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mxbai-embed-xsmall-v1-Q8_0-GGUF

Feature Extraction
sentence-transformers
GGUF
English
mteb
llama-cpp
gguf-my-repo
Eval Results
Inference Endpoints
Model card Files Community
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1 Parent(s): e8bd5ab

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1
+ ---
2
+ tags:
3
+ - mteb
4
+ - llama-cpp
5
+ - gguf-my-repo
6
+ base_model: mixedbread-ai/mxbai-embed-xsmall-v1
7
+ library_name: sentence-transformers
8
+ license: apache-2.0
9
+ language:
10
+ - en
11
+ pipeline_tag: feature-extraction
12
+ model-index:
13
+ - name: mxbai-embed-xsmall-v1
14
+ results:
15
+ - task:
16
+ type: Retrieval
17
+ dataset:
18
+ name: MTEB ArguAna
19
+ type: arguana
20
+ config: default
21
+ split: test
22
+ revision: None
23
+ metrics:
24
+ - type: ndcg_at_1
25
+ value: 25.18
26
+ - type: ndcg_at_3
27
+ value: 39.22
28
+ - type: ndcg_at_5
29
+ value: 43.93
30
+ - type: ndcg_at_10
31
+ value: 49.58
32
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33
+ value: 53.41
34
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35
+ value: 54.11
36
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37
+ value: 25.18
38
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39
+ value: 35.66
40
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41
+ value: 38.25
42
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+ value: 40.58
44
+ - type: map_at_30
45
+ value: 41.6
46
+ - type: map_at_100
47
+ value: 41.69
48
+ - type: recall_at_1
49
+ value: 25.18
50
+ - type: recall_at_3
51
+ value: 49.57
52
+ - type: recall_at_5
53
+ value: 61.09
54
+ - type: recall_at_10
55
+ value: 78.59
56
+ - type: recall_at_30
57
+ value: 94.03
58
+ - type: recall_at_100
59
+ value: 97.94
60
+ - type: precision_at_1
61
+ value: 25.18
62
+ - type: precision_at_3
63
+ value: 16.52
64
+ - type: precision_at_5
65
+ value: 12.22
66
+ - type: precision_at_10
67
+ value: 7.86
68
+ - type: precision_at_30
69
+ value: 3.13
70
+ - type: precision_at_100
71
+ value: 0.98
72
+ - type: accuracy_at_3
73
+ value: 49.57
74
+ - type: accuracy_at_5
75
+ value: 61.09
76
+ - type: accuracy_at_10
77
+ value: 78.59
78
+ - task:
79
+ type: Retrieval
80
+ dataset:
81
+ name: MTEB CQADupstackAndroidRetrieval
82
+ type: BeIR/cqadupstack
83
+ config: default
84
+ split: test
85
+ revision: None
86
+ metrics:
87
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88
+ value: 44.35
89
+ - type: ndcg_at_3
90
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91
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+ value: 49.75
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+ - type: recall_at_5
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127
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141
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151
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153
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155
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157
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158
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159
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167
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169
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171
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191
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197
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205
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207
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215
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217
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219
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220
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221
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223
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225
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226
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227
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228
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229
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+ value: 89.0
231
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233
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234
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235
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236
+ value: 16.01
237
+ - type: precision_at_10
238
+ value: 9.25
239
+ - type: precision_at_30
240
+ value: 3.67
241
+ - type: precision_at_100
242
+ value: 1.23
243
