_model, arg_cls=RLHFArguments),
+                inputs=[LLMTrain.element('model')],
+                outputs=[LLMTrain.element('train_record')] + list(LLMTrain.valid_elements().values()))
+            app.load(
+                partial(LLMInfer.update_input_model, arg_cls=DeployArguments, has_record=False),
+                inputs=[LLMInfer.element('model')],
+                outputs=list(LLMInfer.valid_elements().values()))
+            app.load(
+                partial(LLMExport.update_input_model, arg_cls=ExportArguments, has_record=False),
+                inputs=[LLMExport.element('model')],
+                outputs=list(LLMExport.valid_elements().values()))
+            app.load(
+                partial(LLMEval.update_input_model, arg_cls=EvalArguments, has_record=False),
+                inputs=[LLMEval.element('model')],
+                outputs=list(LLMEval.valid_elements().values()))
+        app.queue(**concurrent).launch(server_name=server, inbrowser=True, server_port=port, height=800, share=share)
+
+
+def webui_main(args: Union[List[str], WebUIArguments, None] = None):
+    return SwiftWebUI(args).main()
diff --git a/ms-swift/swift/ui/base.py b/ms-swift/swift/ui/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ca62a6fef2859964292f15e1bd4ac4fda029bbb
--- /dev/null
+++ b/ms-swift/swift/ui/base.py
@@ -0,0 +1,388 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import dataclasses
+import os
+import sys
+import time
+import typing
+from collections import OrderedDict
+from dataclasses import fields
+from datetime import datetime
+from functools import wraps
+from typing import Any, Dict, List, Type
+
+import gradio as gr
+import json
+from gradio import Accordion, Audio, Button, Checkbox, Dropdown, File, Image, Slider, Tab, TabItem, Textbox, Video
+from modelscope.hub.utils.utils import get_cache_dir
+
+from swift.llm import TEMPLATE_MAPPING, BaseArguments, get_matched_model_meta
+
+all_langs = ['zh', 'en']
+builder: Type['BaseUI'] = None
+base_builder: Type['BaseUI'] = None
+
+
+def update_data(fn):
+
+    @wraps(fn)
+    def wrapper(*args, **kwargs):
+        elem_id = kwargs.get('elem_id', None)
+        self = args[0]
+
+        if builder is not None:
+            choices = base_builder.choice(elem_id)
+            if choices:
+                choices = [str(choice) if choice is not None else None for choice in choices]
+                kwargs['choices'] = choices
+
+        if not isinstance(self, (Tab, TabItem, Accordion)) and 'interactive' not in kwargs:  # noqa
+            kwargs['interactive'] = True
+
+        if 'is_list' in kwargs:
+            self.is_list = kwargs.pop('is_list')
+
+        if base_builder and base_builder.default(elem_id) is not None and not kwargs.get('value'):
+            kwargs['value'] = base_builder.default(elem_id)
+
+        if builder is not None:
+            if elem_id in builder.locales(builder.lang):
+                values = builder.locale(elem_id, builder.lang)
+                if 'info' in values:
+                    kwargs['info'] = values['info']
+                if 'value' in values:
+                    kwargs['value'] = values['value']
+                if 'label' in values:
+                    kwargs['label'] = values['label']
+                if hasattr(builder, 'visible'):
+                    kwargs['visible'] = builder.visible
+                argument = base_builder.argument(elem_id)
+                if argument and 'label' in kwargs:
+                    kwargs['label'] = kwargs['label'] + f'({argument})'
+
+        kwargs['elem_classes'] = 'align'
+        ret = fn(self, **kwargs)
+        self.constructor_args.update(kwargs)
+
+        if builder is not None:
+            builder.element_dict[elem_id] = self
+        return ret
+
+    return wrapper
+
+
+Textbox.__init__ = update_data(Textbox.__init__)
+Dropdown.__init__ = update_data(Dropdown.__init__)
+Checkbox.__init__ = update_data(Checkbox.__init__)
+Slider.__init__ = update_data(Slider.__init__)
+TabItem.__init__ = update_data(TabItem.__init__)
+Accordion.__init__ = update_data(Accordion.__init__)
+Button.__init__ = update_data(Button.__init__)
+File.__init__ = update_data(File.__init__)
+Image.__init__ = update_data(Image.__init__)
+Video.__init__ = update_data(Video.__init__)
+Audio.__init__ = update_data(Audio.__init__)
+
+
+class BaseUI:
+
+    choice_dict: Dict[str, List] = {}
+    default_dict: Dict[str, Any] = {}
+    locale_dict: Dict[str, Dict] = {}
+    element_dict: Dict[str, Dict] = {}
+    arguments: Dict[str, str] = {}
+    sub_ui: List[Type['BaseUI']] = []
+    group: str = None
+    lang: str = all_langs[0]
+    int_regex = r'^[-+]?[0-9]+$'
+    float_regex = r'[-+]?(?:\d*\.*\d+)'
+    bool_regex = r'^(T|t)rue$|^(F|f)alse$'
+    cache_dir = os.path.join(get_cache_dir(), 'swift-web-ui')
+    os.makedirs(cache_dir, exist_ok=True)
+    quote = '\'' if sys.platform != 'win32' else '"'
+    visible = True
+    _locale = {
+        'local_dir_alert': {
+            'value': {
+                'zh': '无法识别model_type和template,请手动选择',
+                'en': 'Cannot recognize the model_type and template, please choose manually'
+            }
+        },
+    }
+
+    @classmethod
+    def build_ui(cls, base_tab: Type['BaseUI']):
+        """Build UI"""
+        global builder, base_builder
+        cls.element_dict = {}
+        old_builder = builder
+        old_base_builder = base_builder
+        builder = cls
+        base_builder = base_tab
+        cls.do_build_ui(base_tab)
+        builder = old_builder
+        base_builder = old_base_builder
+        if cls is base_tab:
+            for ui in cls.sub_ui:
+                ui.after_build_ui(base_tab)
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        pass
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        """Build UI"""
+        pass
+
+    @classmethod
+    def save_cache(cls, key, value):
+        timestamp = str(int(time.time()))
+        key = key.replace('/', '-')
+        filename = os.path.join(cls.cache_dir, key + '-' + timestamp)
+        with open(filename, 'w', encoding='utf-8') as f:
+            json.dump(value, f)
+
+    @classmethod
+    def list_cache(cls, key):
+        files = []
+        key = key.replace('/', '-')
+        for _, _, filenames in os.walk(cls.cache_dir):
+            for filename in filenames:
+                if filename.startswith(key):
+                    idx = filename.rfind('-')
+                    key, ts = filename[:idx], filename[idx + 1:]
+                    dt_object = datetime.fromtimestamp(int(ts))
+                    formatted_time = dt_object.strftime('%Y/%m/%d %H:%M:%S')
+                    files.append(formatted_time)
+        return sorted(files, reverse=True)
+
+    @classmethod
+    def load_cache(cls, key, timestamp) -> BaseArguments:
+        dt_object = datetime.strptime(timestamp, '%Y/%m/%d %H:%M:%S')
+        timestamp = int(dt_object.timestamp())
+        key = key.replace('/', '-')
+        filename = key + '-' + str(timestamp)
+        with open(os.path.join(cls.cache_dir, filename), 'r', encoding='utf-8') as f:
+            return json.load(f)
+
+    @classmethod
+    def clear_cache(cls, key):
+        key = key.replace('/', '-')
+        for _, _, filenames in os.walk(cls.cache_dir):
+            for filename in filenames:
+                if filename.startswith(key):
+                    os.remove(os.path.join(cls.cache_dir, filename))
+
+    @classmethod
+    def choice(cls, elem_id):
+        """Get choice by elem_id"""
+        for sub_ui in BaseUI.sub_ui:
+            _choice = sub_ui.choice(elem_id)
+            if _choice:
+                return _choice
+        return cls.choice_dict.get(elem_id, [])
+
+    @classmethod
+    def default(cls, elem_id):
+        """Get choice by elem_id"""
+        if elem_id in cls.default_dict:
+            return cls.default_dict.get(elem_id)
+        for sub_ui in BaseUI.sub_ui:
+            _choice = sub_ui.default(elem_id)
+            if _choice:
+                return _choice
+        return None
+
+    @classmethod
+    def locale(cls, elem_id, lang):
+        """Get locale by elem_id"""
+        return cls.locales(lang)[elem_id]
+
+    @classmethod
+    def locales(cls, lang):
+        """Get locale by lang"""
+        locales = OrderedDict()
+        for sub_ui in cls.sub_ui:
+            _locales = sub_ui.locales(lang)
+            locales.update(_locales)
+        for key, value in cls.locale_dict.items():
+            locales[key] = {k: v[lang] for k, v in value.items()}
+        return locales
+
+    @classmethod
+    def elements(cls):
+        """Get all elements"""
+        elements = OrderedDict()
+        elements.update(cls.element_dict)
+        for sub_ui in cls.sub_ui:
+            _elements = sub_ui.elements()
+            elements.update(_elements)
+        return elements
+
+    @classmethod
+    def valid_elements(cls):
+        valid_elements = OrderedDict()
+        elements = cls.elements()
+        for key, value in elements.items():
+            if isinstance(value, (Textbox, Dropdown, Slider, Checkbox)) and key != 'train_record':
+                valid_elements[key] = value
+        return valid_elements
+
+    @classmethod
+    def element_keys(cls):
+        return list(cls.elements().keys())
+
+    @classmethod
+    def valid_element_keys(cls):
+        return [
+            key for key, value in cls.elements().items()
+            if isinstance(value, (Textbox, Dropdown, Slider, Checkbox)) and key != 'train_record'
+        ]
+
+    @classmethod
+    def element(cls, elem_id):
+        """Get element by elem_id"""
+        elements = cls.elements()
+        return elements[elem_id]
+
+    @classmethod
+    def argument(cls, elem_id):
+        """Get argument by elem_id"""
+        return cls.arguments.get(elem_id)
+
+    @classmethod
+    def set_lang(cls, lang):
+        cls.lang = lang
+        for sub_ui in cls.sub_ui:
+            sub_ui.lang = lang
+
+    @staticmethod
+    def get_choices_from_dataclass(dataclass):
+        choice_dict = {}
+        for f in fields(dataclass):
+            default_value = f.default
+            if 'MISSING_TYPE' in str(default_value):
+                default_value = None
+            if 'choices' in f.metadata:
+                choice_dict[f.name] = list(f.metadata['choices'])
+            if 'Literal' in str(f.type) and typing.get_args(f.type):
+                choice_dict[f.name] = list(typing.get_args(f.type))
+            if f.name in choice_dict and default_value not in choice_dict[f.name]:
+                choice_dict[f.name].insert(0, default_value)
+        return choice_dict
+
+    @staticmethod
+    def get_default_value_from_dataclass(dataclass):
+        default_dict = {}
+        for f in fields(dataclass):
+            if f.default.__class__ is dataclasses._MISSING_TYPE:
+                default_dict[f.name] = f.default_factory()
+            else:
+                default_dict[f.name] = f.default
+            if isinstance(default_dict[f.name], list):
+                try:
+                    default_dict[f.name] = ' '.join(default_dict[f.name])
+                except TypeError:
+                    default_dict[f.name] = None
+            if not default_dict[f.name]:
+                default_dict[f.name] = None
+        return default_dict
+
+    @staticmethod
+    def get_argument_names(dataclass):
+        arguments = {}
+        for f in fields(dataclass):
+            arguments[f.name] = f'--{f.name}'
+        return arguments
+
+    @classmethod
+    def update_input_model(cls, model, allow_keys=None, has_record=True, arg_cls=BaseArguments, is_ref_model=False):
+        keys = cls.valid_element_keys()
+        if allow_keys:
+            keys = [key for key in keys if key in allow_keys]
+
+        if not model:
+            ret = [gr.update()] * (len(keys) + int(has_record))
+            if len(ret) == 1:
+                return ret[0]
+            else:
+                return ret
+
+        model_meta = get_matched_model_meta(model)
+        local_args_path = os.path.join(model, 'args.json')
+        if model_meta is None and not os.path.exists(local_args_path):
+            gr.Info(cls._locale['local_dir_alert']['value'][cls.lang])
+            ret = [gr.update()] * (len(keys) + int(has_record))
+            if len(ret) == 1:
+                return ret[0]
+            else:
+                return ret
+
+        if os.path.exists(local_args_path):
+            try:
+                if hasattr(arg_cls, 'resume_from_checkpoint'):
+                    try:
+                        args = arg_cls(resume_from_checkpoint=model, load_data_args=True)
+                    except Exception as e:
+                        if 'using `--model`' in str(e):  # TODO a dirty fix
+                            args = arg_cls(model=model, load_data_args=True)
+                        else:
+                            raise e
+                else:
+                    args = arg_cls(ckpt_dir=model, load_data_args=True)
+            except ValueError:
+                return [gr.update()] * (len(keys) + int(has_record))
+            values = []
+            for key in keys:
+                arg_value = getattr(args, key, None)
+                if arg_value and key != 'model':
+                    if key in ('torch_dtype', 'bnb_4bit_compute_dtype'):
+                        arg_value = str(arg_value).split('.')[1]
+                    if isinstance(arg_value, list) and key != 'dataset':
+                        try:
+                            arg_value = ' '.join(arg_value)
+                        except Exception:
+                            arg_value = None
+                    values.append(gr.update(value=arg_value))
+                else:
+                    values.append(gr.update())
+            ret = [gr.update(choices=[])] * int(has_record) + values
+            if len(ret) == 1:
+                return ret[0]
+            else:
+                return ret
+        else:
+            values = []
+            for key in keys:
+                if key not in ('template', 'model_type', 'ref_model_type', 'system'):
+                    values.append(gr.update())
+                elif key in ('template', 'model_type', 'ref_model_type'):
+                    if key == 'ref_model_type':
+                        if is_ref_model:
+                            values.append(gr.update(value=getattr(model_meta, 'model_type')))
+                        else:
+                            values.append(gr.update())
+                    else:
+                        values.append(gr.update(value=getattr(model_meta, key)))
+                else:
+                    values.append(gr.update(value=TEMPLATE_MAPPING[model_meta.template].default_system))
+
+        if has_record:
+            return [gr.update(choices=cls.list_cache(model))] + values
+        else:
+            if len(values) == 1:
+                return values[0]
+            return values
+
+    @classmethod
+    def update_all_settings(cls, model, train_record, base_tab):
+        if not train_record:
+            return [gr.update()] * len(cls.elements())
+        cache = cls.load_cache(model, train_record)
+        updates = []
+        for key, value in base_tab.valid_elements().items():
+            if key in cache:
+                updates.append(gr.update(value=cache[key]))
+            else:
+                updates.append(gr.update())
+        return updates
diff --git a/ms-swift/swift/ui/llm_eval/__init__.py b/ms-swift/swift/ui/llm_eval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b937315b6e719ae8289fee2908aa486222eb76c5
--- /dev/null
+++ b/ms-swift/swift/ui/llm_eval/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
diff --git a/ms-swift/swift/ui/llm_eval/eval.py b/ms-swift/swift/ui/llm_eval/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..ded9038bbae8d7a25e1bc2085bf74459fde787b5
--- /dev/null
+++ b/ms-swift/swift/ui/llm_eval/eval.py
@@ -0,0 +1,130 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+from swift.utils import get_logger
+
+logger = get_logger()
+
+
+class Eval(BaseUI):
+
+    group = 'llm_eval'
+
+    locale_dict = {
+        'eval_backend': {
+            'label': {
+                'zh': '评测后端',
+                'en': 'Eval backend'
+            },
+            'info': {
+                'zh': '选择评测后端',
+                'en': 'Select eval backend'
+            }
+        },
+        'eval_dataset': {
+            'label': {
+                'zh': '评测数据集',
+                'en': 'Evaluation dataset'
+            },
+            'info': {
+                'zh': '选择评测数据集，支持多选 (先选择评测后端)',
+                'en': 'Select eval dataset, multiple datasets supported (select eval backend first)'
+            }
+        },
+        'eval_limit': {
+            'label': {
+                'zh': '评测数据个数',
+                'en': 'Eval numbers for each dataset'
+            },
+            'info': {
+                'zh': '每个评测集的取样数',
+                'en': 'Number of rows sampled from each dataset'
+            }
+        },
+        'eval_output_dir': {
+            'label': {
+                'zh': '评测输出目录',
+                'en': 'Eval output dir'
+            },
+            'info': {
+                'zh': '评测结果的输出目录',
+                'en': 'The dir to save the eval results'
+            }
+        },
+        'custom_eval_config': {
+            'label': {
+                'zh': '自定义数据集评测配置',
+                'en': 'Custom eval config'
+            },
+            'info': {
+                'zh': '可以使用该配置评测自己的数据集，详见github文档的评测部分',
+                'en': 'Use this config to eval your own datasets, check the docs in github for details'
+            }
+        },
+        'eval_url': {
+            'label': {
+                'zh': '评测链接',
+                'en': 'The eval url'
+            },
+            'info': {
+                'zh':
+                'OpenAI样式的评测链接(如：http://localhost:8080/v1/chat/completions)，用于评测接口（模型类型输入为实际模型类型）',
+                'en':
+                'The OpenAI style link(like: http://localhost:8080/v1/chat/completions) for '
+                'evaluation(Input actual model type into model_type)'
+            }
+        },
+        'api_key': {
+            'label': {
+                'zh': '接口token',
+                'en': 'The url token'
+            },
+            'info': {
+                'zh': 'eval_url的token',
+                'en': 'The token used with eval_url'
+            }
+        },
+        'infer_backend': {
+            'label': {
+                'zh': '推理框架',
+                'en': 'Infer backend'
+            },
+        }
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        try:
+            from swift.llm.argument.eval_args import EvalArguments
+            eval_dataset_dict = EvalArguments.list_eval_dataset()
+            default_backend = EvalArguments.eval_backend
+        except Exception as e:
+            logger.warn(e)
+            eval_dataset_dict = {}
+            default_backend = None
+
+        with gr.Row():
+            gr.Dropdown(elem_id='eval_backend', choices=list(eval_dataset_dict.keys()), value=default_backend, scale=20)
+            gr.Dropdown(
+                elem_id='eval_dataset',
+                is_list=True,
+                choices=eval_dataset_dict.get(default_backend, []),
+                multiselect=True,
+                allow_custom_value=True,
+                scale=20)
+            gr.Textbox(elem_id='eval_limit', scale=20)
+            gr.Dropdown(elem_id='infer_backend', scale=20)
+        with gr.Row():
+            gr.Textbox(elem_id='custom_eval_config', scale=20)
+            gr.Textbox(elem_id='eval_output_dir', scale=20)
+            gr.Textbox(elem_id='eval_url', scale=20)
+            gr.Textbox(elem_id='api_key', scale=20)
+
+        def update_eval_dataset(backend):
+            return gr.update(choices=eval_dataset_dict[backend])
+
+        cls.element('eval_backend').change(update_eval_dataset, [cls.element('eval_backend')],
+                                           [cls.element('eval_dataset')])
diff --git a/ms-swift/swift/ui/llm_eval/model.py b/ms-swift/swift/ui/llm_eval/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..570afabf8c63d37a3d1487a97d2591102b93eefd
--- /dev/null
+++ b/ms-swift/swift/ui/llm_eval/model.py
@@ -0,0 +1,78 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from functools import partial
+from typing import Type
+
+import gradio as gr
+
+from swift.llm import TEMPLATE_MAPPING, EvalArguments, ModelType
+from swift.llm.model.register import get_all_models
+from swift.ui.base import BaseUI
+
+
+class Model(BaseUI):
+
+    group = 'llm_eval'
+
+    locale_dict = {
+        'checkpoint': {
+            'value': {
+                'zh': '训练后的模型',
+                'en': 'Trained model'
+            }
+        },
+        'model_type': {
+            'label': {
+                'zh': '选择模型类型',
+                'en': 'Select Model Type'
+            },
+            'info': {
+                'zh': 'SWIFT已支持的模型类型',
+                'en': 'Base model type supported by SWIFT'
+            }
+        },
+        'model': {
+            'label': {
+                'zh': '模型id或路径',
+                'en': 'Model id or path'
+            },
+            'info': {
+                'zh': '实际的模型id，如果是训练后的模型请填入checkpoint-xxx的目录',
+                'en': 'The actual model id or path, if is a trained model, please fill in the checkpoint-xxx dir'
+            }
+        },
+        'reset': {
+            'value': {
+                'zh': '恢复初始值',
+                'en': 'Reset to default'
+            },
+        },
+        'template': {
+            'label': {
+                'zh': '模型Prompt模板类型',
+                'en': 'Prompt template type'
+            },
+            'info': {
+                'zh': '选择匹配模型的Prompt模板',
+                'en': 'Choose the template type of the model'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Row():
+            gr.Dropdown(
+                elem_id='model',
+                scale=20,
+                choices=get_all_models(),
+                value='Qwen/Qwen2.5-7B-Instruct',
+                allow_custom_value=True)
+            gr.Dropdown(elem_id='model_type', choices=ModelType.get_model_name_list(), scale=20)
+            gr.Dropdown(elem_id='template', choices=list(TEMPLATE_MAPPING.keys()), scale=20)
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        cls.element('model').change(
+            partial(cls.update_input_model, arg_cls=EvalArguments, has_record=False),
+            inputs=[cls.element('model')],
+            outputs=list(cls.valid_elements().values()))
diff --git a/ms-swift/swift/ui/llm_export/llm_export.py b/ms-swift/swift/ui/llm_export/llm_export.py
new file mode 100644
index 0000000000000000000000000000000000000000..b71ccf6d7f3d12cf5cd279bff716d2b9557a4373
--- /dev/null
+++ b/ms-swift/swift/ui/llm_export/llm_export.py
@@ -0,0 +1,191 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import re
+import sys
+import time
+from datetime import datetime
+from functools import partial
+from typing import Type
+
+import gradio as gr
+import json
+import torch
+from json import JSONDecodeError
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
+from swift.llm import ExportArguments
+from swift.ui.base import BaseUI
+from swift.ui.llm_export.export import Export
+from swift.ui.llm_export.model import Model
+from swift.ui.llm_export.runtime import ExportRuntime
+from swift.utils import get_device_count
+
+
+class LLMExport(BaseUI):
+    group = 'llm_export'
+
+    sub_ui = [Model, Export, ExportRuntime]
+
+    locale_dict = {
+        'llm_export': {
+            'label': {
+                'zh': 'LLM导出',
+                'en': 'LLM export',
+            }
+        },
+        'more_params': {
+            'label': {
+                'zh': '更多参数',
+                'en': 'More params'
+            },
+            'info': {
+                'zh': '以json格式或--xxx xxx命令行格式填入',
+                'en': 'Fill in with json format or --xxx xxx cmd format'
+            }
+        },
+        'export': {
+            'value': {
+                'zh': '开始导出',
+                'en': 'Begin Export'
+            },
+        },
+        'gpu_id': {
+            'label': {
+                'zh': '选择可用GPU',
+                'en': 'Choose GPU'
+            },
+            'info': {
+                'zh': '选择使用的GPU号，如CUDA不可用只能选择CPU',
+                'en': 'Select GPU to export'
+            }
+        },
+    }
+
+    choice_dict = BaseUI.get_choices_from_dataclass(ExportArguments)
+    default_dict = BaseUI.get_default_value_from_dataclass(ExportArguments)
+    arguments = BaseUI.get_argument_names(ExportArguments)
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.TabItem(elem_id='llm_export', label=''):
+            default_device = 'cpu'
+            device_count = get_device_count()
+            if device_count > 0:
+                default_device = '0'
+            with gr.Blocks():
+                Model.build_ui(base_tab)
+                Export.build_ui(base_tab)
+                ExportRuntime.build_ui(base_tab)
+                with gr.Row():
+                    gr.Textbox(elem_id='more_params', lines=4, scale=20)
+                    gr.Button(elem_id='export', scale=2, variant='primary')
+                gr.Dropdown(
+                    elem_id='gpu_id',
+                    multiselect=True,
+                    choices=[str(i) for i in range(device_count)] + ['cpu'],
+                    value=default_device,
+                    scale=8)
+
+                cls.element('export').click(
+                    cls.export_model, list(base_tab.valid_elements().values()),
+                    [cls.element('runtime_tab'), cls.element('running_tasks')])
+
+                base_tab.element('running_tasks').change(
+                    partial(ExportRuntime.task_changed, base_tab=base_tab), [base_tab.element('running_tasks')],
+                    list(base_tab.valid_elements().values()) + [cls.element('log')])
+                ExportRuntime.element('kill_task').click(
+                    ExportRuntime.kill_task,
+                    [ExportRuntime.element('running_tasks')],
+                    [ExportRuntime.element('running_tasks')] + [ExportRuntime.element('log')],
+                )
+
+    @classmethod
+    def export(cls, *args):
+        export_args = cls.get_default_value_from_dataclass(ExportArguments)
+        kwargs = {}
+        kwargs_is_list = {}
+        other_kwargs = {}
+        more_params = {}
+        more_params_cmd = ''
+        keys = cls.valid_element_keys()
+        for key, value in zip(keys, args):
+            compare_value = export_args.get(key)
+            compare_value_arg = str(compare_value) if not isinstance(compare_value, (list, dict)) else compare_value
+            compare_value_ui = str(value) if not isinstance(value, (list, dict)) else value
+            if key in export_args and compare_value_ui != compare_value_arg and value:
+                if isinstance(value, str) and re.fullmatch(cls.int_regex, value):
+                    value = int(value)
+                elif isinstance(value, str) and re.fullmatch(cls.float_regex, value):
+                    value = float(value)
+                elif isinstance(value, str) and re.fullmatch(cls.bool_regex, value):
+                    value = True if value.lower() == 'true' else False
+                kwargs[key] = value if not isinstance(value, list) else ' '.join(value)
+                kwargs_is_list[key] = isinstance(value, list) or getattr(cls.element(key), 'is_list', False)
+            else:
+                other_kwargs[key] = value
+            if key == 'more_params' and value:
+                try:
+                    more_params = json.loads(value)
+                except (JSONDecodeError or TypeError):
+                    more_params_cmd = value
+
+        kwargs.update(more_params)
+        model = kwargs.get('model')
+        if os.path.exists(model) and os.path.exists(os.path.join(model, 'args.json')):
+            kwargs['ckpt_dir'] = kwargs.pop('model')
+        export_args = ExportArguments(
+            **{
+                key: value.split(' ') if key in kwargs_is_list and kwargs_is_list[key] else value
+                for key, value in kwargs.items()
+            })
+        params = ''
+        sep = f'{cls.quote} {cls.quote}'
+        for e in kwargs:
+            if isinstance(kwargs[e], list):
+                params += f'--{e} {cls.quote}{sep.join(kwargs[e])}{cls.quote} '
+            elif e in kwargs_is_list and kwargs_is_list[e]:
+                all_args = [arg for arg in kwargs[e].split(' ') if arg.strip()]
+                params += f'--{e} {cls.quote}{sep.join(all_args)}{cls.quote} '