+ - type: accuracy_at_3
244
+ value: 62.76
245
+ - type: accuracy_at_5
246
+ value: 68.84
247
+ - type: accuracy_at_10
248
+ value: 75.8
249
+ - type: ndcg_at_1
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251
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+ value: 34.23
253
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267
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271
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275
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277
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279
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281
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283
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285
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287
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291
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293
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+ value: 2.65
295
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+ value: 0.97
297
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299
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301
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303
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305
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307
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309
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313
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317
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319
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323
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327
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329
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331
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333
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335
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740
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819
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831
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876
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993
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1056
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1059
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1060
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+ value: 36.84
1144
+ - type: map_at_30
1145
+ value: 37.92
1146
+ - type: map_at_100
1147
+ value: 38.16
1148
+ - type: recall_at_1
1149
+ value: 23.03
1150
+ - type: recall_at_3
1151
+ value: 43.49
1152
+ - type: recall_at_5
1153
+ value: 53.41
1154
+ - type: recall_at_10
1155
+ value: 65.65
1156
+ - type: recall_at_30
1157
+ value: 80.79
1158
+ - type: recall_at_100
1159
+ value: 90.59
1160
+ - type: precision_at_1
1161
+ value: 25.9
1162
+ - type: precision_at_3
1163
+ value: 16.76
1164
+ - type: precision_at_5
1165
+ value: 12.54
1166
+ - type: precision_at_10
1167
+ value: 7.78
1168
+ - type: precision_at_30
1169
+ value: 3.23
1170
+ - type: precision_at_100
1171
+ value: 1.1
1172
+ - type: accuracy_at_3
1173
+ value: 47.31
1174
+ - type: accuracy_at_5
1175
+ value: 57.16
1176
+ - type: accuracy_at_10
1177
+ value: 69.09
1178
+ - task:
1179
+ type: Retrieval
1180
+ dataset:
1181
+ name: MTEB NFCorpus
1182
+ type: nfcorpus
1183
+ config: default
1184
+ split: test
1185
+ revision: None
1186
+ metrics:
1187
+ - type: ndcg_at_1
1188
+ value: 40.87
1189
+ - type: ndcg_at_3
1190
+ value: 36.79
1191
+ - type: ndcg_at_5
1192
+ value: 34.47
1193
+ - type: ndcg_at_10
1194
+ value: 32.05
1195
+ - type: ndcg_at_30
1196
+ value: 29.23
1197
+ - type: ndcg_at_100
1198
+ value: 29.84
1199
+ - type: map_at_1
1200
+ value: 5.05
1201
+ - type: map_at_3
1202
+ value: 8.5
1203
+ - type: map_at_5
1204
+ value: 9.87
1205
+ - type: map_at_10
1206
+ value: 11.71
1207
+ - type: map_at_30
1208
+ value: 13.48
1209
+ - type: map_at_100
1210
+ value: 14.86
1211
+ - type: recall_at_1
1212
+ value: 5.05
1213
+ - type: recall_at_3
1214
+ value: 9.55
1215
+ - type: recall_at_5
1216
+ value: 11.91
1217
+ - type: recall_at_10
1218
+ value: 16.07
1219
+ - type: recall_at_30
1220
+ value: 22.13
1221
+ - type: recall_at_100
1222
+ value: 30.7
1223
+ - type: precision_at_1
1224
+ value: 42.72
1225
+ - type: precision_at_3
1226
+ value: 34.78
1227
+ - type: precision_at_5
1228
+ value: 30.03
1229
+ - type: precision_at_10
1230
+ value: 23.93
1231
+ - type: precision_at_30
1232
+ value: 14.61
1233
+ - type: precision_at_100
1234
+ value: 7.85
1235
+ - type: accuracy_at_3
1236
+ value: 58.2
1237
+ - type: accuracy_at_5