+            else:
+                params += f'--{e} {cls.quote}{kwargs[e]}{cls.quote} '
+        params += more_params_cmd + ' '
+        devices = other_kwargs['gpu_id']
+        devices = [d for d in devices if d]
+        assert (len(devices) == 1 or 'cpu' not in devices)
+        gpus = ','.join(devices)
+        cuda_param = ''
+        if gpus != 'cpu':
+            if is_torch_npu_available():
+                cuda_param = f'ASCEND_RT_VISIBLE_DEVICES={gpus}'
+            elif is_torch_cuda_available():
+                cuda_param = f'CUDA_VISIBLE_DEVICES={gpus}'
+            else:
+                cuda_param = ''
+        now = datetime.now()
+        time_str = f'{now.year}{now.month}{now.day}{now.hour}{now.minute}{now.second}'
+        file_path = f'output/{export_args.model_type}-{time_str}'
+        if not os.path.exists(file_path):
+            os.makedirs(file_path, exist_ok=True)
+        log_file = os.path.join(os.getcwd(), f'{file_path}/run_export.log')
+        export_args.log_file = log_file
+        params += f'--log_file "{log_file}" '
+        params += '--ignore_args_error true '
+        additional_param = ''
+        if export_args.quant_method == 'gptq':
+            additional_param = 'OMP_NUM_THREADS=14'
+        if sys.platform == 'win32':
+            if cuda_param:
+                cuda_param = f'set {cuda_param} && '
+            if additional_param:
+                additional_param = f'set {additional_param} && '
+            run_command = f'{cuda_param}{additional_param}start /b swift export {params} > {log_file} 2>&1'
+        else:
+            run_command = f'{cuda_param} {additional_param} nohup swift export {params} > {log_file} 2>&1 &'
+        return run_command, export_args, log_file
+
+    @classmethod
+    def export_model(cls, *args):
+        run_command, export_args, log_file = cls.export(*args)
+        os.system(run_command)
+        time.sleep(2)
+        return gr.update(open=True), ExportRuntime.refresh_tasks(log_file)
diff --git a/ms-swift/swift/ui/llm_export/model.py b/ms-swift/swift/ui/llm_export/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d42862f71ded65990b2104b7dda4d625a0953544
--- /dev/null
+++ b/ms-swift/swift/ui/llm_export/model.py
@@ -0,0 +1,83 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from functools import partial
+from typing import Type
+
+import gradio as gr
+
+from swift.llm import TEMPLATE_MAPPING, ExportArguments, ModelType
+from swift.llm.model.register import get_all_models
+from swift.ui.base import BaseUI
+
+
+class Model(BaseUI):
+
+    group = 'llm_export'
+
+    locale_dict = {
+        'checkpoint': {
+            'value': {
+                'zh': '训练后的模型',
+                'en': 'Trained model'
+            }
+        },
+        'model_type': {
+            'label': {
+                'zh': '选择模型类型',
+                'en': 'Select Model Type'
+            },
+            'info': {
+                'zh': 'SWIFT已支持的模型类型',
+                'en': 'Base model type supported by SWIFT'
+            }
+        },
+        'model': {
+            'label': {
+                'zh': '模型id或路径',
+                'en': 'Model id or path'
+            },
+            'info': {
+                'zh': '实际的模型id，如果是训练后的模型请填入checkpoint-xxx的目录',
+                'en': 'The actual model id or path, if is a trained model, please fill in the checkpoint-xxx dir'
+            }
+        },
+        'reset': {
+            'value': {
+                'zh': '恢复初始值',
+                'en': 'Reset to default'
+            },
+        },
+        'template': {
+            'label': {
+                'zh': '模型Prompt模板类型',
+                'en': 'Prompt template type'
+            },
+            'info': {
+                'zh': '选择匹配模型的Prompt模板',
+                'en': 'Choose the template type of the model'
+            }
+        },
+    }
+
+    ignored_models = ['int1', 'int2', 'int4', 'int8', 'awq', 'gptq', 'bnb', 'eetq', 'aqlm', 'hqq']
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Row():
+            all_models = [
+                model for model in get_all_models() if not any([ignored in model for ignored in cls.ignored_models])
+            ]
+            gr.Dropdown(
+                elem_id='model',
+                scale=20,
+                choices=all_models,
+                value='Qwen/Qwen2.5-7B-Instruct',
+                allow_custom_value=True)
+            gr.Dropdown(elem_id='model_type', choices=ModelType.get_model_name_list(), scale=20)
+            gr.Dropdown(elem_id='template', choices=list(TEMPLATE_MAPPING.keys()), scale=20)
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        cls.element('model').change(
+            partial(cls.update_input_model, arg_cls=ExportArguments, has_record=False),
+            inputs=[cls.element('model')],
+            outputs=list(cls.valid_elements().values()))
diff --git a/ms-swift/swift/ui/llm_export/runtime.py b/ms-swift/swift/ui/llm_export/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..f34ac4dfb0e917b2a9e1d9c3fdeb635c62315275
--- /dev/null
+++ b/ms-swift/swift/ui/llm_export/runtime.py
@@ -0,0 +1,75 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from swift.ui.llm_infer.runtime import Runtime
+from swift.utils import get_logger
+
+logger = get_logger()
+
+
+class ExportRuntime(Runtime):
+
+    group = 'llm_export'
+
+    cmd = 'export'
+
+    locale_dict = {
+        'runtime_tab': {
+            'label': {
+                'zh': '运行时',
+                'en': 'Runtime'
+            },
+        },
+        'running_cmd': {
+            'label': {
+                'zh': '运行命令',
+                'en': 'Command line'
+            },
+            'info': {
+                'zh': '执行的实际命令',
+                'en': 'The actual command'
+            }
+        },
+        'show_log': {
+            'value': {
+                'zh': '展示导出状态',
+                'en': 'Show export status'
+            },
+        },
+        'stop_show_log': {
+            'value': {
+                'zh': '停止展示',
+                'en': 'Stop showing running status'
+            },
+        },
+        'log': {
+            'label': {
+                'zh': '日志输出',
+                'en': 'Logging content'
+            },
+            'info': {
+                'zh': '如果日志无更新请再次点击"展示日志内容"',
+                'en': 'Please press "Show log" if the log content is not updating'
+            }
+        },
+        'running_tasks': {
+            'label': {
+                'zh': '运行中导出任务',
+                'en': 'Running export task'
+            },
+            'info': {
+                'zh': '所有的swift export命令启动的任务',
+                'en': 'All tasks started by swift export'
+            }
+        },
+        'refresh_tasks': {
+            'value': {
+                'zh': '找回导出任务',
+                'en': 'Find export'
+            },
+        },
+        'kill_task': {
+            'value': {
+                'zh': '杀死导出任务',
+                'en': 'Kill export'
+            },
+        },
+    }
diff --git a/ms-swift/swift/ui/llm_infer/__init__.py b/ms-swift/swift/ui/llm_infer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b937315b6e719ae8289fee2908aa486222eb76c5
--- /dev/null
+++ b/ms-swift/swift/ui/llm_infer/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
diff --git a/ms-swift/swift/ui/llm_infer/generate.py b/ms-swift/swift/ui/llm_infer/generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..b83b212a95b2efb2c981522e96641f565bb61f05
--- /dev/null
+++ b/ms-swift/swift/ui/llm_infer/generate.py
@@ -0,0 +1,65 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class Generate(BaseUI):
+
+    group = 'llm_infer'
+
+    locale_dict = {
+        'max_new_tokens': {
+            'label': {
+                'zh': '生成序列最大长度',
+                'en': 'Max new tokens'
+            },
+        },
+        'temperature': {
+            'label': {
+                'zh': 'temperature',
+                'en': 'temperature'
+            },
+        },
+        'top_k': {
+            'label': {
+                'zh': 'top_k',
+                'en': 'top_k'
+            },
+        },
+        'top_p': {
+            'label': {
+                'zh': 'top_p',
+                'en': 'top_p'
+            },
+        },
+        'repetition_penalty': {
+            'label': {
+                'zh': 'repetition_penalty',
+                'en': 'repetition_penalty'
+            },
+        },
+        'system': {
+            'label': {
+                'zh': 'system字段',
+                'en': 'system'
+            },
+            'info': {
+                'zh': 'system字段支持在加载模型后修改',
+                'en': 'system can be modified after the model weights loaded'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Row():
+            gr.Textbox(elem_id='max_new_tokens', lines=1, value='2048')
+            gr.Slider(elem_id='temperature', minimum=0.0, maximum=10, step=0.1, value=0.3)
+            gr.Slider(elem_id='top_k', minimum=1, maximum=100, step=5, value=20)
+            gr.Slider(elem_id='top_p', minimum=0.0, maximum=1.0, step=0.05, value=0.7)
+            gr.Slider(elem_id='repetition_penalty', minimum=0.0, maximum=10, step=0.05, value=1.05)
+        with gr.Row():
+            gr.Textbox(elem_id='system', lines=4, scale=20)
diff --git a/ms-swift/swift/ui/llm_infer/llm_infer.py b/ms-swift/swift/ui/llm_infer/llm_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..70480631879158441604e7d9034b8beb048f3181
--- /dev/null
+++ b/ms-swift/swift/ui/llm_infer/llm_infer.py
@@ -0,0 +1,396 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import re
+import signal
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime
+from functools import partial
+from typing import List, Type
+
+import gradio as gr
+import json
+import torch
+from json import JSONDecodeError
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
+from swift.llm import DeployArguments, InferArguments, InferClient, InferRequest, RequestConfig
+from swift.ui.base import BaseUI
+from swift.ui.llm_infer.model import Model
+from swift.ui.llm_infer.runtime import Runtime
+from swift.utils import get_device_count, get_logger
+
+logger = get_logger()
+
+
+class LLMInfer(BaseUI):
+
+    group = 'llm_infer'
+
+    is_multimodal = True
+
+    sub_ui = [Model, Runtime]
+
+    locale_dict = {
+        'generate_alert': {
+            'value': {
+                'zh': '请先部署模型',
+                'en': 'Please deploy model first',
+            }
+        },
+        'port': {
+            'label': {
+                'zh': '端口',
+                'en': 'port'
+            },
+        },
+        'llm_infer': {
+            'label': {
+                'zh': 'LLM推理',
+                'en': 'LLM Inference',
+            }
+        },
+        'load_alert': {
+            'value': {
+                'zh': '部署中，请点击"展示部署状态"查看',
+                'en': 'Start to deploy model, '
+                'please Click "Show running '
+                'status" to view details',
+            }
+        },
+        'loaded_alert': {
+            'value': {
+                'zh': '模型加载完成',
+                'en': 'Model loaded'
+            }
+        },
+        'port_alert': {
+            'value': {
+                'zh': '该端口已被占用',
+                'en': 'The port has been occupied'
+            }
+        },
+        'chatbot': {
+            'value': {
+                'zh': '对话框',
+                'en': 'Chat bot'
+            },
+        },
+        'infer_model_type': {
+            'label': {
+                'zh': 'Lora模块',
+                'en': 'Lora module'
+            },
+            'info': {
+                'zh': '发送给server端哪个LoRA，默认为`default`',
+                'en': 'Which LoRA to use on server, default value is `default`'
+            }
+        },
+        'prompt': {
+            'label': {
+                'zh': '请输入：',
+                'en': 'Input:'
+            },
+        },
+        'clear_history': {
+            'value': {
+                'zh': '清除对话信息',
+                'en': 'Clear history'
+            },
+        },
+        'submit': {
+            'value': {
+                'zh': '🚀 发送',
+                'en': '🚀 Send'
+            },
+        },
+        'gpu_id': {
+            'label': {
+                'zh': '选择可用GPU',
+                'en': 'Choose GPU'
+            },
+            'info': {
+                'zh': '选择训练使用的GPU号，如CUDA不可用只能选择CPU',
+                'en': 'Select GPU to train'
+            }
+        },
+    }
+
+    choice_dict = BaseUI.get_choices_from_dataclass(InferArguments)
+    default_dict = BaseUI.get_default_value_from_dataclass(InferArguments)
+    arguments = BaseUI.get_argument_names(InferArguments)
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.TabItem(elem_id='llm_infer', label=''):
+            default_device = 'cpu'
+            device_count = get_device_count()
+            if device_count > 0:
+                default_device = '0'
+            with gr.Blocks():
+                infer_request = gr.State(None)
+                Model.build_ui(base_tab)
+                Runtime.build_ui(base_tab)
+                with gr.Row():
+                    gr.Dropdown(
+                        elem_id='gpu_id',
+                        multiselect=True,
+                        choices=[str(i) for i in range(device_count)] + ['cpu'],
+                        value=default_device,
+                        scale=8)
+                    infer_model_type = gr.Textbox(elem_id='infer_model_type', scale=4)
+                    gr.Textbox(elem_id='port', lines=1, value='8000', scale=4)
+                chatbot = gr.Chatbot(elem_id='chatbot', elem_classes='control-height')
+                with gr.Row():
+                    prompt = gr.Textbox(elem_id='prompt', lines=1, interactive=True)
+                    with gr.Tabs(visible=cls.is_multimodal):
+                        with gr.TabItem(label='Image'):
+                            image = gr.Image(type='filepath')
+                        with gr.TabItem(label='Video'):
+                            video = gr.Video()
+                        with gr.TabItem(label='Audio'):
+                            audio = gr.Audio(type='filepath')
+
+                with gr.Row():
+                    clear_history = gr.Button(elem_id='clear_history')
+                    submit = gr.Button(elem_id='submit')
+
+                cls.element('load_checkpoint').click(
+                    cls.deploy_model, list(base_tab.valid_elements().values()),
+                    [cls.element('runtime_tab'), cls.element('running_tasks')])
+                submit.click(
+                    cls.send_message,
+                    inputs=[
+                        cls.element('running_tasks'),
+                        cls.element('template'), prompt, image, video, audio, infer_request, infer_model_type,
+                        cls.element('system'),
+                        cls.element('max_new_tokens'),
+                        cls.element('temperature'),
+                        cls.element('top_k'),
+                        cls.element('top_p'),
+                        cls.element('repetition_penalty')
+                    ],
+                    outputs=[prompt, chatbot, image, video, audio, infer_request],
+                    queue=True)
+
+                clear_history.click(
+                    fn=cls.clear_session, inputs=[], outputs=[prompt, chatbot, image, video, audio, infer_request])
+
+                base_tab.element('running_tasks').change(
+                    partial(Runtime.task_changed, base_tab=base_tab), [base_tab.element('running_tasks')],
+                    list(cls.valid_elements().values()) + [cls.element('log')])
+                Runtime.element('kill_task').click(
+                    Runtime.kill_task,
+                    [Runtime.element('running_tasks')],
+                    [Runtime.element('running_tasks')] + [Runtime.element('log')],
+                )
+
+    @classmethod
+    def deploy(cls, *args):
+        deploy_args = cls.get_default_value_from_dataclass(DeployArguments)
+        kwargs = {}
+        kwargs_is_list = {}
+        other_kwargs = {}
+        more_params = {}
+        more_params_cmd = ''
+        keys = cls.valid_element_keys()
+        for key, value in zip(keys, args):
+            compare_value = deploy_args.get(key)
+            compare_value_arg = str(compare_value) if not isinstance(compare_value, (list, dict)) else compare_value
+            compare_value_ui = str(value) if not isinstance(value, (list, dict)) else value
+            if key in deploy_args and compare_value_ui != compare_value_arg and value:
+                if isinstance(value, str) and re.fullmatch(cls.int_regex, value):
+                    value = int(value)
+                elif isinstance(value, str) and re.fullmatch(cls.float_regex, value):
+                    value = float(value)
+                elif isinstance(value, str) and re.fullmatch(cls.bool_regex, value):
+                    value = True if value.lower() == 'true' else False
+                kwargs[key] = value if not isinstance(value, list) else ' '.join(value)
+                kwargs_is_list[key] = isinstance(value, list) or getattr(cls.element(key), 'is_list', False)
+            else:
+                other_kwargs[key] = value
+            if key == 'more_params' and value:
+                try:
+                    more_params = json.loads(value)
+                except (JSONDecodeError or TypeError):
+                    more_params_cmd = value
+
+        kwargs.update(more_params)
+        model = kwargs.get('model')
+        if os.path.exists(model) and os.path.exists(os.path.join(model, 'args.json')):
+            kwargs['ckpt_dir'] = kwargs.pop('model')
+            with open(os.path.join(kwargs['ckpt_dir'], 'args.json'), 'r', encoding='utf-8') as f:
+                _json = json.load(f)
+                kwargs['model_type'] = _json['model_type']
+                kwargs['train_type'] = _json['train_type']
+        deploy_args = DeployArguments(
+            **{
+                key: value.split(' ') if key in kwargs_is_list and kwargs_is_list[key] else value
+                for key, value in kwargs.items()
+            })
+        if deploy_args.port in Runtime.get_all_ports():
+            raise gr.Error(cls.locale('port_alert', cls.lang)['value'])
+        params = ''
+        sep = f'{cls.quote} {cls.quote}'
+        for e in kwargs:
+            if isinstance(kwargs[e], list):
+                params += f'--{e} {cls.quote}{sep.join(kwargs[e])}{cls.quote} '
+            elif e in kwargs_is_list and kwargs_is_list[e]:
+                all_args = [arg for arg in kwargs[e].split(' ') if arg.strip()]
+                params += f'--{e} {cls.quote}{sep.join(all_args)}{cls.quote} '
+            else:
+                params += f'--{e} {cls.quote}{kwargs[e]}{cls.quote} '
+        if 'port' not in kwargs:
+            params += f'--port "{deploy_args.port}" '
+        params += more_params_cmd + ' '
+        devices = other_kwargs['gpu_id']
+        devices = [d for d in devices if d]
+        assert (len(devices) == 1 or 'cpu' not in devices)
+        gpus = ','.join(devices)
+        cuda_param = ''
+        if gpus != 'cpu':
+            if is_torch_npu_available():
+                cuda_param = f'ASCEND_RT_VISIBLE_DEVICES={gpus}'
+            elif is_torch_cuda_available():
+                cuda_param = f'CUDA_VISIBLE_DEVICES={gpus}'
+            else:
+                cuda_param = ''
+        now = datetime.now()
+        time_str = f'{now.year}{now.month}{now.day}{now.hour}{now.minute}{now.second}'
+        file_path = f'output/{deploy_args.model_type}-{time_str}'
+        if not os.path.exists(file_path):
+            os.makedirs(file_path, exist_ok=True)
+        log_file = os.path.join(os.getcwd(), f'{file_path}/run_deploy.log')
+        deploy_args.log_file = log_file
+        params += f'--log_file "{log_file}" '
+        params += '--ignore_args_error true '
+        if sys.platform == 'win32':
+            if cuda_param:
+                cuda_param = f'set {cuda_param} && '
+            run_command = f'{cuda_param}start /b swift deploy {params} > {log_file} 2>&1'
+        else:
+            run_command = f'{cuda_param} nohup swift deploy {params} > {log_file} 2>&1 &'
+        return run_command, deploy_args, log_file
+
+    @classmethod
+    def deploy_model(cls, *args):
+        run_command, deploy_args, log_file = cls.deploy(*args)
+        logger.info(f'Running deployment command: {run_command}')
+        os.system(run_command)
+        gr.Info(cls.locale('load_alert', cls.lang)['value'])
+        time.sleep(2)
+        running_task = Runtime.refresh_tasks(log_file)
+        return gr.update(open=True), running_task
+
+    @classmethod
+    def register_clean_hook(cls):
+        signal.signal(signal.SIGINT, LLMInfer.signal_handler)
+        if os.name != 'nt':
+            signal.signal(signal.SIGTERM, LLMInfer.signal_handler)
+
+    @staticmethod
+    def signal_handler(*args, **kwargs):
+        LLMInfer.clean_deployment()
+        sys.exit(0)
+
+    @classmethod
+    def clear_session(cls):
+        return '', [], gr.update(value=None), gr.update(value=None), gr.update(value=None), []
+
+    @classmethod
+    def _replace_tag_with_media(cls, infer_request: InferRequest):
+        total_history = []
+        messages = deepcopy(infer_request.messages)
+        if messages[0]['role'] == 'system':
+            messages.pop(0)
+        for i in range(0, len(messages), 2):
+            slices = messages[i:i + 2]
+            if len(slices) == 2:
+                user, assistant = slices
+            else:
+                user = slices[0]
+                assistant = {'role': 'assistant', 'content': None}
+            user['content'] = (user['content'] or '').replace('<image>', '').replace('<video>',
+                                                                                     '').replace('<audio>', '').strip()
+            for media in user['medias']:
+                total_history.append([(media, ), None])
+            if user['content'] or assistant['content']:
+                total_history.append((user['content'], assistant['content']))
+        return total_history
+
+    @classmethod
+    def agent_type(cls, response):
+        if not response:
+            return None
+        if response.lower().endswith('observation:'):
+            return 'react'
+        if 'observation:' not in response.lower() and 'action input:' in response.lower():
+            return 'toolbench'
+        return None
+
+    @classmethod
+    def send_message(cls, running_task, template_type, prompt: str, image, video, audio, infer_request: InferRequest,
+                     infer_model_type, system, max_new_tokens, temperature, top_k, top_p, repetition_penalty):
+
+        if not infer_request:
+            infer_request = InferRequest(messages=[])
+        if system:
+            if not infer_request.messages or infer_request.messages[0]['role'] != 'system':
+                infer_request.messages.insert(0, {'role': 'system', 'content': system})
+            else:
+                infer_request.messages[0]['content'] = system
+        if not infer_request.messages or infer_request.messages[-1]['role'] != 'user':
+            infer_request.messages.append({'role': 'user', 'content': '', 'medias': []})
+        media = image or video or audio
+        media_type = 'images' if image else 'videos' if video else 'audios'
+        if media:
+            _saved_medias: List = getattr(infer_request, media_type)
+            if not _saved_medias or _saved_medias[-1] != media:
+                _saved_medias.append(media)
+                infer_request.messages[-1]['content'] = infer_request.messages[-1]['content'] + f'<{media_type[:-1]}>'
+                infer_request.messages[-1]['medias'].append(media)
+
+        if not prompt:
+            yield '', cls._replace_tag_with_media(infer_request), gr.update(value=None), gr.update(
+                value=None), gr.update(value=None), infer_request
+            return
+        else:
+            infer_request.messages[-1]['content'] = infer_request.messages[-1]['content'] + prompt
+
+        _, args = Runtime.parse_info_from_cmdline(running_task)
+        request_config = RequestConfig(
+            temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty)
+        request_config.stream = True
+        request_config.stop = ['Observation:']
+        request_config.max_tokens = max_new_tokens
+        stream_resp_with_history = ''
+        response = ''
+        i = len(infer_request.messages) - 1
+        for i in range(len(infer_request.messages) - 1, -1, -1):
+            if infer_request.messages[i]['role'] == 'assistant':
+                response = infer_request.messages[i]['content']
+        agent_type = cls.agent_type(response)
+        if i != len(infer_request.messages) - 1 and agent_type == 'toolbench':
+            infer_request.messages[i + 1]['role'] = 'tool'
+
+        chat = not template_type.endswith('generation')
+        _infer_request = deepcopy(infer_request)
+        for m in _infer_request.messages:
+            if 'medias' in m:
+                m.pop('medias')
+        model_kwargs = {}
+        if infer_model_type:
+            model_kwargs = {'model': infer_model_type}
+        gen_list = InferClient(
+            port=args['port'], ).infer(
+                infer_requests=[_infer_request], request_config=request_config, **model_kwargs)
+        if infer_request.messages[-1]['role'] != 'assistant':
+            infer_request.messages.append({'role': 'assistant', 'content': ''})
+        for chunk in gen_list[0]:
+            if chunk is None:
+                continue
+            stream_resp_with_history += chunk.choices[0].delta.content if chat else chunk.choices[0].text
+            infer_request.messages[-1]['content'] = stream_resp_with_history
+            yield '', cls._replace_tag_with_media(infer_request), gr.update(value=None), gr.update(
+                value=None), gr.update(value=None), infer_request
diff --git a/ms-swift/swift/ui/llm_infer/model.py b/ms-swift/swift/ui/llm_infer/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b8d577704be28122def085f226185a0d8d56df6
--- /dev/null
+++ b/ms-swift/swift/ui/llm_infer/model.py
@@ -0,0 +1,126 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from functools import partial
+from typing import Type
+
+import gradio as gr
+
+from swift.llm import TEMPLATE_MAPPING, DeployArguments, ModelType
+from swift.llm.model.register import get_all_models
+from swift.ui.base import BaseUI
+from swift.ui.llm_infer.generate import Generate
+
+
+class Model(BaseUI):
+
+    llm_train = 'llm_infer'
+
+    sub_ui = [Generate]
+
+    locale_dict = {
+        'model_type': {
+            'label': {
+                'zh': '选择模型类型',
+                'en': 'Select Model Type'
+            },
+            'info': {
+                'zh': 'SWIFT已支持的模型类型',
+                'en': 'Base model type supported by SWIFT'
+            }
+        },
+        'load_checkpoint': {
+            'value': {
+                'zh': '部署模型',
+                'en': 'Deploy model',
+            }
+        },
+        'model': {
+            'label': {
+                'zh': '模型id或路径',
+                'en': 'Model id or path'
+            },
+            'info': {
+                'zh': '实际的模型id，如果是训练后的模型请填入checkpoint-xxx的目录',
+                'en': 'The actual model id or path, if is a trained model, please fill in the checkpoint-xxx dir'
+            }
+        },
+        'template': {
+            'label': {
+                'zh': '模型Prompt模板类型',
+                'en': 'Prompt template type'
+            },
+            'info': {
+                'zh': '选择匹配模型的Prompt模板',
+                'en': 'Choose the template type of the model'
+            }
+        },
+        'merge_lora': {
+            'label': {
+                'zh': '合并lora',
+                'en': 'merge lora'
+            },
+            'info': {
+                'zh': '仅在sft_type=lora时可用',
+                'en': 'Only available when sft_type=lora'
+            }
+        },
+        'lora_modules': {
+            'label': {
+                'zh': '外部lora模块',
+                'en': 'More lora modules'
+            },
+            'info': {
+                'zh': '空格分割的name=/path1/path2键值对',
+                'en': 'name=/path1/path2 split by blanks'
+            }
+        },
+        'more_params': {
+            'label': {
+                'zh': '更多参数',
+                'en': 'More params'
+            },
+            'info': {
+                'zh': '以json格式或--xxx xxx命令行格式填入',
+                'en': 'Fill in with json format or --xxx xxx cmd format'
+            }
+        },
+        'reset': {
+            'value': {
+                'zh': '恢复初始值',
+                'en': 'Reset to default'
+            },
+        },
+        'infer_backend': {
+            'label': {
+                'zh': '推理框架',
+                'en': 'Infer backend'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Row():
+            gr.Dropdown(
+                elem_id='model',
+                scale=20,
+                choices=get_all_models(),
+                value='Qwen/Qwen2.5-7B-Instruct',
+                allow_custom_value=True)
+            gr.Dropdown(elem_id='model_type', choices=ModelType.get_model_name_list(), scale=20)
+            gr.Dropdown(elem_id='template', choices=list(TEMPLATE_MAPPING.keys()), scale=20)
+            gr.Checkbox(elem_id='merge_lora', scale=4)
+            gr.Button(elem_id='reset', scale=2)
+        with gr.Row():
+            gr.Dropdown(elem_id='infer_backend', value='pt', scale=5)
+        Generate.build_ui(base_tab)
+        with gr.Row():
+            gr.Textbox(elem_id='lora_modules', lines=1, is_list=True, scale=40)
+            gr.Textbox(elem_id='more_params', lines=1, scale=20)
+            gr.Button(elem_id='load_checkpoint', scale=2, variant='primary')
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        cls.element('model').change(
+            partial(cls.update_input_model, arg_cls=DeployArguments, has_record=False),
+            inputs=[cls.element('model')],
+            outputs=list(cls.valid_elements().values()))
diff --git a/ms-swift/swift/ui/llm_infer/runtime.py b/ms-swift/swift/ui/llm_infer/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..31a3e1d9adbebbb15f9955c416d51969f2f1f077
--- /dev/null
+++ b/ms-swift/swift/ui/llm_infer/runtime.py
@@ -0,0 +1,285 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import collections
+import os.path
+import sys
+import time
+from datetime import datetime
+from typing import Dict, List, Tuple, Type
+
+import gradio as gr
+import psutil
+from packaging import version
+
+from swift.ui.base import BaseUI
+from swift.utils import get_logger
+from swift.utils.utils import format_time
+
+logger = get_logger()
+
+
+class Runtime(BaseUI):
+    handlers: Dict[str, Tuple[List, Tuple]] = {}
+
+    group = 'llm_infer'
+
+    cmd = 'deploy'
+
+    log_event = {}
+
+    locale_dict = {
+        'runtime_tab': {
+            'label': {
+                'zh': '运行时',
+                'en': 'Runtime'
+            },
+        },
+        'running_cmd': {
+            'label': {
+                'zh': '运行命令',
+                'en': 'Command line'
+            },
+            'info': {
+                'zh': '执行的实际命令',
+                'en': 'The actual command'
+            }
+        },
+        'show_log': {
+            'value': {
+                'zh': '展示部署状态',
+                'en': 'Show running status'
+            },
+        },
+        'stop_show_log': {
+            'value': {
+                'zh': '停止展示',
+                'en': 'Stop showing running status'
+            },
+        },
+        'log': {
+            'label': {
+                'zh': '日志输出',
+                'en': 'Logging content'
+            },
+            'info': {
+                'zh': '如果日志无更新请再次点击"展示日志内容"',
+                'en': 'Please press "Show log" if the log content is not updating'
+            }
+        },
+        'running_tasks': {
+            'label': {
+                'zh': '运行中部署',
+                'en': 'Running deployments'
+            },
+            'info': {
+                'zh': '所有的swift deploy命令启动的任务',
+                'en': 'Started by swift deploy'
+            }
+        },
+        'refresh_tasks': {
+            'value': {
+                'zh': '找回部署',
+                'en': 'Find deployments'
+            },
+        },
+        'kill_task': {
+            'value': {
+                'zh': '杀死部署',
+                'en': 'Kill running task'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='runtime_tab', open=False, visible=True):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Dropdown(elem_id='running_tasks', scale=10, allow_custom_value=True)
+                    gr.Button(elem_id='refresh_tasks', scale=1, variant='primary')
+                    gr.Button(elem_id='show_log', scale=1, variant='primary')
+                    gr.Button(elem_id='stop_show_log', scale=1)
+                    gr.Button(elem_id='kill_task', scale=1)
+                with gr.Row():
+                    gr.Textbox(elem_id='log', lines=6, visible=False)
+
+                concurrency_limit = {}
+                if version.parse(gr.__version__) >= version.parse('4.0.0'):
+                    concurrency_limit = {'concurrency_limit': 5}
+                base_tab.element('show_log').click(cls.update_log, [],
+                                                   [cls.element('log')]).then(cls.wait,
+                                                                              [base_tab.element('running_tasks')],
+                                                                              [cls.element('log')], **concurrency_limit)
+
+                base_tab.element('stop_show_log').click(cls.break_log_event, [cls.element('running_tasks')], [])
+
+                base_tab.element('refresh_tasks').click(
+                    cls.refresh_tasks,
+                    [base_tab.element('running_tasks')],
+                    [base_tab.element('running_tasks')],
+                )
+
+    @classmethod
+    def break_log_event(cls, task):
+        if not task:
+            return
+        pid, all_args = cls.parse_info_from_cmdline(task)
+        cls.log_event[all_args['log_file']] = True
+
+    @classmethod
+    def update_log(cls):
+        return gr.update(visible=True)
+
+    @classmethod
+    def wait(cls, task):
+        if not task:
+            return [None]
+        _, args = cls.parse_info_from_cmdline(task)
+        log_file = args['log_file']
+        cls.log_event[log_file] = False
+        offset = 0
+        latest_data = ''
+        lines = collections.deque(maxlen=int(os.environ.get('MAX_LOG_LINES', 50)))
+        try:
+            with open(log_file, 'r', encoding='utf-8') as input:
+                input.seek(offset)
+                fail_cnt = 0
+                while True:
+                    try:
+                        latest_data += input.read()
+                    except UnicodeDecodeError:
+                        continue
+                    if not latest_data:
+                        time.sleep(0.5)
+                        fail_cnt += 1
+                        if fail_cnt > 50:
+                            break
+
+                    if cls.log_event.get(log_file, False):
+                        cls.log_event[log_file] = False
+                        break
+
+                    if '\n' not in latest_data:
+                        continue
+                    latest_lines = latest_data.split('\n')
+                    if latest_data[-1] != '\n':
+                        latest_data = latest_lines[-1]
+                        latest_lines = latest_lines[:-1]
+                    else:
+                        latest_data = ''
+                    lines.extend(latest_lines)
+                    yield '\n'.join(lines)
+        except IOError:
+            pass
+
+    @classmethod
+    def get_all_ports(cls):
+        process_name = 'swift'
+        cmd_name = cls.cmd
+        ports = set()
+        for proc in psutil.process_iter():
+            try:
+                cmdlines = proc.cmdline()
+            except (psutil.ZombieProcess, psutil.AccessDenied, psutil.NoSuchProcess):
+                cmdlines = []
+            if any([process_name in cmdline for cmdline in cmdlines]) and any(  # noqa
+                [cmd_name == cmdline for cmdline in cmdlines]):  # noqa
+                try:
+                    ports.add(int(cls.parse_info_from_cmdline(cls.construct_running_task(proc))[1].get('port', 8000)))
+                except IndexError:
+                    pass
+        return ports
+
+    @classmethod
+    def refresh_tasks(cls, running_task=None):
+        log_file = running_task if not running_task or 'pid:' not in running_task else None
+        process_name = 'swift'
+        negative_name = 'swift.exe'
+        cmd_name = cls.cmd
+        process = []
+        selected = None
+        for proc in psutil.process_iter():
+            try:
+                cmdlines = proc.cmdline()
+            except (psutil.ZombieProcess, psutil.AccessDenied, psutil.NoSuchProcess):
+                cmdlines = []
+            if any([process_name in cmdline
+                    for cmdline in cmdlines]) and not any([negative_name in cmdline
+                                                           for cmdline in cmdlines]) and any(  # noqa
+                                                               [cmd_name == cmdline for cmdline in cmdlines]):  # noqa
+                process.append(cls.construct_running_task(proc))
+                if log_file is not None and any(  # noqa
+                    [log_file == cmdline for cmdline in cmdlines]):  # noqa
+                    selected = cls.construct_running_task(proc)
+        if not selected:
+            if running_task and running_task in process:
+                selected = running_task
+        if not selected and process:
+            selected = process[0]
+        return gr.update(choices=process, value=selected)
+
+    @staticmethod
+    def construct_running_task(proc):
+        pid = proc.pid
+        ts = time.time()
+        create_time = proc.create_time()
+        create_time_formatted = datetime.fromtimestamp(create_time).strftime('%Y-%m-%d, %H:%M')
+
+        return f'pid:{pid}/create:{create_time_formatted}' \
+               f'/running:{format_time(ts - create_time)}/cmd:{" ".join(proc.cmdline())}'
+
+    @classmethod
+    def parse_info_from_cmdline(cls, task):
+        pid = None
+        for i in range(3):
+            slash = task.find('/')
+            if i == 0:
+                pid = task[:slash].split(':')[1]
+            task = task[slash + 1:]
+        args = task.split(f'swift {cls.cmd}')[1]
+        args = [arg.strip() for arg in args.split('--') if arg.strip()]
+        all_args = {}
+        for i in range(len(args)):
+            space = args[i].find(' ')
+            splits = args[i][:space], args[i][space + 1:]
+            all_args[splits[0]] = splits[1]
+        return pid, all_args
+
+    @classmethod
+    def kill_task(cls, task):
+        if task:
+            pid, all_args = cls.parse_info_from_cmdline(task)
+            log_file = all_args['log_file']
+            if sys.platform == 'win32':
+                os.system(f'taskkill /f /t /pid "{pid}"')
+            else:
+                os.system(f'pkill -9 -f {log_file}')
+            time.sleep(1)
+            cls.break_log_event(task)
+        return [cls.refresh_tasks()] + [gr.update(value=None)]
+
+    @classmethod
+    def task_changed(cls, task, base_tab):
+        if task:
+            _, all_args = cls.parse_info_from_cmdline(task)
+        else:
+            all_args = {}
+        elements = list(base_tab.valid_elements().values())
+        ret = []
+        is_custom_path = 'ckpt_dir' in all_args
+        for e in elements:
+            if e.elem_id in all_args:
+                if isinstance(e, gr.Dropdown) and e.multiselect:
+                    arg = all_args[e.elem_id].split(' ')
+                else:
+                    if e.elem_id == 'model':
+                        if is_custom_path:
+                            arg = all_args['ckpt_dir']
+                        else:
+                            arg = all_args[e.elem_id]
+                    else:
+                        arg = all_args[e.elem_id]
+                ret.append(gr.update(value=arg))
+            else:
+                ret.append(gr.update())
+        cls.break_log_event(task)
+        return ret + [gr.update(value=None)]
diff --git a/ms-swift/swift/ui/llm_train/__init__.py b/ms-swift/swift/ui/llm_train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b937315b6e719ae8289fee2908aa486222eb76c5
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
diff --git a/ms-swift/swift/ui/llm_train/advanced.py b/ms-swift/swift/ui/llm_train/advanced.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4583316c815e800f85a852f274e51d1b95790dc
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/advanced.py
@@ -0,0 +1,164 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class Advanced(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'advanced_param': {
+            'label': {
+                'zh': '高级参数设置',
+                'en': 'Advanced settings'
+            },
+        },
+        'optim': {
+            'label': {
+                'zh': 'Optimizer类型',
+                'en': 'The Optimizer type'
+            },
+            'info': {
+                'zh': '设置Optimizer类型',
+                'en': 'Set the Optimizer type'
+            }
+        },
+        'weight_decay': {
+            'label': {
+                'zh': '权重衰减',
+                'en': 'Weight decay'
+            },
+            'info': {
+                'zh': '设置weight decay',
+                'en': 'Set the weight decay'
+            }
+        },
+        'logging_steps': {
+            'label': {
+                'zh': '日志打印步数',
+                'en': 'Logging steps'
+            },
+            'info': {
+                'zh': '设置日志打印的步数间隔',
+                'en': 'Set the logging interval'
+            }
+        },
+        'lr_scheduler_type': {
+            'label': {
+                'zh': 'LrScheduler类型',
+                'en': 'The LrScheduler type'
+            },
+            'info': {
+                'zh': '设置LrScheduler类型',
+                'en': 'Set the LrScheduler type'
+            }
+        },
+        'warmup_ratio': {
+            'label': {
+                'zh': '学习率warmup比例',
+                'en': 'Lr warmup ratio'
+            },
+            'info': {
+                'zh': '设置学习率warmup比例',
+                'en': 'Set the warmup ratio in total steps'
+            }
+        },
+        'more_params': {
+            'label': {
+                'zh': '其他高级参数',
+                'en': 'Other params'
+            },
+            'info': {
+                'zh': '以json格式或--xxx xxx命令行格式填入',
+                'en': 'Fill in with json format or --xxx xxx cmd format'
+            }
+        },
+        'truncation_strategy': {
+            'label': {
+                'zh': '数据集超长策略',
+                'en': 'Dataset truncation strategy'
+            },
+            'info': {
+                'zh': '如果token超长该如何处理',
+                'en': 'How to deal with the rows exceed the max length'
+            }
+        },
+        'max_steps': {
+            'label': {
+                'zh': '最大迭代步数',
+                'en': 'Max steps',
+            },
+            'info': {
+                'zh': '设置最大迭代步数，该值如果大于零则数据集迭代次数不生效',
+                'en': 'Set the max steps, if the value > 0 then num_train_epochs has no effects',
+            }
+        },
+        'per_device_eval_batch_size': {
+            'label': {
+                'zh': '验证batch size',
+                'en': 'Val batch size',
+            },
+            'info': {
+                'zh': '验证的batch size',
+                'en': 'Set the val batch size',
+            }
+        },
+        'max_grad_norm': {
+            'label': {
+                'zh': '梯度裁剪',
+                'en': 'Max grad norm',
+            },
+            'info': {
+                'zh': '设置梯度裁剪',
+                'en': 'Set the max grad norm',
+            }
+        },
+        'predict_with_generate': {
+            'label': {
+                'zh': '使用生成指标代替loss',
+                'en': 'Use generate metric instead of loss',
+            },
+            'info': {
+                'zh': '验证时使用generate/Rouge代替loss',
+                'en': 'Use model.generate/Rouge instead of loss',
+            }
+        },
+        'deepspeed': {
+            'label': {
+                'zh': 'deepspeed',
+                'en': 'deepspeed',
+            },
+            'info': {
+                'zh': '可以选择下拉列表，也支持传入路径',
+                'en': 'Choose from the dropbox or fill in a valid path',
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='advanced_param', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Textbox(elem_id='optim', lines=1, scale=20)
+                    gr.Textbox(elem_id='weight_decay', lines=1, scale=20)
+                    gr.Textbox(elem_id='logging_steps', lines=1, scale=20)
+                    gr.Textbox(elem_id='lr_scheduler_type', lines=1, scale=20)
+                    gr.Textbox(elem_id='max_steps', lines=1, scale=20)
+                    gr.Slider(elem_id='warmup_ratio', minimum=0.0, maximum=1.0, step=0.05, scale=20)
+                with gr.Row():
+                    gr.Dropdown(elem_id='truncation_strategy', scale=20)
+                    gr.Slider(elem_id='per_device_eval_batch_size', minimum=1, maximum=256, step=2, scale=20)
+                    gr.Textbox(elem_id='max_grad_norm', lines=1, scale=20)
+                    gr.Dropdown(
+                        elem_id='deepspeed',
+                        scale=20,
+                        allow_custom_value=True,
+                        value=None,
+                        choices=['zero0', 'zero1', 'zero2', 'zero3', 'zero2_offload', 'zero3_offload'])
+                with gr.Row():
+                    gr.Textbox(elem_id='more_params', lines=4, scale=20)
diff --git a/ms-swift/swift/ui/llm_train/dataset.py b/ms-swift/swift/ui/llm_train/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..57594617978d376be69d3bbebe243cc45ee8ac19
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/dataset.py
@@ -0,0 +1,91 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.llm.dataset.register import get_dataset_list
+from swift.ui.base import BaseUI
+
+
+class Dataset(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'dataset': {
+            'label': {
+                'zh': '数据集名称',
+                'en': 'Dataset Code'
+            },
+            'info': {
+                'zh': '选择训练的数据集，支持复选/本地路径',
+                'en': 'The dataset(s) to train the models, support multi select and local folder/files'
+            }
+        },
+        'max_length': {
+            'label': {
+                'zh': '句子最大长度',
+                'en': 'The max length',
+            },
+            'info': {
+                'zh': '设置输入模型的最大长度',
+                'en': 'Set the max length input to the model',
+            }
+        },
+        'split_dataset_ratio': {
+            'label': {
+                'zh': '验证集拆分比例',
+                'en': 'Split ratio of eval dataset'
+            },
+            'info': {
+                'zh': '表示将总数据的多少拆分到验证集中',
+                'en': 'Split the datasets by this ratio for eval'
+            }
+        },
+        'train_dataset_sample': {
+            'label': {
+                'zh': '训练集采样数量',
+                'en': 'The sample size from the train dataset'
+            },
+            'info': {
+                'zh': '从训练集中采样一定行数进行训练',
+                'en': 'Train with the sample size from the dataset',
+            }
+        },
+        'val_dataset_sample': {
+            'label': {
+                'zh': '验证集采样数量',
+                'en': 'The sample size from the val dataset'
+            },
+            'info': {
+                'zh': '从验证集中采样一定行数进行训练',
+                'en': 'Validate with the sample size from the dataset',
+            }
+        },
+        'custom_dataset_info': {
+            'label': {
+                'zh': '外部数据集配置',
+                'en': 'Custom dataset config'
+            },
+            'info': {
+                'zh': '注册外部数据集的配置文件',
+                'en': 'An extra dataset config to register your own datasets'
+            }
+        },
+        'dataset_param': {
+            'label': {
+                'zh': '数据集设置',
+                'en': 'Dataset settings'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='dataset_param', open=True):
+            with gr.Row():
+                gr.Dropdown(
+                    elem_id='dataset', multiselect=True, choices=get_dataset_list(), scale=20, allow_custom_value=True)
+                gr.Textbox(elem_id='custom_dataset_info', is_list=False, scale=20)
+                gr.Slider(elem_id='split_dataset_ratio', minimum=0.0, maximum=1.0, step=0.05, scale=10)
+                gr.Slider(elem_id='max_length', minimum=32, maximum=32768, value=1024, step=1, scale=10)
diff --git a/ms-swift/swift/ui/llm_train/hyper.py b/ms-swift/swift/ui/llm_train/hyper.py
new file mode 100644
index 0000000000000000000000000000000000000000..71f2b95599f28f5a479e01ed7ffa2aaa696e4e51
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/hyper.py
@@ -0,0 +1,129 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class Hyper(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'hyper_param': {
+            'label': {
+                'zh': '超参数设置(更多参数->高级参数设置)',
+                'en': 'Hyper settings(more params->Advanced settings)',
+            },
+        },
+        'per_device_train_batch_size': {
+            'label': {
+                'zh': '训练batch size',
+                'en': 'Train batch size',
+            },
+            'info': {
+                'zh': '训练的batch size',
+                'en': 'Set the train batch size',
+            }
+        },
+        'learning_rate': {
+            'label': {
+                'zh': '学习率',
+                'en': 'Learning rate',
+            },
+            'info': {
+                'zh': '设置学习率',
+                'en': 'Set the learning rate',
+            }
+        },
+        'eval_steps': {
+            'label': {
+                'zh': '交叉验证步数',
+                'en': 'Eval steps',
+            },
+            'info': {
+                'zh': '设置每隔多少步数进行一次验证',
+                'en': 'Set the step interval to validate',
+            }
+        },
+        'num_train_epochs': {
+            'label': {
+                'zh': '数据集迭代轮次',
+                'en': 'Train epoch',
+            },
+            'info': {
+                'zh': '设置对数据集训练多少轮次',
+                'en': 'Set the max train epoch',
+            }
+        },
+        'gradient_accumulation_steps': {
+            'label': {
+                'zh': '梯度累计步数',
+                'en': 'Gradient accumulation steps',
+            },
+            'info': {
+                'zh': '设置梯度累计步数以减小显存占用',
+                'en': 'Set the gradient accumulation steps',
+            }
+        },
+        'attn_impl': {
+            'label': {
+                'zh': 'Flash Attention类型',
+                'en': 'Flash Attention Type',
+            },
+        },
+        'neftune_noise_alpha': {
+            'label': {
+                'zh': 'neftune_noise_alpha',
+                'en': 'neftune_noise_alpha'
+            },
+            'info': {
+                'zh': '使用neftune提升训练效果, 一般设置为5或者10',
+                'en': 'Use neftune to improve performance, normally the value should be 5 or 10'
+            }
+        },
+        'save_steps': {
+            'label': {
+                'zh': '存储步数',
+                'en': 'save steps',
+            },
+            'info': {
+                'zh': '设置每个多少步数进行存储',
+                'en': 'Set the save steps',
+            }
+        },
+        'output_dir': {
+            'label': {
+                'zh': '存储目录',
+                'en': 'The output dir',
+            },
+            'info': {
+                'zh': '设置输出模型存储在哪个文件夹下',
+                'en': 'Set the output folder',
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='hyper_param', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Slider(elem_id='per_device_train_batch_size', minimum=1, maximum=256, step=2, scale=20)
+                    gr.Textbox(elem_id='learning_rate', value='1e-4', lines=1, scale=20)
+                    gr.Textbox(elem_id='num_train_epochs', lines=1, scale=20)
+                    gr.Dropdown(elem_id='attn_impl', scale=20, value='flash_attn')
+                    gr.Slider(elem_id='gradient_accumulation_steps', minimum=1, maximum=256, step=2, value=16, scale=20)
+                with gr.Row():
+                    gr.Textbox(elem_id='eval_steps', lines=1, value='500', scale=20)
+                    gr.Textbox(elem_id='save_steps', value='500', lines=1, scale=20)
+                    gr.Textbox(elem_id='output_dir', scale=20)
+                    gr.Slider(elem_id='neftune_noise_alpha', minimum=0.0, maximum=20.0, step=0.5, scale=20)
+
+    @staticmethod
+    def update_lr(sft_type):
+        if sft_type == 'full':
+            return 1e-5
+        else:
+            return 1e-4
diff --git a/ms-swift/swift/ui/llm_train/llamapro.py b/ms-swift/swift/ui/llm_train/llamapro.py
new file mode 100644
index 0000000000000000000000000000000000000000..8506da1bb792f4531b37e7d7bc6d005f6eb13764
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/llamapro.py
@@ -0,0 +1,40 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class LlamaPro(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'llamapro_tab': {
+            'label': {
+                'zh': 'LLAMAPRO参数设置',
+                'en': 'LLAMAPRO Settings'
+            },
+        },
+        'llamapro_num_new_blocks': {
+            'label': {
+                'zh': 'LLAMAPRO插入层数',
+                'en': 'LLAMAPRO new layers'
+            },
+        },
+        'llamapro_num_groups': {
+            'label': {
+                'zh': 'LLAMAPRO对原模型的分组数',
+                'en': 'LLAMAPRO groups of model'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='llamapro_tab', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Textbox(elem_id='llamapro_num_new_blocks')
+                    gr.Textbox(elem_id='llamapro_num_groups')
diff --git a/ms-swift/swift/ui/llm_train/llm_train.py b/ms-swift/swift/ui/llm_train/llm_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..63518b44f476d825623b2f148fe6c5ed14ec63ea
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/llm_train.py
@@ -0,0 +1,420 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import collections
+import os
+import re
+import sys
+import time
+from functools import partial
+from subprocess import PIPE, STDOUT, Popen
+from typing import Dict, Type
+
+import gradio as gr
+import json
+import torch
+from json import JSONDecodeError
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
+
+from swift.llm import RLHFArguments
+from swift.llm.argument.base_args.base_args import get_supported_tuners
+from swift.ui.base import BaseUI
+from swift.ui.llm_train.advanced import Advanced
+from swift.ui.llm_train.dataset import Dataset
+from swift.ui.llm_train.galore import Galore
+from swift.ui.llm_train.hyper import Hyper
+from swift.ui.llm_train.lisa import Lisa
+from swift.ui.llm_train.llamapro import LlamaPro
+from swift.ui.llm_train.lora import LoRA
+from swift.ui.llm_train.model import Model
+from swift.ui.llm_train.quantization import Quantization
+from swift.ui.llm_train.report_to import ReportTo
+from swift.ui.llm_train.rlhf import RLHF
+from swift.ui.llm_train.runtime import Runtime
+from swift.ui.llm_train.save import Save
+from swift.ui.llm_train.self_cog import SelfCog
+from swift.utils import get_device_count, get_logger
+
+logger = get_logger()
+
+
+class LLMTrain(BaseUI):
+    group = 'llm_train'
+
+    sub_ui = [
+        Model,
+        Dataset,
+        Runtime,
+        Save,
+        LoRA,
+        Hyper,
+        Quantization,
+        SelfCog,
+        Advanced,
+        RLHF,
+        Lisa,
+        Galore,
+        LlamaPro,
+        ReportTo,
+    ]
+
+    locale_dict: Dict[str, Dict] = {
+        'llm_train': {
+            'label': {
+                'zh': 'LLM训练',
+                'en': 'LLM Training',
+            }
+        },
+        'train_stage': {
+            'label': {
+                'zh': '训练Stage',
+                'en': 'Train Stage'
+            },
+            'info': {
+                'zh': '请注意选择与此匹配的数据集，人类对齐配置在页面下方',
+                'en': 'Please choose matched dataset, RLHF settings is at the bottom of the page'
+            }
+        },
+        'submit_alert': {
+            'value': {
+                'zh':
+                '任务已开始，请查看tensorboard或日志记录，关闭本页面不影响训练过程',
+                'en':
+                'Task started, please check the tensorboard or log file, '
+                'closing this page does not affect training'
+            }
+        },
+        'dataset_alert': {
+            'value': {
+                'zh': '请选择或填入一个数据集',
+                'en': 'Please input or select a dataset'
+            }
+        },
+        'submit': {
+            'value': {
+                'zh': '🚀 开始训练',
+                'en': '🚀 Begin'
+            }
+        },
+        'dry_run': {
+            'label': {
+                'zh': '仅生成运行命令',
+                'en': 'Dry-run'
+            },
+            'info': {
+                'zh': '仅生成运行命令，开发者自行运行',
+                'en': 'Generate run command only, for manually running'
+            }
+        },
+        'gpu_id': {
+            'label': {
+                'zh': '选择可用GPU',
+                'en': 'Choose GPU'
+            },
+            'info': {
+                'zh': '选择训练使用的GPU号，如CUDA不可用只能选择CPU',
+                'en': 'Select GPU to train'
+            }
+        },
+        'train_type': {
+            'label': {
+                'zh': '训练方式',
+                'en': 'Train type'
+            },
+            'info': {
+                'zh': '选择训练的方式',
+                'en': 'Select the training type'
+            }
+        },
+        'seed': {
+            'label': {
+                'zh': '随机数种子',
+                'en': 'Seed'
+            },
+            'info': {
+                'zh': '选择随机数种子',
+                'en': 'Select a random seed'
+            }
+        },
+        'torch_dtype': {
+            'label': {
+                'zh': '训练精度',
+                'en': 'Training Precision'
+            },
+            'info': {
+                'zh': '选择训练精度',
+                'en': 'Select the training precision'
+            }
+        },
+        'envs': {
+            'label': {
+                'zh': '环境变量',
+                'en': 'Extra env vars'
+            },
+        },
+        'use_ddp': {
+            'label': {
+                'zh': '使用DDP',
+                'en': 'Use DDP'
+            },
+            'info': {
+                'zh': '是否使用数据并行训练',
+                'en': 'Use Distributed Data Parallel to train'
+            }
+        },
+        'ddp_num': {
+            'label': {
+                'zh': 'DDP分片数量',
+                'en': 'Number of DDP sharding'
+            },
+            'info': {
+                'zh': '启用多少进程的数据并行',
+                'en': 'The data parallel size of DDP'
+            }
+        },
+        'tuner_backend': {
+            'label': {
+                'zh': 'Tuner backend',
+                'en': 'Tuner backend'
+            },
+            'info': {
+                'zh': 'tuner实现框架',
+                'en': 'The tuner backend'
+            }
+        },
+        'use_liger_kernel': {
+            'label': {
+                'zh': '使用Liger kernel',
+                'en': 'Use Liger kernel'
+            },
+            'info': {
+                'zh': 'Liger kernel可以有效降低显存使用',
+                'en': 'Liger kernel can reduce memory usage'
+            }
+        },
+        'train_param': {
+            'label': {
+                'zh': '训练参数设置',
+                'en': 'Train settings'
+            },
+        },
+    }
+
+    choice_dict = BaseUI.get_choices_from_dataclass(RLHFArguments)
+    default_dict = BaseUI.get_default_value_from_dataclass(RLHFArguments)
+    arguments = BaseUI.get_argument_names(RLHFArguments)
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.TabItem(elem_id='llm_train', label=''):
+            default_device = 'cpu'
+            device_count = get_device_count()
+            if device_count > 0:
+                default_device = '0'
+            with gr.Blocks():
+                Model.build_ui(base_tab)
+                Dataset.build_ui(base_tab)
+                with gr.Accordion(elem_id='train_param', open=True):
+                    with gr.Row():
+                        gr.Dropdown(elem_id='train_stage', choices=['pt', 'sft', 'rlhf'], value='sft', scale=3)
+                        gr.Dropdown(elem_id='train_type', scale=2, choices=list(get_supported_tuners()))
+                        gr.Dropdown(elem_id='tuner_backend', scale=2)
+                    with gr.Row():
+                        gr.Textbox(elem_id='seed', scale=4)
+                        gr.Dropdown(elem_id='torch_dtype', scale=4)
+                        gr.Checkbox(elem_id='use_liger_kernel', scale=4)
+                        gr.Checkbox(elem_id='use_ddp', value=False, scale=4)
+                        gr.Textbox(elem_id='ddp_num', value='2', scale=4)
+                Hyper.build_ui(base_tab)
+                Runtime.build_ui(base_tab)
+                with gr.Row():
+                    gr.Dropdown(
+                        elem_id='gpu_id',
+                        multiselect=True,
+                        choices=[str(i) for i in range(device_count)] + ['cpu'],
+                        value=default_device,
+                        scale=8)
+                    gr.Textbox(elem_id='envs', scale=8)
+                    gr.Checkbox(elem_id='dry_run', value=False, scale=4)
+                    submit = gr.Button(elem_id='submit', scale=4, variant='primary')
+
+                LoRA.build_ui(base_tab)
+                RLHF.build_ui(base_tab)
+                Quantization.build_ui(base_tab)
+                Galore.build_ui(base_tab)
+                Lisa.build_ui(base_tab)
+                LlamaPro.build_ui(base_tab)
+                SelfCog.build_ui(base_tab)
+                Save.build_ui(base_tab)
+                ReportTo.build_ui(base_tab)
+                Advanced.build_ui(base_tab)
+
+                cls.element('train_type').change(
+                    Hyper.update_lr, inputs=[base_tab.element('train_type')], outputs=[cls.element('learning_rate')])
+
+                submit.click(
+                    cls.train_local,
+                    list(cls.valid_elements().values()), [
+                        cls.element('running_cmd'),
+                        cls.element('logging_dir'),
+                        cls.element('runtime_tab'),
+                        cls.element('running_tasks'),
+                        cls.element('train_record'),
+                    ],
+                    queue=True)
+
+                base_tab.element('running_tasks').change(
+                    partial(Runtime.task_changed, base_tab=base_tab), [base_tab.element('running_tasks')],
+                    list(base_tab.valid_elements().values()) + [cls.element('log')] + Runtime.all_plots)
+                Runtime.element('kill_task').click(
+                    Runtime.kill_task,
+                    [Runtime.element('running_tasks')],
+                    [Runtime.element('running_tasks')] + [Runtime.element('log')] + Runtime.all_plots,
+                ).then(Runtime.reset, [], [Runtime.element('logging_dir')] + [Hyper.element('output_dir')])
+
+    @classmethod
+    def update_runtime(cls):
+        return gr.update(open=True), gr.update(visible=True)
+
+    @classmethod
+    def train(cls, *args):
+        ignore_elements = ('logging_dir', 'more_params', 'train_stage', 'envs')
+        default_args = cls.get_default_value_from_dataclass(RLHFArguments)
+        kwargs = {}
+        kwargs_is_list = {}
+        other_kwargs = {}
+        more_params = {}
+        more_params_cmd = ''
+        keys = cls.valid_element_keys()
+        train_stage = 'sft'
+        for key, value in zip(keys, args):
+            compare_value = default_args.get(key)
+            if isinstance(value, str) and re.fullmatch(cls.int_regex, value):
+                value = int(value)
+            elif isinstance(value, str) and re.fullmatch(cls.float_regex, value):
+                value = float(value)
+            elif isinstance(value, str) and re.fullmatch(cls.bool_regex, value):
+                value = True if value.lower() == 'true' else False
+            if key not in ignore_elements and key in default_args and compare_value != value and value:
+                kwargs[key] = value if not isinstance(value, list) else ' '.join(value)
+                kwargs_is_list[key] = isinstance(value, list) or getattr(cls.element(key), 'is_list', False)
+            else:
+                other_kwargs[key] = value
+            if key == 'more_params' and value:
+                try:
+                    more_params = json.loads(value)
+                except (JSONDecodeError or TypeError):
+                    more_params_cmd = value
+
+            if key == 'train_stage':
+                train_stage = value
+
+        kwargs.update(more_params)
+        if 'dataset' not in kwargs and 'custom_train_dataset_path' not in kwargs:
+            raise gr.Error(cls.locale('dataset_alert', cls.lang)['value'])
+
+        model = kwargs.get('model')
+        if os.path.exists(model) and os.path.exists(os.path.join(model, 'args.json')):
+            kwargs['resume_from_checkpoint'] = kwargs.pop('model')
+
+        cmd = train_stage
+        if kwargs.get('deepspeed'):
+            more_params_cmd += f' --deepspeed {kwargs.pop("deepspeed")} '
+        try:
+            sft_args = RLHFArguments(
+                **{
+                    key: value.split(' ') if kwargs_is_list.get(key, False) and isinstance(value, str) else value
+                    for key, value in kwargs.items()
+                })
+        except Exception as e:
+            if 'using `--model`' in str(e):  # TODO a dirty fix
+                kwargs['model'] = kwargs.pop('resume_from_checkpoint')
+                sft_args = RLHFArguments(
+                    **{
+                        key: value.split(' ') if kwargs_is_list.get(key, False) and isinstance(value, str) else value
+                        for key, value in kwargs.items()
+                    })
+            else:
+                raise e
+        params = ''
+
+        sep = f'{cls.quote} {cls.quote}'
+        for e in kwargs:
+            if isinstance(kwargs[e], list):
+                params += f'--{e} {cls.quote}{sep.join(kwargs[e])}{cls.quote} '
+            elif e in kwargs_is_list and kwargs_is_list[e]:
+                all_args = [arg for arg in kwargs[e].split(' ') if arg.strip()]
+                params += f'--{e} {cls.quote}{sep.join(all_args)}{cls.quote} '
+            else:
+                params += f'--{e} {cls.quote}{kwargs[e]}{cls.quote} '
+        params += more_params_cmd + ' '
+        params += f'--add_version False --output_dir {sft_args.output_dir} ' \
+                  f'--logging_dir {sft_args.logging_dir} --ignore_args_error True'
+        ddp_param = ''
+        devices = other_kwargs['gpu_id']
+        envs = other_kwargs['envs'] or ''
+        envs = envs.strip()
+        devices = [d for d in devices if d]
+        if other_kwargs['use_ddp']:
+            assert int(other_kwargs['ddp_num']) > 0
+            ddp_param = f'NPROC_PER_NODE={int(other_kwargs["ddp_num"])}'
+        assert (len(devices) == 1 or 'cpu' not in devices)
+        gpus = ','.join(devices)
+        cuda_param = ''
+        if gpus != 'cpu':
+            if is_torch_npu_available():
+                cuda_param = f'ASCEND_RT_VISIBLE_DEVICES={gpus}'
+            elif is_torch_cuda_available():
+                cuda_param = f'CUDA_VISIBLE_DEVICES={gpus}'
+            else:
+                cuda_param = ''
+
+        log_file = os.path.join(sft_args.logging_dir, 'run.log')
+        if sys.platform == 'win32':
+            if cuda_param:
+                cuda_param = f'set {cuda_param} && '
+            if ddp_param:
+                ddp_param = f'set {ddp_param} && '
+            if envs:
+                envs = [env.strip() for env in envs.split(' ') if env.strip()]
+                _envs = ''
+                for env in envs:
+                    _envs += f'set {env} && '
+                envs = _envs
+            run_command = f'{cuda_param}{ddp_param}{envs}start /b swift sft {params} > {log_file} 2>&1'
+        else:
+            run_command = f'{cuda_param} {ddp_param} {envs} nohup swift {cmd} {params} > {log_file} 2>&1 &'
+        logger.info(f'Run training: {run_command}')
+        if model:
+            record = {}
+            for key, value in zip(keys, args):
+                if key in default_args or key in ('more_params', 'train_stage', 'use_ddp', 'ddp_num', 'gpu_id', 'envs'):
+                    record[key] = value or None
+            cls.save_cache(model, record)
+        return run_command, sft_args, other_kwargs
+
+    @classmethod
+    def train_studio(cls, *args):
+        run_command, sft_args, other_kwargs = cls.train(*args)
+        if not other_kwargs['dry_run']:
+            lines = collections.deque(maxlen=int(os.environ.get('MAX_LOG_LINES', 50)))
+            process = Popen(run_command, shell=True, stdout=PIPE, stderr=STDOUT)
+            with process.stdout:
+                for line in iter(process.stdout.readline, b''):
+                    line = line.decode('utf-8')
+                    lines.append(line)
+                    yield ['\n'.join(lines)] + Runtime.plot(run_command) + [run_command]
+        else:
+            yield [
+                'Current is dryrun mode so you can only view the training cmd, please duplicate this space to '
+                'do training or use with inference.'
+            ] + [None] * len(Runtime.sft_plot) + [run_command]
+
+    @classmethod
+    def train_local(cls, *args):
+        run_command, sft_args, other_kwargs = cls.train(*args)
+        if not other_kwargs['dry_run']:
+            os.makedirs(sft_args.logging_dir, exist_ok=True)
+            os.system(run_command)
+            time.sleep(1)  # to make sure the log file has been created.
+            gr.Info(cls.locale('submit_alert', cls.lang)['value'])
+        return run_command, sft_args.logging_dir, gr.update(open=True), Runtime.refresh_tasks(
+            sft_args.output_dir), gr.update(choices=cls.list_cache(sft_args.model))
diff --git a/ms-swift/swift/ui/llm_train/lora.py b/ms-swift/swift/ui/llm_train/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5fbe5d10f7a760c4058069c6b25e8ba87725bef
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/lora.py
@@ -0,0 +1,102 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class LoRA(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'lora_tab': {
+            'label': {
+                'zh': 'LoRA参数设置',
+                'en': 'LoRA settings'
+            },
+        },
+        'target_modules': {
+            'label': {
+                'zh': 'LoRA目标模块',
+                'en': 'LoRA target modules'
+            },
+            'info': {
+                'zh': '设置LoRA目标模块，如训练所有Linear请改为`all-linear`',
+                'en': 'Set the LoRA target modules, fill in `all-linear` if train all Linears'
+            }
+        },
+        'lora_rank': {
+            'label': {
+                'zh': 'LoRA的秩',
+                'en': 'The LoRA rank'
+            }
+        },
+        'lora_alpha': {
+            'label': {
+                'zh': 'LoRA的alpha',
+                'en': 'The LoRA alpha'
+            }
+        },
+        'lora_dropout': {
+            'label': {
+                'zh': 'LoRA的dropout',
+                'en': 'The LoRA dropout'
+            }
+        },
+        'use_rslora': {
+            'label': {
+                'zh': '使用rslora',
+                'en': 'Use rslora'
+            }
+        },
+        'use_dora': {
+            'label': {
+                'zh': '使用dora',
+                'en': 'Use dora'
+            }
+        },
+        'lora_dtype': {
+            'label': {
+                'zh': 'lora部分的参数类型',
+                'en': 'The dtype of lora parameters'
+            }
+        },
+        'init_weights': {
+            'label': {
+                'zh': 'lora初始化方法',
+                'en': 'init lora weights'
+            },
+            'info': {
+                'zh': 'gaussian/pissa/pissa_niter_[n]/olora/loftq/true/false',
+                'en': 'gaussian/pissa/pissa_niter_[n]/olora/loftq/true/false',
+            }
+        },
+        'lorap_lr_ratio': {
+            'label': {
+                'zh': 'Lora+学习率倍率',
+                'en': 'The lr ratio of Lora+'
+            },
+            'info': {
+                'zh': '建议值16.0',
+                'en': 'Suggested value: 16.0'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='lora_tab', open=True):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Textbox(elem_id='target_modules', lines=1, scale=5, value='all-linear', is_list=True)
+                    gr.Slider(elem_id='lora_rank', value=8, minimum=1, maximum=512, step=8, scale=2)
+                    gr.Slider(elem_id='lora_alpha', value=32, minimum=1, maximum=512, step=8, scale=2)
+                    gr.Textbox(elem_id='lora_dropout', scale=2)
+                with gr.Row():
+                    gr.Dropdown(elem_id='lora_dtype', scale=2, value=None)
+                    gr.Textbox(elem_id='lorap_lr_ratio', scale=2)
+                    gr.Checkbox(elem_id='use_rslora', scale=2)
+                    gr.Checkbox(elem_id='use_dora', scale=2)
+                    gr.Textbox(elem_id='init_weights', scale=4)
diff --git a/ms-swift/swift/ui/llm_train/model.py b/ms-swift/swift/ui/llm_train/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9653bedee42f89c7a3c3a22c48401549a738de1
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/model.py
@@ -0,0 +1,127 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from functools import partial
+from typing import Type
+
+import gradio as gr
+
+from swift.llm import TEMPLATE_MAPPING, ModelType, RLHFArguments
+from swift.llm.model.register import get_all_models
+from swift.ui.base import BaseUI
+
+
+class Model(BaseUI):
+    group = 'llm_train'
+
+    locale_dict = {
+        'model_type': {
+            'label': {
+                'zh': '模型类型',
+                'en': 'Select Model Type'
+            },
+            'info': {
+                'zh': 'SWIFT已支持的模型类型',
+                'en': 'Base model type supported by SWIFT'
+            }
+        },
+        'model': {
+            'label': {
+                'zh': '模型id或路径',
+                'en': 'Model id or path'
+            },
+            'info': {
+                'zh': '实际的模型id',
+                'en': 'The actual model id or model path'
+            }
+        },
+        'template': {
+            'label': {
+                'zh': '模型Prompt模板类型',
+                'en': 'Prompt template type'
+            },
+            'info': {
+                'zh': '选择匹配模型的Prompt模板',
+                'en': 'Choose the template type of the model'
+            }
+        },
+        'system': {
+            'label': {
+                'zh': 'system字段',
+                'en': 'system'
+            },
+            'info': {
+                'zh': '选择system字段的内容',
+                'en': 'Choose the content of the system field'
+            }
+        },
+        'reset': {
+            'value': {
+                'zh': '恢复模型初始值',
+                'en': 'Reset model default'
+            },
+        },
+        'train_record': {
+            'label': {
+                'zh': '训练记录',
+                'en': 'Train record'
+            },
+            'info': {
+                'zh': '展示使用web-ui的历史训练及参数',
+                'en': 'Show the training history and parameters'
+            }
+        },
+        'clear_cache': {
+            'value': {
+                'zh': '删除训练记录',
+                'en': 'Delete train records'
+            },
+        },
+        'model_param': {
+            'label': {
+                'zh': '模型设置',
+                'en': 'Model settings'
+            },
+        },
+        'checkpoint': {
+            'value': {
+                'zh': '训练后的模型',
+                'en': 'Trained model'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='model_param', open=True):
+            with gr.Row():
+                model = gr.Dropdown(
+                    elem_id='model',
+                    scale=20,
+                    choices=get_all_models(),
+                    value='Qwen/Qwen2.5-7B-Instruct',
+                    allow_custom_value=True)
+                gr.Dropdown(elem_id='model_type', choices=ModelType.get_model_name_list(), scale=20)
+                gr.Dropdown(elem_id='template', choices=list(TEMPLATE_MAPPING.keys()), scale=20)
+                train_record = gr.Dropdown(elem_id='train_record', choices=[], scale=20)
+                clear_cache = gr.Button(elem_id='clear_cache', scale=2)
+            with gr.Row():
+                gr.Textbox(elem_id='system', lines=1, scale=20)
+
+        def clear_record(model):
+            if model:
+                cls.clear_cache(model)
+                return gr.update(choices=[])
+            return gr.update()
+
+        clear_cache.click(clear_record, inputs=[model], outputs=[train_record])
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        cls.element('model').change(
+            partial(base_tab.update_input_model, arg_cls=RLHFArguments),
+            inputs=[cls.element('model')],
+            outputs=[cls.element('train_record')] + list(base_tab.valid_elements().values()))
+
+        cls.element('train_record').change(
+            partial(base_tab.update_all_settings, base_tab=base_tab),
+            inputs=[cls.element('model'), cls.element('train_record')],
+            outputs=list(base_tab.valid_elements().values()))
diff --git a/ms-swift/swift/ui/llm_train/quantization.py b/ms-swift/swift/ui/llm_train/quantization.py
new file mode 100644
index 0000000000000000000000000000000000000000..98b59f4f6eb63781c5213bc8027b067d4df49c25
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/quantization.py
@@ -0,0 +1,68 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class Quantization(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'quantization_tab': {
+            'label': {
+                'zh': '量化参数设置',
+                'en': 'Quantization settings'
+            },
+        },
+        'quant_method': {
+            'label': {
+                'zh': '量化方式',
+                'en': 'Quantization method'
+            },
+            'info': {
+                'zh': '如果制定了量化位数，本参数默认为bnb',
+                'en': 'Default is bnb if quantization_bit is specified'
+            }
+        },
+        'quant_bits': {
+            'label': {
+                'zh': '量化bit数',
+                'en': 'Quantization bit'
+            },
+            'info': {
+                'zh': '设置量化bit数, 0代表不进行量化',
+                'en': 'Set the quantization bit, 0 for no quantization'
+            }
+        },
+        'bnb_4bit_compute_dtype': {
+            'label': {
+                'zh': 'bnb_4bit_compute_dtype',
+                'en': 'bnb_4bit_compute_dtype'
+            },
+        },
+        'bnb_4bit_quant_type': {
+            'label': {
+                'zh': 'bnb_4bit_quant_type',
+                'en': 'bnb_4bit_quant_type'
+            },
+        },
+        'bnb_4bit_use_double_quant': {
+            'label': {
+                'zh': 'bnb_4bit_use_double_quant',
+                'en': 'bnb_4bit_use_double_quant'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='quantization_tab', open=False):
+            with gr.Row():
+                gr.Dropdown(elem_id='quant_bits', value=None)
+                gr.Dropdown(elem_id='quant_method', value=None)
+                gr.Dropdown(elem_id='bnb_4bit_compute_dtype', value=None)
+                gr.Dropdown(elem_id='bnb_4bit_quant_type', value=None)
+                gr.Checkbox(elem_id='bnb_4bit_use_double_quant', value=None)
diff --git a/ms-swift/swift/ui/llm_train/report_to.py b/ms-swift/swift/ui/llm_train/report_to.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa77c08737e4668981e8c185d808772d08260ed3
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/report_to.py
@@ -0,0 +1,75 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class ReportTo(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'reporter': {
+            'label': {
+                'zh': '训练记录',
+                'en': 'Training report'
+            },
+        },
+        'report_to': {
+            'label': {
+                'zh': '训练记录方式',
+                'en': 'Report to'
+            },
+        },
+        'swanlab_token': {
+            'label': {
+                'zh': 'swanlab登录token',
+                'en': 'The login token of swanlab'
+            },
+        },
+        'swanlab_project': {
+            'label': {
+                'zh': 'swanlab项目名称',
+                'en': 'Project of swanlab'
+            },
+        },
+        'swanlab_workspace': {
+            'label': {
+                'zh': 'swanlab工作空间',
+                'en': 'Workspace of swanlab'
+            },
+        },
+        'swanlab_exp_name': {
+            'label': {
+                'zh': 'swanlab实验名称',
+                'en': 'Experiment of swanlab'
+            },
+        },
+        'swanlab_mode': {
+            'label': {
+                'zh': 'swanlab工作模式',
+                'en': 'Work mode of swanlab'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='reporter', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Dropdown(
+                        elem_id='report_to',
+                        multiselect=True,
+                        is_list=True,
+                        choices=['tensorboard', 'wandb', 'swanlab'],
+                        allow_custom_value=True,
+                        scale=20)
+                    gr.Textbox(elem_id='swanlab_token', lines=1, scale=20)
+                    gr.Textbox(elem_id='swanlab_project', lines=1, scale=20)
+                with gr.Row():
+                    gr.Textbox(elem_id='swanlab_workspace', lines=1, scale=20)
+                    gr.Textbox(elem_id='swanlab_exp_name', lines=1, scale=20)
+                    gr.Dropdown(elem_id='swanlab_mode', scale=20)
diff --git a/ms-swift/swift/ui/llm_train/rlhf.py b/ms-swift/swift/ui/llm_train/rlhf.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef4e3152fcd8dedef11e60b250aea8ddf83e641b
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/rlhf.py
@@ -0,0 +1,102 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from functools import partial
+from typing import Type
+
+import gradio as gr
+
+from swift.llm import ModelType
+from swift.llm.model.register import get_all_models
+from swift.ui.base import BaseUI
+
+
+class RLHF(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'rlhf_tab': {
+            'label': {
+                'zh': '人类对齐参数设置',
+                'en': 'RLHF settings'
+            },
+        },
+        'rlhf_type': {
+            'label': {
+                'zh': '人类对齐算法类型',
+                'en': 'RLHF type'
+            },
+        },
+        'ref_model_type': {
+            'label': {
+                'zh': '选择ref模型',
+                'en': 'Select ref model'
+            },
+            'info': {
+                'zh': 'SWIFT已支持的模型名称',
+                'en': 'Base model supported by SWIFT'
+            }
+        },
+        'ref_model': {
+            'label': {
+                'zh': 'ref模型id或路径',
+                'en': 'Ref model id or path'
+            },
+            'info': {
+                'zh': '实际的模型id或路径',
+                'en': 'The actual model id or path'
+            }
+        },
+        'beta': {
+            'label': {
+                'zh': 'KL正则项系数',
+                'en': 'KL regression ratio'
+            },
+        },
+        'rpo_alpha': {
+            'label': {
+                'zh': 'DPO中混合sft交叉熵的系数',
+                'en': 'DPO Cross Entropy ratio'
+            },
+        },
+        'simpo_gamma': {
+            'label': {
+                'zh': 'SimPO reward margin',
+                'en': 'SimPO reward margin'
+            },
+        },
+        'desirable_weight': {
+            'label': {
+                'zh': 'KTO符合项系数',
+                'en': 'KTO desirable ratio'
+            },
+        },
+        'undesirable_weight': {
+            'label': {
+                'zh': 'KTO不符合项系数',
+                'en': 'KTO undesirable ratio'
+            },
+        }
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='rlhf_tab', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Dropdown(elem_id='rlhf_type', value=None)
+                    gr.Dropdown(
+                        elem_id='ref_model', scale=20, value=None, choices=get_all_models(), allow_custom_value=True)
+                    gr.Dropdown(elem_id='ref_model_type', choices=ModelType.get_model_name_list(), value=None, scale=20)
+                with gr.Row():
+                    gr.Slider(elem_id='beta', minimum=0., maximum=5.0, step=0.1, scale=20)
+                    gr.Slider(elem_id='rpo_alpha', minimum=0., maximum=2, step=0.1, scale=20)
+                    gr.Slider(elem_id='simpo_gamma', minimum=0., maximum=2.0, step=0.1, scale=20)
+                    gr.Slider(elem_id='desirable_weight', minimum=0., maximum=2.0, step=0.1, scale=20)
+                    gr.Slider(elem_id='undesirable_weight', minimum=0., maximum=2.0, step=0.1, scale=20)
+
+    @classmethod
+    def after_build_ui(cls, base_tab: Type['BaseUI']):
+        cls.element('ref_model').change(
+            partial(cls.update_input_model, allow_keys=['ref_model_type'], has_record=False, is_ref_model=True),
+            inputs=[cls.element('ref_model')],
+            outputs=[cls.element('ref_model_type')])
diff --git a/ms-swift/swift/ui/llm_train/runtime.py b/ms-swift/swift/ui/llm_train/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..71cf828fb1933ddfbfcd9fc0769d368624547aa5
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/runtime.py
@@ -0,0 +1,571 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import collections
+import os.path
+import sys
+import time
+import webbrowser
+from datetime import datetime
+from typing import Dict, List, Tuple, Type
+
+import gradio as gr
+import json
+import matplotlib.pyplot as plt
+import psutil
+from packaging import version
+from transformers import is_tensorboard_available
+
+from swift.ui.base import BaseUI
+from swift.ui.llm_train.utils import close_loop, run_command_in_subprocess
+from swift.utils import TB_COLOR, TB_COLOR_SMOOTH, get_logger, read_tensorboard_file, tensorboard_smoothing
+from swift.utils.utils import format_time
+
+logger = get_logger()
+
+
+class Runtime(BaseUI):
+
+    handlers: Dict[str, Tuple[List, Tuple]] = {}
+
+    group = 'llm_train'
+
+    all_plots = None
+
+    log_event = {}
+
+    sft_plot = [
+        {
+            'name': 'train/loss',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/acc',
+            'smooth': None,
+        },
+        {
+            'name': 'train/learning_rate',
+            'smooth': None,
+        },
+        {
+            'name': 'eval/loss',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'eval/acc',
+            'smooth': None,
+        },
+    ]
+
+    dpo_plot = [
+        {
+            'name': 'train/loss',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/rewards/accuracies',
+            'smooth': None,
+        },
+        {
+            'name': 'train/rewards/margins',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/logps/chosen',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/logps/rejected',
+            'smooth': 0.9,
+        },
+    ]
+
+    kto_plot = [
+        {
+            'name': 'kl',
+            'smooth': None,
+        },
+        {
+            'name': 'rewards/chosen_sum',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'logps/chosen_sum',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'rewards/rejected_sum',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'logps/rejected_sum',
+            'smooth': 0.9,
+        },
+    ]
+
+    orpo_plot = [
+        {
+            'name': 'train/loss',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/rewards/accuracies',
+            'smooth': None,
+        },
+        {
+            'name': 'train/rewards/margins',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/rewards/chosen',
+            'smooth': 0.9,
+        },
+        {
+            'name': 'train/log_odds_ratio',
+            'smooth': 0.9,
+        },
+    ]
+
+    locale_dict = {
+        'runtime_tab': {
+            'label': {
+                'zh': '运行时',
+                'en': 'Runtime'
+            },
+        },
+        'tb_not_found': {
+            'value': {
+                'zh': 'tensorboard未安装,使用pip install tensorboard进行安装',
+                'en': 'tensorboard not found, install it by pip install tensorboard',
+            }
+        },
+        'running_cmd': {
+            'label': {
+                'zh': '运行命令',
+                'en': 'Command line'
+            },
+            'info': {
+                'zh': '执行的实际命令',
+                'en': 'The actual command'
+            }
+        },
+        'show_log': {
+            'value': {
+                'zh': '展示运行状态',
+                'en': 'Show running status'
+            },
+        },
+        'stop_show_log': {
+            'value': {
+                'zh': '停止展示运行状态',
+                'en': 'Stop showing running status'
+            },
+        },
+        'logging_dir': {
+            'label': {
+                'zh': '日志路径',
+                'en': 'Logging dir'
+            },
+            'info': {
+                'zh': '支持手动传入文件路径',
+                'en': 'Support fill custom path in'
+            }
+        },
+        'log': {
+            'label': {
+                'zh': '日志输出',
+                'en': 'Logging content'
+            },
+            'info': {
+                'zh': '如果日志无更新请再次点击"展示日志内容"',
+                'en': 'Please press "Show log" if the log content is not updating'
+            }
+        },
+        'running_tasks': {
+            'label': {
+                'zh': '运行中任务',
+                'en': 'Running Tasks'
+            },
+            'info': {
+                'zh': '运行中的任务（所有的swift sft命令）',
+                'en': 'All running tasks(started by swift sft)'
+            }
+        },
+        'refresh_tasks': {
+            'value': {
+                'zh': '找回运行时任务',
+                'en': 'Find running tasks'
+            },
+        },
+        'kill_task': {
+            'value': {
+                'zh': '杀死任务',
+                'en': 'Kill running task'
+            },
+        },
+        'tb_url': {
+            'label': {
+                'zh': 'Tensorboard链接',
+                'en': 'Tensorboard URL'
+            },
+            'info': {
+                'zh': '仅展示，不可编辑',
+                'en': 'Not editable'
+            }
+        },
+        'start_tb': {
+            'value': {
+                'zh': '打开TensorBoard',
+                'en': 'Start TensorBoard'
+            },
+        },
+        'close_tb': {
+            'value': {
+                'zh': '关闭TensorBoard',
+                'en': 'Close TensorBoard'
+            },
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='runtime_tab', open=False, visible=True):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Textbox(elem_id='running_cmd', lines=1, scale=20, interactive=False, max_lines=1)
+                    gr.Textbox(elem_id='logging_dir', lines=1, scale=20, max_lines=1)
+                    gr.Button(elem_id='show_log', scale=2, variant='primary')
+                    gr.Button(elem_id='stop_show_log', scale=2)
+                    gr.Textbox(elem_id='tb_url', lines=1, scale=10, interactive=False, max_lines=1)
+                    gr.Button(elem_id='start_tb', scale=2, variant='primary')
+                    gr.Button(elem_id='close_tb', scale=2)
+                with gr.Row():
+                    gr.Textbox(elem_id='log', lines=6, visible=False)
+                with gr.Row():
+                    gr.Dropdown(elem_id='running_tasks', scale=10)
+                    gr.Button(elem_id='refresh_tasks', scale=1)
+                    gr.Button(elem_id='kill_task', scale=1)
+
+                with gr.Row():
+                    cls.all_plots = []
+                    for idx, k in enumerate(Runtime.sft_plot):
+                        name = k['name']
+                        cls.all_plots.append(gr.Plot(elem_id=str(idx), label=name))
+
+                concurrency_limit = {}
+                if version.parse(gr.__version__) >= version.parse('4.0.0'):
+                    concurrency_limit = {'concurrency_limit': 5}
+                base_tab.element('show_log').click(
+                    Runtime.update_log, [base_tab.element('running_tasks')], [cls.element('log')] + cls.all_plots).then(
+                        Runtime.wait, [base_tab.element('logging_dir'),
+                                       base_tab.element('running_tasks')], [cls.element('log')] + cls.all_plots,
+                        **concurrency_limit)
+
+                base_tab.element('stop_show_log').click(cls.break_log_event, [cls.element('running_tasks')], [])
+
+                base_tab.element('start_tb').click(
+                    Runtime.start_tb,
+                    [base_tab.element('logging_dir')],
+                    [base_tab.element('tb_url')],
+                )
+
+                base_tab.element('close_tb').click(
+                    Runtime.close_tb,
+                    [base_tab.element('logging_dir')],
+                    [],
+                )
+
+                base_tab.element('refresh_tasks').click(
+                    Runtime.refresh_tasks,
+                    [base_tab.element('running_tasks')],
+                    [base_tab.element('running_tasks')],
+                )
+
+    @classmethod
+    def get_plot(cls, task):
+        if not task or 'swift sft' in task or 'swift pt' in task:
+            return cls.sft_plot
+
+        args: dict = cls.parse_info_from_cmdline(task)[1]
+        train_type = args.get('rlhf_type', 'dpo')
+        if train_type in ('dpo', 'cpo', 'simpo'):
+            return cls.dpo_plot
+        elif train_type == 'kto':
+            return cls.kto_plot
+        elif train_type == 'orpo':
+            return cls.orpo_plot
+
+    @classmethod
+    def update_log(cls, task):
+        ret = [gr.update(visible=True)]
+        plot = Runtime.get_plot(task)
+        for i in range(len(plot)):
+            p = plot[i]
+            ret.append(gr.update(visible=True, label=p['name']))
+        return ret
+
+    @classmethod
+    def get_initial(cls, line):
+        tqdm_starts = ['Train:', 'Map:', 'Val:', 'Filter:']
+        for start in tqdm_starts:
+            if line.startswith(start):
+                return start
+        return None
+
+    @classmethod
+    def wait(cls, logging_dir, task):
+        if not logging_dir:
+            return [None] + Runtime.plot(task)
+        log_file = os.path.join(logging_dir, 'run.log')
+        cls.log_event[logging_dir] = False
+        offset = 0
+        latest_data = ''
+        lines = collections.deque(maxlen=int(os.environ.get('MAX_LOG_LINES', 50)))
+        try:
+            with open(log_file, 'r', encoding='utf-8') as input:
+                input.seek(offset)
+                fail_cnt = 0
+                while True:
+                    try:
+                        latest_data += input.read()
+                    except UnicodeDecodeError:
+                        continue
+                    if not latest_data:
+                        time.sleep(0.5)
+                        fail_cnt += 1
+                        if fail_cnt > 50:
+                            break
+
+                    if cls.log_event.get(logging_dir, False):
+                        cls.log_event[logging_dir] = False
+                        break
+
+                    if '\n' not in latest_data:
+                        continue
+                    latest_lines = latest_data.split('\n')
+                    if latest_data[-1] != '\n':
+                        latest_data = latest_lines[-1]
+                        latest_lines = latest_lines[:-1]
+                    else:
+                        latest_data = ''
+                    lines.extend(latest_lines)
+                    start = cls.get_initial(lines[-1])
+                    if start:
+                        i = len(lines) - 2
+                        while i >= 0:
+                            if lines[i].startswith(start):
+                                del lines[i]
+                                i -= 1
+                            else:
+                                break
+                    yield ['\n'.join(lines)] + Runtime.plot(task)
+        except IOError:
+            pass
+
+    @classmethod
+    def break_log_event(cls, task):
+        if not task:
+            return
+        pid, all_args = Runtime.parse_info_from_cmdline(task)
+        cls.log_event[all_args['logging_dir']] = True
+
+    @classmethod
+    def show_log(cls, logging_dir):
+        webbrowser.open('file://' + os.path.join(logging_dir, 'run.log'), new=2)
+
+    @classmethod
+    def start_tb(cls, logging_dir):
+        if not is_tensorboard_available():
+            gr.Error(cls.locale('tb_not_found', cls.lang)['value'])
+            return ''
+
+        logging_dir = logging_dir.strip()
+        logging_dir = logging_dir if not logging_dir.endswith(os.sep) else logging_dir[:-1]
+        if logging_dir in cls.handlers:
+            return cls.handlers[logging_dir][1]
+
+        handler, lines = run_command_in_subprocess('tensorboard', '--logdir', logging_dir, timeout=2)
+        localhost_addr = ''
+        for line in lines:
+            if 'http://localhost:' in line:
+                line = line[line.index('http://localhost:'):]
+                localhost_addr = line[:line.index(' ')]
+        cls.handlers[logging_dir] = (handler, localhost_addr)
+        logger.info('===========Tensorboard Log============')
+        logger.info('\n'.join(lines))
+        webbrowser.open(localhost_addr, new=2)
+        return localhost_addr
+
+    @staticmethod
+    def close_tb(logging_dir):
+        if logging_dir in Runtime.handlers:
+            close_loop(Runtime.handlers[logging_dir][0])
+            Runtime.handlers.pop(logging_dir)
+
+    @staticmethod
+    def refresh_tasks(running_task=None):
+        output_dir = running_task if not running_task or 'pid:' not in running_task else None
+        process_name = 'swift'
+        negative_name = 'swift.exe'
+        cmd_name = ['pt', 'sft', 'rlhf']
+        process = []
+        selected = None
+        for proc in psutil.process_iter():
+            try:
+                cmdlines = proc.cmdline()
+            except (psutil.ZombieProcess, psutil.AccessDenied, psutil.NoSuchProcess):
+                cmdlines = []
+            if any([process_name in cmdline
+                    for cmdline in cmdlines]) and not any([negative_name in cmdline
+                                                           for cmdline in cmdlines]) and any(  # noqa
+                                                               [cmdline in cmd_name for cmdline in cmdlines]):  # noqa
+                process.append(Runtime.construct_running_task(proc))
+                if output_dir is not None and any(  # noqa
+                    [output_dir == cmdline for cmdline in cmdlines]):  # noqa
+                    selected = Runtime.construct_running_task(proc)
+        if not selected:
+            if running_task and running_task in process:
+                selected = running_task
+        if not selected and process:
+            selected = process[0]
+        return gr.update(choices=process, value=selected)
+
+    @staticmethod
+    def construct_running_task(proc):
+        pid = proc.pid
+        ts = time.time()
+        create_time = proc.create_time()
+        create_time_formatted = datetime.fromtimestamp(create_time).strftime('%Y-%m-%d, %H:%M')
+
+        return f'pid:{pid}/create:{create_time_formatted}' \
+               f'/running:{format_time(ts-create_time)}/cmd:{" ".join(proc.cmdline())}'
+
+    @staticmethod
+    def parse_info_from_cmdline(task):
+        pid = None
+        if '/cmd:' in task:
+            for i in range(3):
+                slash = task.find('/')
+                if i == 0:
+                    pid = task[:slash].split(':')[1]
+                task = task[slash + 1:]
+        if 'swift sft' in task:
+            args = task.split('swift sft')[1]
+        elif 'swift pt' in task:
+            args = task.split('swift pt')[1]
+        elif 'swift rlhf' in task:
+            args = task.split('swift rlhf')[1]
+        else:
+            raise ValueError(f'Cannot parse cmd line: {task}')
+        args = [arg.strip() for arg in args.split('--') if arg.strip()]
+        all_args = {}
+        for i in range(len(args)):
+            space = args[i].find(' ')
+            splits = args[i][:space], args[i][space + 1:]
+            all_args[splits[0]] = splits[1]
+
+        output_dir = all_args['output_dir']
+        if os.path.exists(os.path.join(output_dir, 'args.json')):
+            with open(os.path.join(output_dir, 'args.json'), 'r', encoding='utf-8') as f:
+                _json = json.load(f)
+            for key in all_args.keys():
+                all_args[key] = _json.get(key)
+                if isinstance(all_args[key], list):
+                    if any([' ' in value for value in all_args[key]]):
+                        all_args[key] = [f'"{value}"' for value in all_args[key]]
+                    all_args[key] = ' '.join(all_args[key])
+        return pid, all_args
+
+    @staticmethod
+    def kill_task(task):
+        if task:
+            pid, all_args = Runtime.parse_info_from_cmdline(task)
+            output_dir = all_args['output_dir']
+            if sys.platform == 'win32':
+                os.system(f'taskkill /f /t /pid "{pid}"')
+            else:
+                os.system(f'pkill -9 -f {output_dir}')
+            time.sleep(1)
+            Runtime.break_log_event(task)
+        return [Runtime.refresh_tasks()] + [gr.update(value=None)] * (len(Runtime.get_plot(task)) + 1)
+
+    @staticmethod
+    def reset():
+        return None, 'output'
+
+    @staticmethod
+    def task_changed(task, base_tab):
+        if task:
+            _, all_args = Runtime.parse_info_from_cmdline(task)
+        else:
+            all_args = {}
+        elements = list(base_tab.valid_elements().values())
+        ret = []
+        for e in elements:
+            if e.elem_id in all_args:
+                if isinstance(e, gr.Dropdown) and e.multiselect:
+                    arg = all_args[e.elem_id].split(' ')
+                else:
+                    arg = all_args[e.elem_id]
+                ret.append(gr.update(value=arg))
+            else:
+                ret.append(gr.update())
+        Runtime.break_log_event(task)
+        return ret + [gr.update(value=None)] * (len(Runtime.get_plot(task)) + 1)
+
+    @staticmethod
+    def plot(task):
+        plot = Runtime.get_plot(task)
+        if not task:
+            return [None] * len(plot)
+        _, all_args = Runtime.parse_info_from_cmdline(task)
+        tb_dir = all_args['logging_dir']
+        if not os.path.exists(tb_dir):
+            return [None] * len(plot)
+        fname = [
+            fname for fname in os.listdir(tb_dir)
+            if os.path.isfile(os.path.join(tb_dir, fname)) and fname.startswith('events.out')
+        ]
+        if fname:
+            fname = fname[0]
+        else:
+            return [None] * len(plot)
+        tb_path = os.path.join(tb_dir, fname)
+        data = read_tensorboard_file(tb_path)
+
+        plots = []
+        for k in plot:
+            name = k['name']
+            smooth = k['smooth']
+            if name == 'train/acc':
+                if 'train/token_acc' in data:
+                    name = 'train/token_acc'
+                if 'train/seq_acc' in data:
+                    name = 'train/seq_acc'
+            if name == 'eval/acc':
+                if 'eval/token_acc' in data:
+                    name = 'eval/token_acc'
+                if 'eval/seq_acc' in data:
+                    name = 'eval/seq_acc'
+            if name not in data:
+                plots.append(None)
+                continue
+            _data = data[name]
+            steps = [d['step'] for d in _data]
+            values = [d['value'] for d in _data]
+            if len(values) == 0:
+                continue
+
+            plt.close('all')
+            fig = plt.figure()
+            ax = fig.add_subplot()
+            # _, ax = plt.subplots(1, 1, squeeze=True, figsize=(8, 5), dpi=100)
+            ax.set_title(name)
+            if len(values) == 1:
+                ax.scatter(steps, values, color=TB_COLOR_SMOOTH)
+            elif smooth is not None:
+                ax.plot(steps, values, color=TB_COLOR)
+                values_s = tensorboard_smoothing(values, smooth)
+                ax.plot(steps, values_s, color=TB_COLOR_SMOOTH)
+            else:
+                ax.plot(steps, values, color=TB_COLOR_SMOOTH)
+            plots.append(fig)
+        return plots
diff --git a/ms-swift/swift/ui/llm_train/save.py b/ms-swift/swift/ui/llm_train/save.py
new file mode 100644
index 0000000000000000000000000000000000000000..fff01df32321eebca9f87feafe2548076c6a9ada
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/save.py
@@ -0,0 +1,84 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class Save(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'save_param': {
+            'label': {
+                'zh': '存储参数设置',
+                'en': 'Saving settings'
+            },
+        },
+        'push_to_hub': {
+            'label': {
+                'zh': '推送魔搭Hub',
+                'en': 'Push to modelscope hub',
+            },
+            'info': {
+                'zh': '是否推送魔搭的模型库',
+                'en': 'Whether push the output model to modelscope hub',
+            }
+        },
+        'hub_model_id': {
+            'label': {
+                'zh': '魔搭模型id',
+                'en': 'The model-id in modelscope',
+            },
+            'info': {
+                'zh': '设置魔搭的模型id',
+                'en': 'Set the model-id of modelscope',
+            }
+        },
+        'hub_private_repo': {
+            'label': {
+                'zh': '设置仓库私有',
+                'en': 'Model is private',
+            },
+            'info': {
+                'zh': '以私有方式推送魔搭hub',
+                'en': 'Set the model as private',
+            }
+        },
+        'hub_strategy': {
+            'label': {
+                'zh': '推送策略',
+                'en': 'Push strategy',
+            },
+            'info': {
+                'zh': '设置模型推送策略',
+                'en': 'Set the push strategy',
+            }
+        },
+        'hub_token': {
+            'label': {
+                'zh': '仓库token',
+                'en': 'The hub token',
+            },
+            'info': {
+                'zh': '该token可以在www.modelscope.cn找到',
+                'en': 'Find the token in www.modelscope.cn',
+            }
+        }
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='save_param', open=False):
+            with gr.Blocks():
+                with gr.Row():
+                    gr.Checkbox(elem_id='push_to_hub', scale=20)
+                    gr.Textbox(elem_id='hub_model_id', lines=1, scale=20)
+                    gr.Checkbox(elem_id='hub_private_repo', scale=20)
+                    gr.Dropdown(
+                        elem_id='hub_strategy',
+                        scale=20,
+                        choices=['end', 'every_save', 'checkpoint', 'all_checkpoints'])
+                    gr.Textbox(elem_id='hub_token', lines=1, scale=20)
diff --git a/ms-swift/swift/ui/llm_train/self_cog.py b/ms-swift/swift/ui/llm_train/self_cog.py
new file mode 100644
index 0000000000000000000000000000000000000000..2554aacf23590abb2ec1d2ca0cb8c8802661b6f2
--- /dev/null
+++ b/ms-swift/swift/ui/llm_train/self_cog.py
@@ -0,0 +1,57 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import Type
+
+import gradio as gr
+
+from swift.ui.base import BaseUI
+
+
+class SelfCog(BaseUI):
+
+    group = 'llm_train'
+
+    locale_dict = {
+        'self_cognition': {
+            'label': {
+                'zh': '自我认知任务参数设置',
+                'en': 'Self cognition settings'
+            },
+        },
+        'self_cognition_sample': {
+            'label': {
+                'zh': '数据及采样条数',
+                'en': 'Dataset sample size'
+            },
+            'info': {
+                'zh': '设置数据集采样的条数',
+                'en': 'Set the dataset sample size'
+            }
+        },
+        'model_name': {
+            'label': {
+                'zh': '模型认知名称',
+                'en': 'Model name'
+            },
+            'info': {
+                'zh': '设置模型应当认知自己的名字, 格式为:中文名字 英文名字,中间以空格分隔',
+                'en': 'Set the name of the model think itself of, the format is Chinesename Englishname, split by space'
+            }
+        },
+        'model_author': {
+            'label': {
+                'zh': '模型作者',
+                'en': 'Model author'
+            },
+            'info': {
+                'zh': '设置模型认知的自己的作者, 格式为:中文作者 英文作者,中间以空格分隔',
+                'en': 'Set the author of the model, the format is Chineseauthor Englishauthor, split by space'
+            }
+        },
+    }
+
+    @classmethod
+    def do_build_ui(cls, base_tab: Type['BaseUI']):
+        with gr.Accordion(elem_id='self_cognition', open=False):
+            with gr.Row():
+                gr.Textbox(elem_id='model_name', scale=20, is_list=True)
+                gr.Textbox(elem_id='model_author', scale=20, is_list=True)
diff --git a/ms-swift/swift/utils/__init__.py b/ms-swift/swift/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..db53194b192522ed0aca7b227843d366cf93c1ad
--- /dev/null
+++ b/ms-swift/swift/utils/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from .env import (get_dist_setting, get_node_setting, get_pai_tensorboard_dir, is_deepspeed_enabled, is_dist,
+                  is_dist_ta, is_local_master, is_master, is_mp, is_mp_ddp, is_pai_training_job, torchacc_trim_graph,
+                  use_hf_hub, use_torchacc)
+from .import_utils import (is_liger_available, is_lmdeploy_available, is_megatron_available, is_swanlab_available,
+                           is_unsloth_available, is_vllm_ascend_available, is_vllm_available, is_wandb_available,
+                           is_xtuner_available)
+from .io_utils import JsonlWriter, append_to_jsonl, download_ms_file, get_file_mm_type, read_from_jsonl, write_to_jsonl
+from .logger import get_logger
+from .np_utils import get_seed, stat_array, transform_jsonl_to_df
+from .tb_utils import TB_COLOR, TB_COLOR_SMOOTH, plot_images, read_tensorboard_file, tensorboard_smoothing
+from .torch_utils import (Serializer, activate_parameters, find_all_linears, find_embedding, find_layers, find_norm,
+                          freeze_parameters, gc_collect, get_current_device, get_device, get_device_count,
+                          get_model_parameter_info, get_n_params_grads, init_process_group, safe_ddp_context,
+                          set_default_ddp_config, set_device, show_layers, time_synchronize)
+from .utils import (add_version_to_work_dir, check_json_format, copy_files_by_pattern, deep_getattr, find_free_port,
+                    get_env_args, import_external_file, lower_bound, parse_args, patch_getattr, read_multi_line,
+                    seed_everything, split_list, subprocess_run, test_time, upper_bound)
diff --git a/ms-swift/swift/utils/__pycache__/np_utils.cpython-310.pyc b/ms-swift/swift/utils/__pycache__/np_utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..63e6848f3b93059d50d00e4573a00cb67efcc559
Binary files /dev/null and b/ms-swift/swift/utils/__pycache__/np_utils.cpython-310.pyc differ
diff --git a/ms-swift/swift/utils/constants.py b/ms-swift/swift/utils/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ffb37cb8f1bfa875f849d96fe8091e0fd288f9f
--- /dev/null
+++ b/ms-swift/swift/utils/constants.py
@@ -0,0 +1,27 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+BIN_EXTENSIONS = [
+    '.*.bin',
+    '.*.ts',
+    '.*.pt',
+    '.*.data-00000-of-00001',
+    '.*.onnx',
+    '.*.meta',
+    '.*.pb',
+    '.*.index',
+]
+
+PEFT_TYPE_KEY = 'peft_type'
+SWIFT_TYPE_KEY = 'swift_type'
+DEFAULT_ADAPTER = 'default'
+
+
+class Invoke(object):
+    KEY = 'invoked_by'
+    THIRD_PARTY = 'third_party'
+    PRETRAINED = 'from_pretrained'
+    PIPELINE = 'pipeline'
+    TRAINER = 'trainer'
+    LOCAL_TRAINER = 'local_trainer'
+    PREPROCESSOR = 'preprocessor'
+    SWIFT = 'swift'
diff --git a/ms-swift/swift/utils/logger.py b/ms-swift/swift/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ce2adec8784d38e578463624bb4a6957c171a80
--- /dev/null
+++ b/ms-swift/swift/utils/logger.py
@@ -0,0 +1,138 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import importlib.util
+import logging
+import os
+from contextlib import contextmanager
+from types import MethodType
+from typing import Optional
+
+from modelscope.utils.logger import get_logger as get_ms_logger
+
+
+# Avoid circular reference
+def _is_local_master():
+    local_rank = int(os.getenv('LOCAL_RANK', -1))
+    return local_rank in {-1, 0}
+
+
+init_loggers = {}
+
+# old format
+# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger_format = logging.Formatter('[%(levelname)s:%(name)s] %(message)s')
+
+info_set = set()
+warning_set = set()
+
+
+def info_once(self, msg, *args, **kwargs):
+    hash_id = kwargs.get('hash_id') or msg
+    if hash_id in info_set:
+        return
+    info_set.add(hash_id)
+    self.info(msg)
+
+
+def warning_once(self, msg, *args, **kwargs):
+    hash_id = kwargs.get('hash_id') or msg
+    if hash_id in warning_set:
+        return
+    warning_set.add(hash_id)
+    self.warning(msg)
+
+
+def get_logger(log_file: Optional[str] = None, log_level: Optional[int] = None, file_mode: str = 'w'):
+    """ Get logging logger
+
+    Args:
+        log_file: Log filename, if specified, file handler will be added to
+            logger
+        log_level: Logging level.
+        file_mode: Specifies the mode to open the file, if filename is
+            specified (if filemode is unspecified, it defaults to 'w').
+    """
+    if log_level is None:
+        log_level = os.getenv('LOG_LEVEL', 'INFO').upper()
+        log_level = getattr(logging, log_level, logging.INFO)
+    logger_name = __name__.split('.')[0]
+    logger = logging.getLogger(logger_name)
+    logger.propagate = False
+    if logger_name in init_loggers:
+        add_file_handler_if_needed(logger, log_file, file_mode, log_level)
+        return logger
+
+    # handle duplicate logs to the console
+    # Starting in 1.8.0, PyTorch DDP attaches a StreamHandler <stderr> (NOTSET)
+    # to the root logger. As logger.propagate is True by default, this root
+    # level handler causes logging messages from rank>0 processes to
+    # unexpectedly show up on the console, creating much unwanted clutter.
+    # To fix this issue, we set the root logger's StreamHandler, if any, to log
+    # at the ERROR level.
+    for handler in logger.root.handlers:
+        if type(handler) is logging.StreamHandler:
+            handler.setLevel(logging.ERROR)
+
+    stream_handler = logging.StreamHandler()
+    handlers = [stream_handler]
+
+    is_worker0 = _is_local_master()
+
+    if is_worker0 and log_file is not None:
+        file_handler = logging.FileHandler(log_file, file_mode)
+        handlers.append(file_handler)
+
+    for handler in handlers:
+        handler.setFormatter(logger_format)
+        handler.setLevel(log_level)
+        logger.addHandler(handler)
+
+    if is_worker0:
+        logger.setLevel(log_level)
+    else:
+        logger.setLevel(logging.ERROR)
+
+    init_loggers[logger_name] = True
+
+    logger.info_once = MethodType(info_once, logger)
+    logger.warning_once = MethodType(warning_once, logger)
+    return logger
+
+
+logger = get_logger()
+ms_logger = get_ms_logger()
+
+logger.handlers[0].setFormatter(logger_format)
+ms_logger.handlers[0].setFormatter(logger_format)
+log_level = os.getenv('LOG_LEVEL', 'INFO').upper()
+if _is_local_master():
+    ms_logger.setLevel(log_level)
+else:
+    ms_logger.setLevel(logging.ERROR)
+
+
+@contextmanager
+def ms_logger_ignore_error():
+    ms_logger = get_ms_logger()
+    origin_log_level = ms_logger.level
+    ms_logger.setLevel(logging.CRITICAL)
+    try:
+        yield
+    finally:
+        ms_logger.setLevel(origin_log_level)
+
+
+def add_file_handler_if_needed(logger, log_file, file_mode, log_level):
+    for handler in logger.handlers:
+        if isinstance(handler, logging.FileHandler):
+            return
+
+    if importlib.util.find_spec('torch') is not None:
+        is_worker0 = int(os.getenv('LOCAL_RANK', -1)) in {-1, 0}
+    else:
+        is_worker0 = True
+
+    if is_worker0 and log_file is not None:
+        file_handler = logging.FileHandler(log_file, file_mode)
+        file_handler.setFormatter(logger_format)
+        file_handler.setLevel(log_level)
+        logger.addHandler(file_handler)
diff --git a/ms-swift/swift/utils/tb_utils.py b/ms-swift/swift/utils/tb_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..050e84d1ae238f8efb93b6598f0641e4177030c0
--- /dev/null
+++ b/ms-swift/swift/utils/tb_utils.py
@@ -0,0 +1,72 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import os
+from typing import Dict, List, Optional, Tuple
+
+import matplotlib.pyplot as plt
+from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+
+Item = Dict[str, float]
+TB_COLOR, TB_COLOR_SMOOTH = '#FFE2D9', '#FF7043'
+
+
+def read_tensorboard_file(fpath: str) -> Dict[str, List[Item]]:
+    if not os.path.isfile(fpath):
+        raise FileNotFoundError(f'fpath: {fpath}')
+    ea = EventAccumulator(fpath)
+    ea.Reload()
+    res: Dict[str, List[Item]] = {}
+    tags = ea.Tags()['scalars']
+    for tag in tags:
+        values = ea.Scalars(tag)
+        r: List[Item] = []
+        for v in values:
+            r.append({'step': v.step, 'value': v.value})
+        res[tag] = r
+    return res
+
+
+def tensorboard_smoothing(values: List[float], smooth: float = 0.9) -> List[float]:
+    norm_factor = 0
+    x = 0
+    res: List[float] = []
+    for i in range(len(values)):
+        x = x * smooth + values[i]  # Exponential decay
+        norm_factor *= smooth
+        norm_factor += 1
+        res.append(x / norm_factor)
+    return res
+
+
+def plot_images(images_dir: str,
+                tb_dir: str,
+                smooth_key: Optional[List[str]] = None,
+                smooth_val: float = 0.9,
+                figsize: Tuple[int, int] = (8, 5),
+                dpi: int = 100) -> None:
+    """Using tensorboard's data content to plot images"""
+    smooth_key = smooth_key or []
+    os.makedirs(images_dir, exist_ok=True)
+    fname = [fname for fname in os.listdir(tb_dir) if os.path.isfile(os.path.join(tb_dir, fname))][0]
+    tb_path = os.path.join(tb_dir, fname)
+    data = read_tensorboard_file(tb_path)
+
+    for k in data.keys():
+        _data = data[k]
+        steps = [d['step'] for d in _data]
+        values = [d['value'] for d in _data]
+        if len(values) == 0:
+            continue
+        _, ax = plt.subplots(1, 1, squeeze=True, figsize=figsize, dpi=dpi)
+        ax.set_title(k)
+        if len(values) == 1:
+            ax.scatter(steps, values, color=TB_COLOR_SMOOTH)
+        elif k in smooth_key:
+            ax.plot(steps, values, color=TB_COLOR)
+            values_s = tensorboard_smoothing(values, smooth_val)
+            ax.plot(steps, values_s, color=TB_COLOR_SMOOTH)
+        else:
+            ax.plot(steps, values, color=TB_COLOR_SMOOTH)
+        fpath = os.path.join(images_dir, k.replace('/', '_').replace('.', '_'))
+        plt.savefig(fpath, dpi=dpi, bbox_inches='tight')
+        plt.close()
diff --git a/ms-swift/swift/utils/torch_utils.py b/ms-swift/swift/utils/torch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..419150b6658f2fdd8728d09d65406747634e68ea
--- /dev/null
+++ b/ms-swift/swift/utils/torch_utils.py
@@ -0,0 +1,391 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import gc
+import hashlib
+import os
+import pickle
+import re
+import time
+import uuid
+from bisect import bisect_right
+from contextlib import contextmanager, nullcontext
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from datasets.utils.filelock import FileLock
+from modelscope.hub.utils.utils import get_cache_dir
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.utils import is_torch_cuda_available, is_torch_mps_available, is_torch_npu_available
+
+from .env import get_dist_setting, is_dist, is_dist_ta, is_local_master, is_master
+from .logger import get_logger
+from .utils import deep_getattr
+
+logger = get_logger()
+
+
+def _find_local_mac() -> str:
+    mac = uuid.getnode()
+    mac_address = ':'.join(('%012x' % mac)[i:i + 2] for i in range(0, 12, 2))
+    return mac_address
+
+
+def get_n_params_grads(model) -> Tuple[List[int], List[int]]:
+    n_params, n_grads = [], []
+    for p in model.parameters():
+        if is_deepspeed_zero3_enabled():
+            import deepspeed
+            context = deepspeed.zero.GatheredParameters(p)
+        else:
+            context = nullcontext()
+        with context:
+            n_params.append(p.numel())
+            n_grads.append(p.numel() if p.requires_grad else 0)
+    return n_params, n_grads
+
+
+def get_model_parameter_info(model: nn.Module, name: Optional[str] = None) -> str:
+    n_params, n_grads = get_n_params_grads(model)
+    n_params = sum(n_params)
+    n_grads = sum(n_grads)
+    n_buffers = sum(p.numel() for p in model.buffers())
+
+    if name is None:
+        name = model.__class__.__name__
+
+    n_params /= 1e6
+    n_grads /= 1e6
+    n_buffers /= 1e6
+    s = (f'{name}: '
+         f'{n_params:.4f}M Params ({n_grads:.4f}M Trainable '
+         f'[{100 * n_grads / n_params:.4f}%]), '
+         f'{n_buffers:.4f}M Buffers.')
+    return s
+
+
+def find_sub_module(module: torch.nn.Module, module_name: str) -> List[torch.nn.Module]:
+    _modules = list()
+    for name, sub_module in module.named_modules():
+        if not name:
+            continue
+        if name.endswith(module_name):
+            _modules.append(sub_module)
+    return _modules
+
+
+def show_layers(model: nn.Module, max_lines: Optional[int] = 20) -> None:
+    named_p = list(model.named_parameters())
+    for i, (n, p) in enumerate(named_p):
+        if max_lines is not None and i >= max_lines:
+            logger.info('...')
+            break
+        logger.info(f'[{n}]: requires_grad={p.requires_grad}, dtype={p.dtype}, device={p.device}')
+
+
+def freeze_parameters(model: nn.Module,
+                      freeze_parameters_ratio: float,
+                      freeze_parameters: List[str],
+                      freeze_parameters_regex: Optional[str] = None) -> None:
+    if freeze_parameters_ratio > 0:
+        n_parameters = get_n_params_grads(model)[0]
+        n_parameters = np.array(n_parameters, dtype=np.int64)
+        n_freeze_parameters = int(np.sum(n_parameters) * freeze_parameters_ratio)
+        n_parameters_cs = np.cumsum(n_parameters)
+        idx = bisect_right(n_parameters_cs, n_freeze_parameters)
+        for _, p in zip(range(idx), model.parameters()):
+            p.requires_grad = False
+
+    if len(freeze_parameters) > 0:
+        for n, p in model.named_parameters():
+            for freeze_p in freeze_parameters:
+                if n.startswith(freeze_p):
+                    p.requires_grad = False
+
+    if freeze_parameters_regex is not None:
+        try:
+            pattern = re.compile(freeze_parameters_regex)
+        except re.error as e:
+            logger.warning(f"Invalid freeze_parameters_regex '{freeze_parameters_regex}': {e}")
+            return
+
+        for n, p in model.named_parameters():
+            if pattern.search(n):
+                p.requires_grad = False
+
+
+def activate_parameters(model: nn.Module,
+                        additional_trainable_parameters: List[str],
+                        trainable_parameters_regex: Optional[str] = None) -> None:
+    has_activate = False
+    if len(additional_trainable_parameters) > 0:
+        for n, p in model.named_parameters():
+            for additional_tp in additional_trainable_parameters:
+                if n.startswith(additional_tp):
+                    p.requires_grad = True
+                    has_activate = True
+        if not has_activate:
+            logger.warning('len(additional_trainable_parameters) > 0 but no parameters are activated. '
+                           f'additional_trainable_parameters: {additional_trainable_parameters}')
+
+    has_activate = False
+    if trainable_parameters_regex is not None:
+        try:
+            pattern = re.compile(trainable_parameters_regex)
+        except re.error as e:
+            logger.warning(f"Invalid trainable_parameters_regex '{trainable_parameters_regex}': {e}")
+            return
+
+        for n, p in model.named_parameters():
+            if pattern.search(n):
+                p.requires_grad = True
+                has_activate = True
+
+        if not has_activate:
+            logger.warning('trainable_parameters_regex is provided but no parameters are activated. '
+                           f'trainable_parameters_regex: {trainable_parameters_regex}')
+
+
+def time_synchronize() -> float:
+    torch.cuda.synchronize()
+    return time.perf_counter()  # second
+
+
+def _get_max_memory(device_ids: List[int]) -> Dict[Union[int, str], int]:
+    """add feat in accelerate to support MP + DDP"""
+    import psutil
+    # Make sure CUDA is initialized on each GPU to have the right memory info.
+    for i in device_ids:
+        _ = torch.tensor([0], device=i)
+
+    device_ids_set = set(device_ids)
+    max_memory = {}
+    for i in range(get_device_count()):
+        max_memory[i] = 0
+        if i in device_ids_set:
+            max_memory[i] = torch.cuda.mem_get_info(i)[0]
+    max_memory['cpu'] = psutil.virtual_memory().available
+    return max_memory
+
+
+def _sync_max_memory(max_memory: Dict[Union[int, str], int]) -> Dict[Union[int, str], int]:
+    """Make sure that the model structure of MP(device_map) is the same, when using DDP."""
+    max_memory_list = [v for k, v in max_memory.items() if (v > 0 and k != 'cpu')]
+    _, local_rank, world_size, _ = get_dist_setting()
+    src_tensor = torch.tensor(max_memory_list).to(local_rank)
+    tgt_tensor_list = [torch.zeros_like(src_tensor) for _ in range(world_size)]
+    dist.all_gather(tgt_tensor_list, src_tensor)
+    tgt_tensor = torch.stack(tgt_tensor_list, dim=0)
+    new_max_memory_iter = iter(tgt_tensor.min(dim=0)[0].tolist())
+    new_max_memory = {}
+    for k, v in max_memory.items():
+        new_max_memory[k] = v
+        if v > 0 and k != 'cpu':
+            new_max_memory[k] = next(new_max_memory_iter)
+    return new_max_memory
+
+
+def find_layers(
+    model: nn.Module,
+    cond: Callable[[str, nn.Module], bool],
+    sub_module: Optional[str] = None,
+    min_name_len: Optional[int] = None,
+) -> List[str]:
+    # The content of target_module_names cannot exist in inner_nodes.
+    sub_module_str = sub_module
+    if sub_module is None:
+        sub_module = model
+    else:
+        sub_module = deep_getattr(model, sub_module)
+    inner_nodes = set()
+    for name, module in model.named_modules():
+        name = re.sub(r'\d+\.', '{}.', name)
+        if not cond(name, module):
+            inner_nodes.add(name)
+    target_module_names = set()
+    for name, module in sub_module.named_modules():
+        if sub_module_str:
+            name = f'{sub_module_str}.{name}' if name else sub_module_str
+        if cond(name, module):
+            module_name_list = name.split('.')
+            module_name = module_name_list.pop()
+            i = 1
+            for inner_node in inner_nodes:
+                while module_name_list and inner_node.endswith(re.sub(
+                        r'\d+\.', '{}.', module_name)) or min_name_len and i < min_name_len:
+                    module_name = f'{module_name_list.pop()}.{module_name}'
+                    i += 1
+            target_module_names.add(module_name)
+    return list(target_module_names)
+
+
+def find_norm(model: nn.Module) -> List[str]:
+    # find_layer_norm
+    return find_layers(
+        model,
+        lambda name, module: isinstance(module, torch.nn.LayerNorm) or 'rmsnorm' in module.__class__.__name__.lower())
+
+
+def find_embedding(model: nn.Module) -> List[str]:
+    return find_layers(model, lambda name, module: isinstance(module, torch.nn.Embedding))
+
+
+def find_all_linears(model, model_arch=None, extra_layers=None, sub_module=None):
+    if model_arch is None:
+        from swift.llm import get_model_arch
+        model_arch = get_model_arch(model.model_meta.model_arch)
+    # lm_head
+    if model_arch and model_arch.lm_head:
+        output = model_arch.lm_head
+        idx = output.rfind('.')
+        lm_head_name = output[idx + 1:]
+    else:
+        lm_head_name = 'lm_head'
+    # 'score', 'classifier': classification model
+    # 'v_head': reward model
+    ignore_layers = [lm_head_name, 'score', 'v_head', 'classifier'] + ['lora_A', 'lora_B', 'base_layer']
+    ignore_linear_cls = [
+        'glulinear'  # phi4-mm
+    ]
+
+    def _cond(name, module):
+        module_name = module.__class__.__name__.lower()
+        if (extra_layers and isinstance(module, tuple(extra_layers)) or
+            ('linear' in module_name and all(linear_cls not in module_name
+                                             for linear_cls in ignore_linear_cls))) and all(layer not in name
+                                                                                            for layer in ignore_layers):
+            return True
+        return False
+
+    return find_layers(model, _cond, sub_module=sub_module)
+
+
+@contextmanager
+def safe_ddp_context(hash_id: Optional[str], use_barrier: bool = False):
+    if use_barrier and dist.is_initialized():
+        if is_dist() or is_dist_ta():
+            if not is_master():
+                dist.barrier()
+            if not is_local_master():
+                # Compatible with multi-machine scenarios,
+                # where each machine uses different storage hardware.
+                dist.barrier()
+        yield
+        if is_dist() or is_dist_ta():
+            if is_master():
+                dist.barrier()
+            if is_local_master():
+                dist.barrier()
+    elif hash_id is not None:
+        lock_dir = os.path.join(get_cache_dir(), 'lockers')
+        os.makedirs(lock_dir, exist_ok=True)
+        file_path = hashlib.sha256(hash_id.encode('utf-8')).hexdigest() + '.lock'
+        file_path = os.path.join(lock_dir, file_path)
+        with FileLock(file_path):
+            yield
+    else:
+        yield
+
+
+def get_device(local_rank: Optional[Union[str, int]] = None) -> str:
+    if local_rank is None:
+        local_rank = max(0, get_dist_setting()[1])
+    local_rank = str(local_rank)
+    if is_torch_npu_available():
+        device = 'npu:{}'.format(local_rank)
+    elif is_torch_mps_available():
+        device = 'mps:{}'.format(local_rank)
+    elif is_torch_cuda_available():
+        device = 'cuda:{}'.format(local_rank)
+    else:
+        device = 'cpu'
+
+    return device
+
+
+def get_current_device():
+    if is_torch_npu_available():
+        current_device = torch.npu.current_device()
+    elif is_torch_cuda_available():
+        current_device = torch.cuda.current_device()
+    elif is_torch_mps_available():
+        current_device = 'mps'
+    else:
+        current_device = 'cpu'
+    return current_device
+
+
+def set_device(local_rank: Optional[Union[str, int]] = None):
+    if local_rank is None:
+        local_rank = max(0, get_dist_setting()[1])
+    if is_torch_npu_available():
+        torch.npu.set_device(local_rank)
+    elif is_torch_cuda_available():
+        torch.cuda.set_device(local_rank)
+
+
+def get_device_count() -> int:
+    if is_torch_npu_available():
+        return torch.npu.device_count()
+    elif is_torch_cuda_available():
+        return torch.cuda.device_count()
+    else:
+        return 0
+
+
+def gc_collect() -> None:
+    gc.collect()
+    if is_torch_npu_available():
+        torch.npu.empty_cache()
+    elif is_torch_mps_available():
+        torch.mps.empty_cache()
+    elif is_torch_cuda_available():
+        torch.cuda.empty_cache()
+
+
+class Serializer:
+
+    @staticmethod
+    def to_tensor(obj):
+        res = pickle.dumps(obj)
+        res = np.array([len(res)], dtype=np.int64).tobytes() + res
+        res = np.frombuffer(res, dtype=np.uint8).copy()
+        res = torch.from_numpy(res)
+        return res
+
+    @staticmethod
+    def from_tensor(obj):
+        if isinstance(obj, torch.Tensor):
+            obj = obj.cpu().numpy()
+        res = obj.tobytes()
+        buffer_size = np.frombuffer(res[:8], dtype=np.int64)[0]
+        res = res[8:]
+        return pickle.loads(res[:buffer_size])
+
+
+def set_default_ddp_config():
+    # It runs normally with Python as well.
+    rank = int(os.getenv('RANK', -1))
+    if rank == -1:
+        os.environ['NPROC_PER_NODE'] = '1'
+        os.environ['RANK'] = '0'
+        os.environ['LOCAL_RANK'] = '0'
+        os.environ['WORLD_SIZE'] = '1'
+        os.environ['LOCAL_WORLD_SIZE'] = '1'
+        os.environ['MASTER_ADDR'] = '127.0.0.1'
+        os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', '29500')
+
+
+def init_process_group(ddp_backend: Optional[str] = None):
+    if dist.is_initialized():
+        return
+    set_device()
+    if ddp_backend is None:
+        if is_torch_npu_available():
+            ddp_backend = 'hccl'
+        elif torch.cuda.is_available():
+            ddp_backend = 'nccl'
+        else:
+            ddp_backend = 'gloo'
+    dist.init_process_group(backend=ddp_backend)
diff --git a/ms-swift/tests/deploy/test_dataset.py b/ms-swift/tests/deploy/test_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..505a0519cdc85f0977df9dfb07fd58fe0e6bc8cb
--- /dev/null
+++ b/ms-swift/tests/deploy/test_dataset.py
@@ -0,0 +1,61 @@
+def _test_client(port=8000):
+    import time
+    import aiohttp
+    from swift.llm import InferClient, InferRequest, RequestConfig, load_dataset, run_deploy
+    dataset = load_dataset(['AI-ModelScope/alpaca-gpt4-data-zh#1000'], num_proc=4)
+    infer_client = InferClient(port=port)
+    while True:
+        try:
+            infer_client.models
+            break
+        except Exception:
+            time.sleep(1)
+            pass
+    infer_requests = []
+    for data in dataset[0]:
+        infer_requests.append(InferRequest(**data))
+    request_config = RequestConfig(seed=42, max_tokens=256, temperature=0.8)
+
+    resp = infer_client.infer(infer_requests, request_config=request_config, use_tqdm=False)
+    print(len(resp))
+
+
+def _test(infer_backend):
+    import os
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+    from swift.llm import DeployArguments
+    from swift.llm import run_deploy
+    args = DeployArguments(model='Qwen/Qwen2-7B-Instruct', infer_backend=infer_backend, verbose=False)
+    with run_deploy(args) as port:
+        _test_client(port)
+
+
+def test_vllm():
+    _test('vllm')
+
+
+def test_lmdeploy():
+    _test('lmdeploy')
+
+
+def test_pt():
+    _test('pt')
+
+
+def test_vllm_origin():
+    import subprocess
+    import sys
+    from modelscope import snapshot_download
+    model_dir = snapshot_download('Qwen/Qwen2-7B-Instruct')
+    args = [sys.executable, '-m', 'vllm.entrypoints.openai.api_server', '--model', model_dir]
+    process = subprocess.Popen(args)
+    _test_client()
+    process.terminate()
+
+
+if __name__ == '__main__':
+    # test_vllm_origin()
+    # test_vllm()
+    test_lmdeploy()
+    # test_pt()
diff --git a/ms-swift/tests/deploy/test_logprobs.py b/ms-swift/tests/deploy/test_logprobs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba7667d44cfcd450f7bf8b87862b6d5a5f36be39
--- /dev/null
+++ b/ms-swift/tests/deploy/test_logprobs.py
@@ -0,0 +1,113 @@
+def _test_client(port: int, print_logprobs: bool = False, test_vlm: bool = False):
+    import requests
+    import time
+    import aiohttp
+    from pprint import pprint
+    from swift.llm import InferClient, InferRequest, RequestConfig
+
+    infer_client = InferClient(port=port)
+
+    while True:
+        try:
+            models = infer_client.models
+            print(f'models: {models}')
+        except aiohttp.ClientConnectorError:
+            time.sleep(5)
+            continue
+        break
+
+    if test_vlm:
+        query = '这是什么'
+        # http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png
+        messages = [{
+            'role':
+            'user',
+            'content': [
+                {
+                    'type': 'text',
+                    'text': '这是什么'
+                },
+                {
+                    'type': 'image_url',
+                    'image_url': {
+                        'url': 'cat.png'
+                    }
+                },
+            ]
+        }]
+    else:
+        query = '123*234=?'
+        messages = [{'role': 'user', 'content': query}]
+
+    infer_request = InferRequest(messages=messages)
+    request_config = RequestConfig(seed=42, max_tokens=256, temperature=0.8, logprobs=True, top_logprobs=5)
+
+    resp = infer_client.infer([infer_request], request_config=request_config)[0]
+    response = resp.choices[0].message.content
+    print(f'query: {query}')
+    print(f'response: {response}')
+    if print_logprobs:
+        pprint(resp.choices[0].logprobs)
+
+    request_config = RequestConfig(
+        stream=True, seed=42, max_tokens=256, temperature=0.8, top_k=20, top_p=0.8, logprobs=True, top_logprobs=5)
+    gen_list = infer_client.infer([infer_request], request_config=request_config)
+    print(f'query: {query}')
+    print('response: ', end='')
+    for chunk in gen_list[0]:
+        print(chunk.choices[0].delta.content, end='', flush=True)
+        if print_logprobs and chunk.choices[0].logprobs is not None:
+            pprint(chunk.choices[0].logprobs)
+    print()
+
+
+def _test(infer_backend, test_vlm: bool = False):
+    import os
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+    from swift.llm import DeployArguments
+    from swift.llm import deploy_main
+    import multiprocessing
+    mp = multiprocessing.get_context('spawn')
+    model = 'Qwen/Qwen2-VL-7B-Instruct' if test_vlm else 'Qwen/Qwen2-7B-Instruct'
+    args = DeployArguments(model=model, infer_backend=infer_backend, verbose=False)
+    process = mp.Process(target=deploy_main, args=(args, ))
+    process.start()
+    _test_client(args.port, True, test_vlm)
+    process.terminate()
+
+
+def test_vllm_vlm():
+    _test('vllm', test_vlm=True)
+
+
+def test_vllm():
+    _test('vllm')
+
+
+def test_lmdeploy():
+    _test('lmdeploy')
+
+
+def test_pt():
+    _test('pt')
+
+
+def test_vllm_origin():
+    import os
+    import subprocess
+    import sys
+    from modelscope import snapshot_download
+    model_dir = snapshot_download('Qwen/Qwen2-7B-Instruct')
+    args = [sys.executable, '-m', 'vllm.entrypoints.openai.api_server', '--model', model_dir]
+    process = subprocess.Popen(args)
+    _test_client(8000)
+    process.terminate()
+
+
+if __name__ == '__main__':
+    # test_vllm_origin()
+    # test_vllm()
+    test_vllm_vlm()
+    # test_lmdeploy()
+    # test_pt()
diff --git a/ms-swift/tests/infer/test_agent.py b/ms-swift/tests/infer/test_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..38da516bfcdebc463571f249440c3873b79c9adf
--- /dev/null
+++ b/ms-swift/tests/infer/test_agent.py
@@ -0,0 +1,30 @@
+import os
+
+import torch
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+kwargs = {
+    'per_device_train_batch_size': 2,
+    'save_steps': 50,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+}
+
+
+def test_sft():
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+    from swift.llm import sft_main, TrainArguments, infer_main, InferArguments
+    sft_main(
+        TrainArguments(model='Qwen/Qwen2-7B-Instruct', dataset=['iic/ms_agent#2000'], loss_scale='react', **kwargs))
+
+
+def test_infer():
+    from swift.llm import infer_main, InferArguments
+    ckpt_dir = 'output/Qwen2-7B-Instruct/v229-20241126-133152/checkpoint-100'
+    infer_main(InferArguments(ckpt_dir=ckpt_dir))
+
+
+if __name__ == '__main__':
+    test_sft()
+    # test_infer()
diff --git a/ms-swift/tests/infer/test_max_memory.py b/ms-swift/tests/infer/test_max_memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..142a2194803fee5febad214c0c22a90743ed1338
--- /dev/null
+++ b/ms-swift/tests/infer/test_max_memory.py
@@ -0,0 +1,10 @@
+from swift.llm import InferArguments, infer_main
+
+
+def test_max_memory():
+    infer_main(
+        InferArguments(model='Qwen/Qwen2.5-7B-Instruct', max_memory='{0: "50GB", 1: "5GB"}', device_map='sequential'))
+
+
+if __name__ == '__main__':
+    test_max_memory()
diff --git a/ms-swift/tests/llm/data/alpaca.jsonl b/ms-swift/tests/llm/data/alpaca.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89802b51b3f7f71b319fe59342d46aa6c13e8bab
--- /dev/null
+++ b/ms-swift/tests/llm/data/alpaca.jsonl
@@ -0,0 +1,3 @@
+{"instruction": "11111", "input": "22222", "output": "33333", "history": [["aaaaa", "bbbbb"]], "system": "system123"}
+{"instruction": "aaaaa", "output": "ccccc"}
+{"instruction": "AAAAA", "input": "BBBBB", "output": "CCCCC"}
diff --git a/ms-swift/tests/llm/data/conversations.jsonl b/ms-swift/tests/llm/data/conversations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85142eaa1a87197cd659734acb032aa6c0c71ab2
--- /dev/null
+++ b/ms-swift/tests/llm/data/conversations.jsonl
@@ -0,0 +1,3 @@
+{"conversations": [{"from": "system", "value": "00000"}, {"from": "user", "value": "11111"}, {"from": "assistant", "value": "22222"}]}
+{"conversations": [{"from": "user", "value": "aaaaa"}, {"from": "assistant", "value": "bbbbb"}, {"from": "user", "value": "ccccc"}, {"from": "assistant", "value": "ddddd"}]}
+{"conversations": [{"from": "user", "value": "AAAAA"}, {"from": "assistant", "value": "BBBBB"}, {"from": "user", "value": "CCCCC"}, {"from": "assistant", "value": "DDDDD"}]}
diff --git a/ms-swift/tests/llm/data/multi_modal_2.jsonl b/ms-swift/tests/llm/data/multi_modal_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..351b784f2e7e2095de9de7a30be13108ca6d3cbb
--- /dev/null
+++ b/ms-swift/tests/llm/data/multi_modal_2.jsonl
@@ -0,0 +1,3 @@
+{"query": "55555", "response": "66666", "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
+{"query": "eeeee", "response": "fffff", "history": [], "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
+{"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]], "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
diff --git a/ms-swift/tests/llm/data/multi_modal_3.jsonl b/ms-swift/tests/llm/data/multi_modal_3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbc439d6d11910b5ec1b7ad706702292d0d8eb08
--- /dev/null
+++ b/ms-swift/tests/llm/data/multi_modal_3.jsonl
@@ -0,0 +1,3 @@
+{"query": "55555", "response": "66666", "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
+{"query": "eeeee", "response": "fffff", "history": [], "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
+{"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]], "images": ["https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"]}
diff --git a/ms-swift/tests/llm/data/swift_multi.json b/ms-swift/tests/llm/data/swift_multi.json
new file mode 100644
index 0000000000000000000000000000000000000000..e1e8b50ae1bd67f6976e8c8e2bafc042e1c5e8df
--- /dev/null
+++ b/ms-swift/tests/llm/data/swift_multi.json
@@ -0,0 +1,3 @@
+[{"system": "00000", "query": "55555", "response": "66666"},
+{"query": "eeeee", "response": "fffff", "history": []},
+{"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]]}]
diff --git a/ms-swift/tests/llm/data/swift_pre.jsonl b/ms-swift/tests/llm/data/swift_pre.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9dadedece652ca8d79c0492c1d677eb85c3ad8a
--- /dev/null
+++ b/ms-swift/tests/llm/data/swift_pre.jsonl
@@ -0,0 +1,3 @@
+{"response": "11111"}
+{"response": "aaaaa"}
+{"response": "AAAAA"}
diff --git a/ms-swift/tests/megatron/test_align/test_llm.py b/ms-swift/tests/megatron/test_align/test_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c5cdf3946baacfeae359c8996e702cdd22f8368
--- /dev/null
+++ b/ms-swift/tests/megatron/test_align/test_llm.py
@@ -0,0 +1,94 @@
+import os
+
+import torch
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def _test_model(model_id):
+    from swift.llm import export_main, ExportArguments
+    if model_id.endswith('mcore'):
+        export_main(
+            ExportArguments(
+                mcore_model=model_id,
+                to_hf=True,
+                exist_ok=True,
+                test_convert_precision=True,
+                torch_dtype=torch.bfloat16))
+    else:
+        export_main(
+            ExportArguments(
+                model=model_id,
+                to_mcore=True,
+                exist_ok=True,
+                test_convert_precision=True,
+                torch_dtype=torch.bfloat16,
+            ))
+
+
+def test_qwen2():
+    _test_model('Qwen/Qwen2-0.5B-Instruct')
+
+
+def test_llama2():
+    _test_model('modelscope/Llama-2-7b-chat-ms')
+
+
+def test_llama3():
+    _test_model('LLM-Research/Meta-Llama-3-8B-Instruct')
+
+
+def test_marco_o1():
+    _test_model('AIDC-AI/Marco-o1')
+
+
+def test_deepseek_r1_llama():
+    _test_model('deepseek-ai/DeepSeek-R1-Distill-Llama-8B')
+
+
+def test_deepseek_r1_qwen():
+    _test_model('deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B')
+
+
+def test_yi():
+    _test_model('01ai/Yi-1.5-6B-Chat')
+
+
+def test_megrez():
+    _test_model('InfiniAI/Megrez-3b-Instruct')
+
+
+def test_llama3_1():
+    _test_model('LLM-Research/Meta-Llama-3.1-8B-Instruct')
+
+
+def test_llama3_2():
+    _test_model('LLM-Research/Llama-3.2-1B-Instruct')
+
+
+def test_qwen3():
+    _test_model('Qwen/Qwen3-0.6B-Base')
+
+
+def test_qwen2_moe():
+    _test_model('Qwen/Qwen1.5-MoE-A2.7B-Chat')
+
+
+def test_qwen3_moe():
+    _test_model('Qwen/Qwen3-15B-A2B-Base')
+
+
+if __name__ == '__main__':
+    # test_qwen2()
+    # test_llama2()
+    # test_llama3()
+    # test_marco_o1()
+    # test_deepseek_r1_llama()
+    # test_deepseek_r1_qwen()
+    # test_yi()
+    # test_megrez()
+    # test_llama3_1()
+    # test_llama3_2()
+    # test_qwen3()
+    # test_qwen2_moe()
+    test_qwen3_moe()
diff --git a/ms-swift/tests/megatron/test_model.py b/ms-swift/tests/megatron/test_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e154879ddb7e33ef729fb7a1d589ba87e7cf2b
--- /dev/null
+++ b/ms-swift/tests/megatron/test_model.py
@@ -0,0 +1,65 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ['MASTER_PORT'] = '29560'
+
+
+def get_mg_model_tokenizer(model_id):
+    from megatron.training.initialize import initialize_megatron
+    set_default_ddp_config()
+    hf_model, processor = get_model_tokenizer(model_id, torch_dtype=torch.float32)
+    megatron_model_meta = get_megatron_model_meta(processor.model_meta.model_type)
+    model_info = processor.model_info
+    kwargs = megatron_model_meta.convert_hf_config(model_info.config)
+    megatron_args = MegatronArguments(**kwargs, seq_length=1, use_cpu_initialization=True, no_initialization=True)
+    patch_megatron_tokenizer(processor)
+    extra_args = megatron_args.parse_to_megatron()
+    initialize_megatron(args_defaults=extra_args)
+    mg_model = megatron_model_meta.model_provider()
+    megatron_model_meta.convert_hf2mcore(hf_model, mg_model)
+    return hf_model, mg_model, processor
+
+
+def test_bf16_fp32():
+    hf_model_fp32, processor = get_model_tokenizer(model_id, torch_dtype=torch.float32)
+    hf_model_bf16, processor = get_model_tokenizer(model_id, torch_dtype=torch.bfloat16)
+    template = get_template(hf_model_fp32.model_meta.template, processor)
+    input_ids = template.encode(InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]))['input_ids']
+    input_ids = torch.tensor(input_ids)[None].to('cuda')
+    with torch.inference_mode():
+        hf_logits_fp32 = hf_model_fp32(input_ids).logits
+        hf_logits_bf16 = hf_model_bf16(input_ids).logits
+    mean_diff = (hf_logits_fp32 - hf_logits_bf16).abs().mean().item()
+    max_diff = (hf_logits_fp32 - hf_logits_bf16).abs().max().item()
+    # mean_diff: 0.13342587649822235, max_diff: 7.1983513832092285
+    print(f'mean_diff: {mean_diff}, max_diff: {max_diff}')
+
+
+def test_align(hf_model, mg_model, processor):
+    from megatron.training.utils import get_ltor_masks_and_position_ids
+    template = get_template(hf_model.model_meta.template, processor)
+    input_ids = template.encode(InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]))['input_ids']
+    input_ids = torch.tensor(input_ids)[None].to('cuda')
+    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(input_ids, -100, True, True, True)
+    with torch.inference_mode():
+        hf_model.cuda()
+        mg_model.cuda()
+        hf_logits = hf_model(input_ids).logits
+        mg_logits = mg_model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids)
+    mean_diff = (mg_logits - hf_logits).abs().mean().item()
+    max_diff = (mg_logits - hf_logits).abs().max().item()
+    print(f'mean_diff: {mean_diff}, max_diff: {max_diff}')
+
+
+model_id = 'Qwen/Qwen2-7B-Instruct'
+
+if __name__ == '__main__':
+    import torch
+    from swift.llm import InferRequest, get_model_tokenizer, get_template
+    from swift.utils import set_default_ddp_config
+    from swift.megatron.argument import MegatronArguments
+    from swift.megatron.model import get_megatron_model_meta
+    from swift.megatron.utils import patch_megatron_tokenizer
+    # test_bf16_fp32()
+    hf_model, mg_model, processor = get_mg_model_tokenizer(model_id)
+    test_align(hf_model, mg_model, processor)
diff --git a/ms-swift/tests/megatron/test_save.py b/ms-swift/tests/megatron/test_save.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfc78182ae8e3e532fd758496e5dd754eb9fd395
--- /dev/null
+++ b/ms-swift/tests/megatron/test_save.py
@@ -0,0 +1,61 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def get_mg_model_tokenizer():
+    model_id = 'Qwen/Qwen2.5-7B-Instruct'
+    hf_model_id = 'Qwen/Qwen2.5-7B'
+    from megatron.training.initialize import initialize_megatron
+    set_default_ddp_config()
+    hf_model, _ = get_model_tokenizer(hf_model_id, torch_dtype=torch.float32)
+    _, processor = get_model_tokenizer(model_id, load_model=False)
+    megatron_model_meta = get_megatron_model_meta(processor.model_meta.model_type)
+    model_info = processor.model_info
+    kwargs = megatron_model_meta.convert_hf_config(model_info.config)
+    megatron_args = MegatronArguments(
+        **kwargs,
+        seq_length=1,
+        use_cpu_initialization=True,
+        no_initialization=True,
+        load='Qwen2-7B-Instruct-mcore',
+        save='mcore-hf-test',
+        no_load_optim=True,
+        no_load_rng=True)
+    patch_megatron_tokenizer(processor)
+    extra_args = megatron_args.parse_to_megatron()
+    initialize_megatron(args_defaults=extra_args)
+    mg_model = megatron_model_meta.model_provider()
+    megatron_model_meta.convert_mcore2hf(hf_model, mg_model)
+    return hf_model, mg_model, processor
+
+
+def test_align(hf_model, mg_model, processor):
+    from megatron.training.utils import get_ltor_masks_and_position_ids
+    template = get_template(hf_model.model_meta.template, processor)
+    input_ids = template.encode(InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]))['input_ids']
+    input_ids = torch.tensor(input_ids)[None].to('cuda')
+    attention_mask, _, position_ids = get_ltor_masks_and_position_ids(input_ids, -100, True, True, True)
+    with torch.inference_mode():
+        hf_model.cuda()
+        mg_model.cuda()
+        hf_logits = hf_model(input_ids).logits
+        mg_logits = mg_model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids)
+    mean_diff = (mg_logits - hf_logits).abs().mean().item()
+    max_diff = (mg_logits - hf_logits).abs().max().item()
+    print(f'mean_diff: {mean_diff}, max_diff: {max_diff}')
+
+
+def test_save():
+    hf_model, mg_model, processor = get_mg_model_tokenizer()
+    test_align(hf_model, mg_model, processor)
+
+
+if __name__ == '__main__':
+    import torch
+    from swift.llm import InferRequest, get_model_tokenizer, get_template
+    from swift.utils import set_default_ddp_config
+    from swift.megatron.argument import MegatronArguments
+    from swift.megatron.model import get_megatron_model_meta
+    from swift.megatron.utils import patch_megatron_tokenizer
+    test_save()
diff --git a/ms-swift/tests/megatron/test_train.py b/ms-swift/tests/megatron/test_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..165be0b42b7648bc84febe0e5418b267aac5b2bf
--- /dev/null
+++ b/ms-swift/tests/megatron/test_train.py
@@ -0,0 +1,37 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+
+
+def test_sft():
+    from swift.megatron import megatron_sft_main, MegatronTrainArguments
+    megatron_sft_main(
+        MegatronTrainArguments(
+            load='Qwen2-7B-Instruct-mcore',
+            dataset=[
+                'AI-ModelScope/alpaca-gpt4-data-zh#500', 'swift/self-cognition#500',
+                'AI-ModelScope/alpaca-gpt4-data-en#500'
+            ],
+            tensor_model_parallel_size=2,
+            train_iters=100,
+            model_author='swift',
+            model_name='swift-robot',
+            eval_iters=5,
+            finetune=True))
+
+
+def test_pt():
+    from swift.megatron import megatron_pt_main, MegatronTrainArguments
+    megatron_pt_main(
+        MegatronTrainArguments(
+            load='Qwen2-7B-mcore',
+            dataset=['AI-ModelScope/alpaca-gpt4-data-zh#500', 'AI-ModelScope/alpaca-gpt4-data-en#500'],
+            tensor_model_parallel_size=2,
+            train_iters=200,
+            eval_iters=5,
+            finetune=True))
+
+
+if __name__ == '__main__':
+    # test_sft()
+    test_pt()
diff --git a/ms-swift/tests/models/test_llm.py b/ms-swift/tests/models/test_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f60c811f420a5fee45e93c267f877ce5b22bb256
--- /dev/null
+++ b/ms-swift/tests/models/test_llm.py
@@ -0,0 +1,16 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
+
+
+def test_llama3():
+    from swift.llm import infer_main, InferArguments
+    infer_main(
+        InferArguments(
+            model='LLM-Research/Meta-Llama-3.1-8B-Instruct',
+            max_batch_size=2,
+            val_dataset='AI-ModelScope/alpaca-gpt4-data-en#2'))
+
+
+if __name__ == '__main__':
+    test_llama3()
diff --git a/ms-swift/tests/models/test_mllm.py b/ms-swift/tests/models/test_mllm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e4bd485228863100ca6ae4686fdae71acfbbf4
--- /dev/null
+++ b/ms-swift/tests/models/test_mllm.py
@@ -0,0 +1,16 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def test_cogvlm():
+    from swift.llm import infer_main, InferArguments, sft_main, TrainArguments
+    # infer_main(InferArguments(model='ZhipuAI/cogvlm2-video-llama3-chat'))
+    sft_main(
+        TrainArguments(
+            model='ZhipuAI/cogvlm2-video-llama3-chat',
+            dataset=['AI-ModelScope/alpaca-gpt4-data-zh#200', 'swift/VideoChatGPT:Generic#200']))
+
+
+if __name__ == '__main__':
+    test_cogvlm()
diff --git a/ms-swift/tests/run.py b/ms-swift/tests/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f6d7b6aeef371f262f200db61078d374b42cb87
--- /dev/null
+++ b/ms-swift/tests/run.py
@@ -0,0 +1,622 @@
+#!/usr/bin/env python
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import argparse
+import datetime
+import math
+import os
+import subprocess
+import sys
+sys.path.insert(0, '/swift')
+import tempfile
+import time
+import unittest
+from fnmatch import fnmatch
+from pathlib import Path
+from unittest import TextTestResult
+
+import pandas
+# NOTICE: Tensorflow 1.15 seems not so compatible with pytorch.
+#         A segmentation fault may be raise by pytorch cpp library
+#         if 'import tensorflow' in front of 'import torch'.
+#         Putting a 'import torch' here can bypass this incompatibility.
+import torch
+import yaml
+from model_tag import ModelTag, commit_model_ut_result
+from test_utils import get_case_model_info
+
+from swift.utils.logger import get_logger
+
+logger = get_logger()
+
+
+def test_cases_result_to_df(result_list):
+    table_header = [
+        'Name', 'Result', 'Info', 'Start time', 'Stop time',
+        'Time cost(seconds)'
+    ]
+    df = pandas.DataFrame(
+        result_list, columns=table_header).sort_values(
+            by=['Start time'], ascending=True)
+    return df
+
+
+def statistics_test_result(df):
+    total_cases = df.shape[0]
+    # yapf: disable
+    success_cases = df.loc[df['Result'] == 'Success'].shape[0]
+    error_cases = df.loc[df['Result'] == 'Error'].shape[0]
+    failures_cases = df.loc[df['Result'] == 'Failures'].shape[0]
+    expected_failure_cases = df.loc[df['Result'] == 'ExpectedFailures'].shape[0]
+    unexpected_success_cases = df.loc[df['Result'] == 'UnexpectedSuccesses'].shape[0]
+    skipped_cases = df.loc[df['Result'] == 'Skipped'].shape[0]
+    # yapf: enable
+
+    if failures_cases > 0 or \
+       error_cases > 0 or \
+       unexpected_success_cases > 0:
+        final_result = 'FAILED'
+    else:
+        final_result = 'SUCCESS'
+    result_msg = '%s (Runs=%s,success=%s,failures=%s,errors=%s,\
+    skipped=%s,expected failures=%s,unexpected successes=%s)' % (
+        final_result, total_cases, success_cases, failures_cases, error_cases,
+        skipped_cases, expected_failure_cases, unexpected_success_cases)
+
+    model_cases = get_case_model_info()
+    for model_name, case_info in model_cases.items():
+        cases = df.loc[df['Name'].str.contains('|'.join(list(case_info)))]
+        results = cases['Result']
+        result = None
+        if any(results == 'Error') or any(results == 'Failures') or any(
+                results == 'UnexpectedSuccesses'):
+            result = ModelTag.MODEL_FAIL
+        elif any(results == 'Success'):
+            result = ModelTag.MODEL_PASS
+        elif all(results == 'Skipped'):
+            result = ModelTag.MODEL_SKIP
+        else:
+            print(f'invalid results for {model_name} \n{result}')
+
+        if result is not None:
+            commit_model_ut_result(model_name, result)
+    print('Testing result summary.')
+    print(result_msg)
+    if final_result == 'FAILED':
+        sys.exit(1)
+
+
+def gather_test_suites_in_files(test_dir, case_file_list, list_tests):
+    test_suite = unittest.TestSuite()
+    for case in case_file_list:
+        test_case = unittest.defaultTestLoader.discover(
+            start_dir=test_dir, pattern=case)
+        test_suite.addTest(test_case)
+        if hasattr(test_case, '__iter__'):
+            for subcase in test_case:
+                if list_tests:
+                    print(subcase)
+        else:
+            if list_tests:
+                print(test_case)
+    return test_suite
+
+
+def gather_test_suites_files(test_dir, pattern):
+    case_file_list = []
+    for dirpath, dirnames, filenames in os.walk(test_dir):
+        for file in filenames:
+            if fnmatch(file, pattern):
+                case_file_list.append(file)
+
+    return case_file_list
+
+
+def collect_test_results(case_results):
+    result_list = [
+    ]  # each item is Case, Result, Start time, Stop time, Time cost
+    for case_result in case_results.successes:
+        result_list.append(
+            (case_result.test_full_name, 'Success', '', case_result.start_time,
+             case_result.stop_time, case_result.time_cost))
+    for case_result in case_results.errors:
+        result_list.append(
+            (case_result[0].test_full_name, 'Error', case_result[1],
+             case_result[0].start_time, case_result[0].stop_time,
+             case_result[0].time_cost))
+    for case_result in case_results.skipped:
+        result_list.append(
+            (case_result[0].test_full_name, 'Skipped', case_result[1],
+             case_result[0].start_time, case_result[0].stop_time,
+             case_result[0].time_cost))
+    for case_result in case_results.expectedFailures:
+        result_list.append(
+            (case_result[0].test_full_name, 'ExpectedFailures', case_result[1],
+             case_result[0].start_time, case_result[0].stop_time,
+             case_result[0].time_cost))
+    for case_result in case_results.failures:
+        result_list.append(
+            (case_result[0].test_full_name, 'Failures', case_result[1],
+             case_result[0].start_time, case_result[0].stop_time,
+             case_result[0].time_cost))
+    for case_result in case_results.unexpectedSuccesses:
+        result_list.append((case_result.test_full_name, 'UnexpectedSuccesses',
+                            '', case_result.start_time, case_result.stop_time,
+                            case_result.time_cost))
+    return result_list
+
+
+def run_command_with_popen(cmd):
+    with subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            bufsize=1,
+            encoding='utf8') as sub_process:
+        for line in iter(sub_process.stdout.readline, ''):
+            sys.stdout.write(line)
+
+
+def async_run_command_with_popen(cmd, device_id):
+    logger.info('Worker id: %s args: %s' % (device_id, cmd))
+    env = os.environ.copy()
+    env['CUDA_VISIBLE_DEVICES'] = '%s' % device_id
+    sub_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        bufsize=1,
+        universal_newlines=True,
+        env=env,
+        encoding='utf8')
+    return sub_process
+
+
+def save_test_result(df, args):
+    if args.result_dir is not None:
+        file_name = str(int(datetime.datetime.now().timestamp() * 1000))
+        os.umask(0)
+        Path(args.result_dir).mkdir(mode=0o777, parents=True, exist_ok=True)
+        Path(os.path.join(args.result_dir, file_name)).touch(
+            mode=0o666, exist_ok=True)
+        df.to_pickle(os.path.join(args.result_dir, file_name))
+
+
+def run_command(cmd):
+    logger.info('Running command: %s' % ' '.join(cmd))
+    response = subprocess.run(
+        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    try:
+        response.check_returncode()
+        logger.info(response.stdout.decode('utf8'))
+    except subprocess.CalledProcessError as error:
+        logger.error(
+            'stdout: %s, stderr: %s' %
+            (response.stdout.decode('utf8'), error.stderr.decode('utf8')))
+
+
+def install_packages(pkgs):
+    cmd = [sys.executable, '-m', 'pip', 'install']
+    for pkg in pkgs:
+        cmd.append(pkg)
+
+    run_command(cmd)
+
+
+def install_requirements(requirements):
+    for req in requirements:
+        cmd = [
+            sys.executable, '-m', 'pip', 'install', '-r',
+            'requirements/%s' % req, '-f',
+            'https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html'
+        ]
+        run_command(cmd)
+
+
+def wait_for_free_worker(workers):
+    while True:
+        for idx, worker in enumerate(workers):
+            if worker is None:
+                logger.info('return free worker: %s' % (idx))
+                return idx
+            if worker.poll() is None:  # running, get output
+                for line in iter(worker.stdout.readline, ''):
+                    if line != '':
+                        sys.stdout.write(line)
+                    else:
+                        break
+            else:  # worker process completed.
+                logger.info('Process end: %s' % (idx))
+                workers[idx] = None
+                return idx
+        time.sleep(0.001)
+
+
+def wait_for_workers(workers):
+    while True:
+        for idx, worker in enumerate(workers):
+            if worker is None:
+                continue
+            # check worker is completed.
+            if worker.poll() is None:
+                for line in iter(worker.stdout.readline, ''):
+                    if line != '':
+                        sys.stdout.write(line)
+                    else:
+                        break
+            else:
+                logger.info('Process idx: %s end!' % (idx))
+                workers[idx] = None
+
+        is_all_completed = True
+        for idx, worker in enumerate(workers):
+            if worker is not None:
+                is_all_completed = False
+                break
+
+        if is_all_completed:
+            logger.info('All sub process is completed!')
+            break
+        time.sleep(0.001)
+
+
+def parallel_run_case_in_env(env_name, env, test_suite_env_map, isolated_cases,
+                             result_dir, parallel):
+    logger.info('Running case in env: %s' % env_name)
+    # install requirements and deps # run_config['envs'][env]
+    if 'requirements' in env:
+        install_requirements(env['requirements'])
+    if 'dependencies' in env:
+        install_packages(env['dependencies'])
+    # case worker processes
+    worker_processes = [None] * parallel
+    for test_suite_file in isolated_cases:  # run case in subprocess
+        if test_suite_file in test_suite_env_map and test_suite_env_map[
+                test_suite_file] == env_name:
+            cmd = [
+                'python',
+                'tests/run.py',
+                '--pattern',
+                test_suite_file,
+                '--result_dir',
+                result_dir,
+            ]
+            worker_idx = wait_for_free_worker(worker_processes)
+            worker_process = async_run_command_with_popen(cmd, worker_idx)
+            os.set_blocking(worker_process.stdout.fileno(), False)
+            worker_processes[worker_idx] = worker_process
+        else:
+            pass  # case not in run list.
+
+    # run remain cases in a process.
+    remain_suite_files = []
+    for k, v in test_suite_env_map.items():
+        if k not in isolated_cases and v == env_name:
+            remain_suite_files.append(k)
+    if len(remain_suite_files) == 0:
+        wait_for_workers(worker_processes)
+        return
+    # roughly split case in parallel
+    part_count = math.ceil(len(remain_suite_files) / parallel)
+    suites_chunks = [
+        remain_suite_files[x:x + part_count]
+        for x in range(0, len(remain_suite_files), part_count)
+    ]
+    for suites_chunk in suites_chunks:
+        worker_idx = wait_for_free_worker(worker_processes)
+        cmd = [
+            'python', 'tests/run.py', '--result_dir', result_dir, '--suites'
+        ]
+        for suite in suites_chunk:
+            cmd.append(suite)
+        worker_process = async_run_command_with_popen(cmd, worker_idx)
+        os.set_blocking(worker_process.stdout.fileno(), False)
+        worker_processes[worker_idx] = worker_process
+
+    wait_for_workers(worker_processes)
+
+
+def run_case_in_env(env_name, env, test_suite_env_map, isolated_cases,
+                    result_dir):
+    # install requirements and deps # run_config['envs'][env]
+    if 'requirements' in env:
+        install_requirements(env['requirements'])
+    if 'dependencies' in env:
+        install_packages(env['dependencies'])
+
+    for test_suite_file in isolated_cases:  # run case in subprocess
+        if test_suite_file in test_suite_env_map and test_suite_env_map[
+                test_suite_file] == env_name:
+            cmd = [
+                'python',
+                'tests/run.py',
+                '--pattern',
+                test_suite_file,
+                '--result_dir',
+                result_dir,
+            ]
+            run_command_with_popen(cmd)
+        else:
+            pass  # case not in run list.
+
+    # run remain cases in a process.
+    remain_suite_files = []
+    for k, v in test_suite_env_map.items():
+        if k not in isolated_cases and v == env_name:
+            remain_suite_files.append(k)
+    if len(remain_suite_files) == 0:
+        return
+    cmd = ['python', 'tests/run.py', '--result_dir', result_dir, '--suites']
+    for suite in remain_suite_files:
+        cmd.append(suite)
+    run_command_with_popen(cmd)
+
+
+def run_non_parallelizable_test_suites(suites, result_dir):
+    cmd = ['python', 'tests/run.py', '--result_dir', result_dir, '--suites']
+    for suite in suites:
+        cmd.append(suite)
+    run_command_with_popen(cmd)
+
+
+# Selected cases:
+def get_selected_cases():
+    cmd = ['python', '-u', 'tests/run_analysis.py']
+    selected_cases = []
+    with subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            bufsize=1,
+            encoding='utf8') as sub_process:
+        for line in iter(sub_process.stdout.readline, ''):
+            sys.stdout.write(line)
+            if line.startswith('Selected cases:'):
+                line = line.replace('Selected cases:', '').strip()
+                selected_cases = line.split(',')
+        sub_process.wait()
+        if sub_process.returncode != 0:
+            msg = 'Run analysis exception, returncode: %s!' % sub_process.returncode
+            logger.error(msg)
+            raise Exception(msg)
+    return selected_cases
+
+
+def run_in_subprocess(args):
+    # only case args.isolated_cases run in subprocess, all other run in a subprocess
+    if not args.no_diff:  # run based on git diff
+        try:
+            test_suite_files = get_selected_cases()
+            logger.info('Tests suite to run: ')
+            for f in test_suite_files:
+                logger.info(f)
+        except Exception:
+            logger.error(
+                'Get test suite based diff exception!, will run all cases.')
+            test_suite_files = gather_test_suites_files(
+                os.path.abspath(args.test_dir), args.pattern)
+        if len(test_suite_files) == 0:
+            logger.error('Get no test suite based on diff, run all the cases.')
+            test_suite_files = gather_test_suites_files(
+                os.path.abspath(args.test_dir), args.pattern)
+    else:
+        test_suite_files = gather_test_suites_files(
+            os.path.abspath(args.test_dir), args.pattern)
+
+    non_parallelizable_suites = []
+    test_suite_files = [
+        x for x in test_suite_files if x not in non_parallelizable_suites
+    ]
+
+    run_config = None
+    isolated_cases = []
+    test_suite_env_map = {}
+    # put all the case in default env.
+    for test_suite_file in test_suite_files:
+        test_suite_env_map[test_suite_file] = 'default'
+
+    if args.run_config is not None and Path(args.run_config).exists():
+        with open(args.run_config, encoding='utf-8') as f:
+            run_config = yaml.load(f, Loader=yaml.FullLoader)
+        if 'isolated' in run_config:
+            isolated_cases = run_config['isolated']
+
+        if 'envs' in run_config:
+            for env in run_config['envs']:
+                if env != 'default':
+                    for test_suite in run_config['envs'][env]['tests']:
+                        if test_suite in test_suite_env_map:
+                            test_suite_env_map[test_suite] = env
+
+    if args.subprocess:  # run all case in subprocess
+        isolated_cases = test_suite_files
+
+    with tempfile.TemporaryDirectory() as temp_result_dir:
+        # first run cases that nonparallelizable
+        run_non_parallelizable_test_suites(non_parallelizable_suites,
+                                           temp_result_dir)
+
+        # run case parallel in envs
+        for env in set(test_suite_env_map.values()):
+            parallel_run_case_in_env(env, run_config['envs'][env],
+                                     test_suite_env_map, isolated_cases,
+                                     temp_result_dir, args.parallel)
+
+        result_dfs = []
+        result_path = Path(temp_result_dir)
+        for result in result_path.iterdir():
+            if Path.is_file(result):
+                df = pandas.read_pickle(result)
+                result_dfs.append(df)
+        result_pd = pandas.concat(
+            result_dfs)  # merge result of every test suite.
+        print_table_result(result_pd)
+        print_abnormal_case_info(result_pd)
+        statistics_test_result(result_pd)
+
+
+def get_object_full_name(obj):
+    klass = obj.__class__
+    module = klass.__module__
+    if module == 'builtins':
+        return klass.__qualname__
+    return module + '.' + klass.__qualname__
+
+
+class TimeCostTextTestResult(TextTestResult):
+    """Record test case time used!"""
+
+    def __init__(self, stream, descriptions, verbosity):
+        self.successes = []
+        super(TimeCostTextTestResult,
+                     self).__init__(stream, descriptions, verbosity)
+
+    def startTest(self, test):
+        test.start_time = datetime.datetime.now()
+        test.test_full_name = get_object_full_name(
+            test) + '.' + test._testMethodName
+        self.stream.writeln('Test case:  %s start at: %s' %
+                            (test.test_full_name, test.start_time))
+
+        return super(TimeCostTextTestResult, self).startTest(test)
+
+    def stopTest(self, test):
+        TextTestResult.stopTest(self, test)
+        test.stop_time = datetime.datetime.now()
+        test.time_cost = (test.stop_time - test.start_time).total_seconds()
+        self.stream.writeln(
+            'Test case: %s stop at: %s, cost time: %s(seconds)' %
+            (test.test_full_name, test.stop_time, test.time_cost))
+        if torch.cuda.is_available(
+        ) and test.time_cost > 5.0:  # print nvidia-smi
+            cmd = ['nvidia-smi']
+            run_command_with_popen(cmd)
+        super(TimeCostTextTestResult, self).stopTest(test)
+
+    def addSuccess(self, test):
+        self.successes.append(test)
+        super(TextTestResult, self).addSuccess(test)
+
+
+class TimeCostTextTestRunner(unittest.runner.TextTestRunner):
+    resultclass = TimeCostTextTestResult
+
+    def run(self, test):
+        return super(TimeCostTextTestRunner, self).run(test)
+
+    def _makeResult(self):
+        result = super(TimeCostTextTestRunner, self)._makeResult()
+        return result
+
+
+def gather_test_cases(test_dir, pattern, list_tests):
+    case_list = []
+    for dirpath, dirnames, filenames in os.walk(test_dir):
+        for file in filenames:
+            if fnmatch(file, pattern):
+                case_list.append(file)
+
+    test_suite = unittest.TestSuite()
+
+    for case in case_list:
+        test_case = unittest.defaultTestLoader.discover(
+            start_dir=test_dir, pattern=case)
+        test_suite.addTest(test_case)
+        if hasattr(test_case, '__iter__'):
+            for subcase in test_case:
+                if list_tests:
+                    print(subcase)
+        else:
+            if list_tests:
+                print(test_case)
+    return test_suite
+
+
+def print_abnormal_case_info(df):
+    df = df.loc[(df['Result'] == 'Error') | (df['Result'] == 'Failures')]
+    for _, row in df.iterrows():
+        print('Case %s run result: %s, msg:\n%s' %
+              (row['Name'], row['Result'], row['Info']))
+
+
+def print_table_result(df):
+    df = df.loc[df['Result'] != 'Skipped']
+    df = df.drop('Info', axis=1)
+    formatters = {
+        'Name': '{{:<{}s}}'.format(df['Name'].str.len().max()).format,
+        'Result': '{{:<{}s}}'.format(df['Result'].str.len().max()).format,
+    }
+    with pandas.option_context('display.max_rows', None, 'display.max_columns',
+                               None, 'display.width', None):
+        print(df.to_string(justify='left', formatters=formatters, index=False))
+
+
+def main(args):
+    runner = TimeCostTextTestRunner()
+    if args.suites is not None and len(args.suites) > 0:
+        logger.info('Running: %s' % ' '.join(args.suites))
+        test_suite = gather_test_suites_in_files(args.test_dir, args.suites,
+                                                 args.list_tests)
+    else:
+        test_suite = gather_test_cases(
+            os.path.abspath(args.test_dir), args.pattern, args.list_tests)
+    if not args.list_tests:
+        result = runner.run(test_suite)
+        logger.info('Running case completed, pid: %s, suites: %s' %
+                    (os.getpid(), args.suites))
+        result = collect_test_results(result)
+        df = test_cases_result_to_df(result)
+        if args.result_dir is not None:
+            save_test_result(df, args)
+        else:
+            print_table_result(df)
+            print_abnormal_case_info(df)
+            statistics_test_result(df)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser('test runner')
+    parser.add_argument(
+        '--list_tests', action='store_true', help='list all tests')
+    parser.add_argument(
+        '--pattern', default='test_*.py', help='test file pattern')
+    parser.add_argument(
+        '--test_dir', default='tests', help='directory to be tested')
+    parser.add_argument(
+        '--level', default=0, type=int, help='2 -- all, 1 -- p1, 0 -- p0')
+    parser.add_argument(
+        '--profile', action='store_true', help='enable profiling')
+    parser.add_argument(
+        '--run_config',
+        default=None,
+        help='specified case run config file(yaml file)')
+    parser.add_argument(
+        '--subprocess',
+        action='store_true',
+        help='run all test suite in subprocess')
+    parser.add_argument(
+        '--result_dir',
+        default=None,
+        help='Save result to directory, internal use only')
+    parser.add_argument(
+        '--parallel',
+        default=1,
+        type=int,
+        help='Set case parallels, default single process, set with gpu number.'
+    )
+    parser.add_argument(
+        '--no-diff',
+        action='store_true',
+        help=
+        'Default running case based on git diff(with master), disable with --no-diff)'
+    )
+    parser.add_argument(
+        '--suites',
+        nargs='*',
+        help='Run specified test suites(test suite files list split by space)')
+    args = parser.parse_args()
+    print(args)
+    if args.run_config is not None or args.subprocess:
+        run_in_subprocess(args)
+    else:
+        main(args)
diff --git a/ms-swift/tests/run_config.yaml b/ms-swift/tests/run_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3175f11e8f089ed12615af240112dda4e09ad4f5
--- /dev/null
+++ b/ms-swift/tests/run_config.yaml
@@ -0,0 +1,8 @@
+# isolate cases in env, we can install different dependencies in each env.
+isolated:  # test cases that may require excessive amount of GPU memory or run long time, which will be executed in dedicated process.
+
+envs:
+  default: # default env, case not in other env will in default, pytorch.
+    dependencies: # requirement packages，pip install before test case run.
+      - numpy>=1.20,<=1.22.0
+      - protobuf<4,>=3.20.2
diff --git a/ms-swift/tests/sample/test_client.py b/ms-swift/tests/sample/test_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..0481a155321fccf510e718ef2bd4344407b8d746
--- /dev/null
+++ b/ms-swift/tests/sample/test_client.py
@@ -0,0 +1,35 @@
+import os
+
+
+def test_client():
+    from swift.llm import sampling_main, SamplingArguments
+    import json
+    base_url = 'https://dashscope.aliyuncs.com/compatible-mode/v1'
+    api_key = os.environ.get('OPENAI_API_KEY')
+    engine_kwargs = json.dumps({
+        'base_url': base_url,
+        'api_key': api_key,
+    })
+    dataset = 'tastelikefeet/competition_math#5'
+    system = """A conversation between User and Assistant. The user asks a question, and the Assistant solves it.
+        The assistant first thinks about the reasoning process in the mind and then provides the user
+        with the answer. The reasoning process and answer are enclosed
+        within <think> </think> and <answer> </answer> tags, respectively,
+        i.e., <think> reasoning process here </think> <answer> answer here </answer>."""
+    args = SamplingArguments(
+        sampler_type='distill',
+        sampler_engine='client',
+        model='deepseek-r1',
+        dataset=dataset,
+        num_return_sequences=1,
+        stream=True,
+        system=system,
+        temperature=0.6,
+        top_p=0.95,
+        engine_kwargs=engine_kwargs,
+    )
+    sampling_main(args)
+
+
+if __name__ == '__main__':
+    test_client()
diff --git a/ms-swift/tests/test_align/test_cls.py b/ms-swift/tests/test_align/test_cls.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ed752d68a74d71aed749d981895058540590b89
--- /dev/null
+++ b/ms-swift/tests/test_align/test_cls.py
@@ -0,0 +1,60 @@
+import os
+from pprint import pprint
+
+import torch
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+kwargs = {
+    'per_device_train_batch_size': 4,
+    'per_device_eval_batch_size': 4,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+    'save_steps': 100,
+    'max_length': 512,
+    'task_type': 'seq_cls',
+    'num_labels': 2,
+}
+
+
+def calc_acc(infer_result):
+    n_correct = 0
+    for res in infer_result:
+        if res['response'] == res['labels']:
+            n_correct += 1
+    return f'acc: {n_correct/len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
+
+
+def test_llm():
+    from swift.llm import sft_main, TrainArguments, infer_main, InferArguments, Template
+    res = []
+    for model in ['Qwen/Qwen2.5-0.5B-Instruct', 'Qwen/Qwen2.5-0.5B', 'AI-ModelScope/bert-base-chinese']:
+        dataset = ['DAMO_NLP/jd:cls#2000']
+        result = sft_main(TrainArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, **kwargs))
+        last_model_checkpoint = result['last_model_checkpoint']
+        infer_result = infer_main(
+            InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, truncation_strategy='right'))
+        res.append(calc_acc(infer_result))
+        infer_result2 = infer_main(
+            InferArguments(
+                ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16, truncation_strategy='right'))
+        res.append(calc_acc(infer_result2))
+
+    model = 'Qwen/Qwen2.5-0.5B-Instruct'
+    dataset = ['DAMO_NLP/jd#2000']
+    train_kwargs = kwargs.copy()
+    train_kwargs.pop('task_type')
+    train_kwargs.pop('num_labels')
+    result = sft_main(TrainArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, **train_kwargs))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_result = infer_main(
+        InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, truncation_strategy='right'))
+    res.append(calc_acc(infer_result))
+    infer_result2 = infer_main(
+        InferArguments(
+            ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16, truncation_strategy='right'))
+    res.append(calc_acc(infer_result2))
+    pprint(res)
+
+
+if __name__ == '__main__':
+    test_llm()
diff --git a/ms-swift/tests/test_align/test_lmdeploy_vlm.py b/ms-swift/tests/test_align/test_lmdeploy_vlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..280a1acaad054d82f0b9f794416eb53e2a1e89a0
--- /dev/null
+++ b/ms-swift/tests/test_align/test_lmdeploy_vlm.py
@@ -0,0 +1,80 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def _infer_image(model, system=None, images=None):
+    engine = LmdeployEngine(model)
+    if images is None:
+        images = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png']
+    messages = []
+    if system is not None:
+        messages += [{'role': 'system', 'content': system}]
+    messages.append({'role': 'user', 'content': 'describe the image.'})
+    resp_list = engine.infer([InferRequest(messages=messages, images=images)],
+                             RequestConfig(temperature=0, max_tokens=64, repetition_penalty=1.))
+    return resp_list[0].choices[0].message.content
+
+
+def _infer_image_pipeline(model, images=None, prefix='<IMAGE_TOKEN>\n'):
+    from lmdeploy import pipeline, GenerationConfig
+    from lmdeploy.vl import load_image
+    from swift.llm import safe_snapshot_download
+    gen_config = GenerationConfig(temperature=0., repetition_penalty=1., max_new_tokens=64)
+    pipe = pipeline(safe_snapshot_download(model))
+
+    image = load_image('http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png')
+    response = pipe((f'{prefix}describe the image.', image), gen_config=gen_config)
+    return response.text
+
+
+def test_internvl2_5():
+    model = 'OpenGVLab/InternVL2_5-4B'
+    response = _infer_image(model)
+    response2 = _infer_image_pipeline(model)
+    assert response == response2
+
+
+def test_internvl2():
+    model = 'OpenGVLab/InternVL2-2B'
+    response = _infer_image(model)
+    response2 = _infer_image_pipeline(model)  # Missing '\n' after '<|im_end|>'
+    assert response == response2
+
+
+def test_deepseek_vl():
+    model = 'deepseek-ai/deepseek-vl-1.3b-chat'
+    response = _infer_image(model)
+    response2 = _infer_image_pipeline(model, prefix='<IMAGE_TOKEN>')
+    assert response == response2
+
+
+def test_qwen_vl():
+    model = 'Qwen/Qwen-VL-Chat'
+    response = _infer_image_pipeline(model)  # Missing: 'Picture 1: '
+    response2 = _infer_image(model)
+    assert response == response2
+
+
+def test_qwen2_vl():
+    model = 'Qwen/Qwen2-VL-2B-Instruct'
+    response = _infer_image_pipeline(model, prefix='<IMAGE_TOKEN>')
+    response2 = _infer_image(model)
+    assert response == response2
+
+
+def test_qwen2_5_vl():
+    model = 'Qwen/Qwen2.5-VL-3B-Instruct'
+    response = _infer_image(model)
+    response2 = _infer_image_pipeline(model, prefix='<IMAGE_TOKEN>')
+    assert response == response2
+
+
+if __name__ == '__main__':
+    from swift.llm import LmdeployEngine, InferRequest, RequestConfig
+    # test_internvl2()
+    # test_internvl2_5()
+    # test_deepseek_vl()
+    # test_qwen_vl()
+    # test_qwen2_vl()
+    test_qwen2_5_vl()
diff --git a/ms-swift/tests/test_align/test_padding_side.py b/ms-swift/tests/test_align/test_padding_side.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd298b583a159f4a7c6fa0766bb1bc19378f8f15
--- /dev/null
+++ b/ms-swift/tests/test_align/test_padding_side.py
@@ -0,0 +1,72 @@
+import os
+from pprint import pprint
+
+import torch
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+kwargs = {
+    'per_device_train_batch_size': 4,
+    'per_device_eval_batch_size': 4,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+    'save_steps': 100,
+    'max_length': 8192,
+}
+
+
+def calc_acc(infer_result):
+    n_correct = 0
+    for res in infer_result:
+        if res['response'] == res['labels']:
+            n_correct += 1
+    return f'acc: {n_correct/len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
+
+
+def calc_diff(infer_result, infer_result2):
+    n_correct = 0
+    for x1, x2 in zip(infer_result, infer_result2):
+        if x1['response'] == x2['response']:
+            n_correct += 1
+    return f'acc: {n_correct/len(infer_result)}, n_correct: {n_correct}, len(res): {len(infer_result)}'
+
+
+def test_llm():
+    from swift.llm import sft_main, TrainArguments, infer_main, InferArguments, Template
+    res = []
+    for padding_side in ['left', 'right']:
+        model = 'Qwen/Qwen2.5-0.5B-Instruct'
+        dataset = ['damo/zh_cls_fudan-news#2000']
+        result = sft_main(
+            TrainArguments(model=model, dataset=dataset, split_dataset_ratio=0.1, padding_side=padding_side, **kwargs))
+        last_model_checkpoint = result['last_model_checkpoint']
+        infer_result = infer_main(InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True))
+        res.append(calc_acc(infer_result))
+        infer_result2 = infer_main(
+            InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16))
+        res.append(calc_acc(infer_result2))
+    pprint(res)
+
+
+def test_mllm():
+    from swift.llm import sft_main, TrainArguments, infer_main, InferArguments, Template
+    res = []
+    for padding_side in ['left', 'right']:
+        model = 'Qwen/Qwen2-VL-2B-Instruct'
+        dataset = ['AI-ModelScope/LaTeX_OCR#2000']
+        result = sft_main(TrainArguments(model=model, dataset=dataset, padding_side=padding_side, **kwargs))
+        last_model_checkpoint = result['last_model_checkpoint']
+        infer_result = infer_main(InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True))
+        res.append(infer_result)
+        infer_result2 = infer_main(
+            InferArguments(ckpt_dir=last_model_checkpoint, load_data_args=True, max_batch_size=16))
+        res.append(infer_result2)
+    print(calc_diff(res[0], res[1]))
+    print(calc_diff(res[2], res[3]))
+    print(calc_diff(res[0], res[2]))
+    print(calc_diff(res[0], res[3]))
+    print(calc_diff(res[2], res[1]))
+
+
+if __name__ == '__main__':
+    test_llm()
+    test_mllm()
diff --git a/ms-swift/tests/test_align/test_template/test_gene.py b/ms-swift/tests/test_align/test_template/test_gene.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e985889b26e0dfe32a7bb10bfb38ccba920bb0a
--- /dev/null
+++ b/ms-swift/tests/test_align/test_template/test_gene.py
@@ -0,0 +1,29 @@
+import os
+
+import torch
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+os.environ['SWIFT_DEBUG'] = '1'
+
+
+def test_deepseek_janus_pro_gene():
+    from swift.llm import infer_main, InferArguments
+    args = InferArguments(model='deepseek-ai/Janus-Pro-1B', infer_backend='pt')
+    infer_main(args)
+
+
+def test_emu3_gen(infer_backend):
+    from swift.llm import infer_main, InferArguments
+    args = InferArguments(
+        model='BAAI/Emu3-Gen',
+        infer_backend=infer_backend,
+        stream=False,
+        use_chat_template=False,
+        top_k=2048,
+        max_new_tokens=40960)
+    infer_main(args)
+
+
+if __name__ == '__main__':
+    # test_emu3_gen('pt')
+    test_deepseek_janus_pro_gene()
diff --git a/ms-swift/tests/train/test_kto.py b/ms-swift/tests/train/test_kto.py
new file mode 100644
index 0000000000000000000000000000000000000000..076da99c2ecbd759fbbac022f9cd3923f8084e0a
--- /dev/null
+++ b/ms-swift/tests/train/test_kto.py
@@ -0,0 +1,39 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+kwargs = {
+    'per_device_train_batch_size': 2,
+    'save_steps': 5,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+}
+
+
+def test_llm():
+    from swift.llm import rlhf_main, RLHFArguments, infer_main, InferArguments
+    result = rlhf_main(
+        RLHFArguments(
+            rlhf_type='kto',
+            model='Qwen/Qwen2-7B-Instruct',
+            dataset=['AI-ModelScope/ultrafeedback-binarized-preferences-cleaned-kto#100'],
+            **kwargs))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+def test_mllm():
+    from swift.llm import rlhf_main, RLHFArguments, infer_main, InferArguments
+    result = rlhf_main(
+        RLHFArguments(
+            rlhf_type='kto',
+            model='Qwen/Qwen2-VL-7B-Instruct',
+            dataset=['AI-ModelScope/ultrafeedback-binarized-preferences-cleaned-kto#100'],
+            **kwargs))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+if __name__ == '__main__':
+    # test_llm()
+    test_mllm()
diff --git a/ms-swift/tests/train/test_pt.py b/ms-swift/tests/train/test_pt.py
new file mode 100644
index 0000000000000000000000000000000000000000..e858e24acfb5f8c462351b0566da470604f470ab
--- /dev/null
+++ b/ms-swift/tests/train/test_pt.py
@@ -0,0 +1,33 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+kwargs = {
+    'per_device_train_batch_size': 2,
+    'save_steps': 5,
+    'gradient_accumulation_steps': 4,
+    'num_train_epochs': 1,
+}
+
+
+def test_llm():
+    from swift.llm import pt_main, TrainArguments, infer_main, InferArguments
+    result = pt_main(TrainArguments(model='Qwen/Qwen2-7B-Instruct', dataset=['swift/sharegpt:all#100'], **kwargs))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+def test_mllm():
+    from swift.llm import pt_main, TrainArguments, infer_main, InferArguments
+    result = pt_main(
+        TrainArguments(
+            model='Qwen/Qwen2-VL-7B-Instruct',
+            dataset=['modelscope/coco_2014_caption:validation#20', 'AI-ModelScope/alpaca-gpt4-data-en#20'],
+            **kwargs))
+    last_model_checkpoint = result['last_model_checkpoint']
+    infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
+
+
+if __name__ == '__main__':
+    # test_llm()
+    test_mllm()
diff --git a/ms-swift/tests/train/test_train_eval.py b/ms-swift/tests/train/test_train_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..294c2ad8c4411eaa382c6ab986cd90eda8c4d04b
--- /dev/null
+++ b/ms-swift/tests/train/test_train_eval.py
@@ -0,0 +1,34 @@
+import os
+
+kwargs = {
+    'per_device_train_batch_size': 5,
+    'save_steps': 5,
+    'gradient_accumulation_steps': 1,
+    'num_train_epochs': 1,
+}
+
+
+def test_train_eval_loop():
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0,2'
+    from swift.llm import sft_main, TrainArguments
+    sft_main(
+        TrainArguments(
+            model='Qwen/Qwen2.5-0.5B-Instruct',
+            dataset=['AI-ModelScope/alpaca-gpt4-data-zh#100'],
+            target_modules=['all-linear', 'all-embedding'],
+            modules_to_save=['all-embedding', 'all-norm'],
+            eval_strategy='steps',
+            eval_steps=5,
+            per_device_eval_batch_size=5,
+            eval_use_evalscope=True,
+            eval_datasets=['gsm8k'],
+            eval_datasets_args={'gsm8k': {
+                'few_shot_num': 0
+            }},
+            eval_limit=10,
+            report_to=['wandb'],
+            **kwargs))
+
+
+if __name__ == '__main__':
+    test_train_eval_loop()