1238
+ value: 64.09
1239
+ - type: accuracy_at_10
1240
+ value: 69.35
1241
+ - task:
1242
+ type: Retrieval
1243
+ dataset:
1244
+ name: MTEB QuoraRetrieval
1245
+ type: quora
1246
+ config: default
1247
+ split: test
1248
+ revision: None
1249
+ metrics:
1250
+ - type: ndcg_at_1
1251
+ value: 80.62
1252
+ - type: ndcg_at_3
1253
+ value: 84.62
1254
+ - type: ndcg_at_5
1255
+ value: 86.25
1256
+ - type: ndcg_at_10
1257
+ value: 87.7
1258
+ - type: ndcg_at_30
1259
+ value: 88.63
1260
+ - type: ndcg_at_100
1261
+ value: 88.95
1262
+ - type: map_at_1
1263
+ value: 69.91
1264
+ - type: map_at_3
1265
+ value: 80.7
1266
+ - type: map_at_5
1267
+ value: 82.57
1268
+ - type: map_at_10
1269
+ value: 83.78
1270
+ - type: map_at_30
1271
+ value: 84.33
1272
+ - type: map_at_100
1273
+ value: 84.44
1274
+ - type: recall_at_1
1275
+ value: 69.91
1276
+ - type: recall_at_3
1277
+ value: 86.36
1278
+ - type: recall_at_5
1279
+ value: 90.99
1280
+ - type: recall_at_10
1281
+ value: 95.19
1282
+ - type: recall_at_30
1283
+ value: 98.25
1284
+ - type: recall_at_100
1285
+ value: 99.47
1286
+ - type: precision_at_1
1287
+ value: 80.62
1288
+ - type: precision_at_3
1289
+ value: 37.03
1290
+ - type: precision_at_5
1291
+ value: 24.36
1292
+ - type: precision_at_10
1293
+ value: 13.4
1294
+ - type: precision_at_30
1295
+ value: 4.87
1296
+ - type: precision_at_100
1297
+ value: 1.53
1298
+ - type: accuracy_at_3
1299
+ value: 92.25
1300
+ - type: accuracy_at_5
1301
+ value: 95.29
1302
+ - type: accuracy_at_10
1303
+ value: 97.74
1304
+ - task:
1305
+ type: Retrieval
1306
+ dataset:
1307
+ name: MTEB SCIDOCS
1308
+ type: scidocs
1309
+ config: default
1310
+ split: test
1311
+ revision: None
1312
+ metrics:
1313
+ - type: ndcg_at_1
1314
+ value: 24.1
1315
+ - type: ndcg_at_3
1316
+ value: 20.18
1317
+ - type: ndcg_at_5
1318
+ value: 17.72
1319
+ - type: ndcg_at_10
1320
+ value: 21.5
1321
+ - type: ndcg_at_30
1322
+ value: 26.66
1323
+ - type: ndcg_at_100
1324
+ value: 30.95
1325
+ - type: map_at_1
1326
+ value: 4.88
1327
+ - type: map_at_3
1328
+ value: 9.09
1329
+ - type: map_at_5
1330
+ value: 10.99
1331
+ - type: map_at_10
1332
+ value: 12.93
1333
+ - type: map_at_30
1334
+ value: 14.71
1335
+ - type: map_at_100
1336
+ value: 15.49
1337
+ - type: recall_at_1
1338
+ value: 4.88
1339
+ - type: recall_at_3
1340
+ value: 11.55
1341
+ - type: recall_at_5
1342
+ value: 15.91
1343
+ - type: recall_at_10
1344
+ value: 22.82
1345
+ - type: recall_at_30
1346
+ value: 35.7
1347
+ - type: recall_at_100
1348
+ value: 50.41
1349
+ - type: precision_at_1
1350
+ value: 24.1
1351
+ - type: precision_at_3
1352
+ value: 19.0
1353
+ - type: precision_at_5
1354
+ value: 15.72
1355
+ - type: precision_at_10
1356
+ value: 11.27
1357
+ - type: precision_at_30
1358
+ value: 5.87
1359
+ - type: precision_at_100
1360
+ value: 2.49
1361
+ - type: accuracy_at_3
1362
+ value: 43.0
1363
+ - type: accuracy_at_5
1364
+ value: 51.6
1365
+ - type: accuracy_at_10
1366
+ value: 62.7
1367
+ - task:
1368
+ type: Retrieval
1369
+ dataset:
1370
+ name: MTEB SciFact
1371
+ type: scifact
1372
+ config: default
1373
+ split: test
1374
+ revision: None
1375
+ metrics:
1376
+ - type: ndcg_at_1
1377
+ value: 52.33
1378
+ - type: ndcg_at_3
1379
+ value: 61.47
1380
+ - type: ndcg_at_5
1381
+ value: 63.82
1382
+ - type: ndcg_at_10
1383
+ value: 65.81
1384
+ - type: ndcg_at_30
1385
+ value: 67.75
1386
+ - type: ndcg_at_100
1387
+ value: 68.96
1388
+ - type: map_at_1
1389
+ value: 50.46
1390
+ - type: map_at_3
1391
+ value: 58.51
1392
+ - type: map_at_5
1393
+ value: 60.12
1394
+ - type: map_at_10
1395
+ value: 61.07
1396
+ - type: map_at_30
1397
+ value: 61.64
1398
+ - type: map_at_100
1399
+ value: 61.8
1400
+ - type: recall_at_1
1401
+ value: 50.46
1402
+ - type: recall_at_3
1403
+ value: 67.81
1404
+ - type: recall_at_5
1405
+ value: 73.6
1406
+ - type: recall_at_10
1407
+ value: 79.31
1408
+ - type: recall_at_30
1409
+ value: 86.8
1410
+ - type: recall_at_100
1411
+ value: 93.5
1412
+ - type: precision_at_1
1413
+ value: 52.33
1414
+ - type: precision_at_3
1415
+ value: 24.56
1416
+ - type: precision_at_5
1417
+ value: 16.27
1418
+ - type: precision_at_10
1419
+ value: 8.9
1420
+ - type: precision_at_30
1421
+ value: 3.28
1422
+ - type: precision_at_100
1423
+ value: 1.06
1424
+ - type: accuracy_at_3
1425
+ value: 69.67
1426
+ - type: accuracy_at_5
1427
+ value: 75.0
1428
+ - type: accuracy_at_10
1429
+ value: 80.67
1430
+ - task:
1431
+ type: Retrieval
1432
+ dataset:
1433
+ name: MTEB TRECCOVID
1434
+ type: trec-covid
1435
+ config: default
1436
+ split: test
1437
+ revision: None
1438
+ metrics:
1439
+ - type: ndcg_at_1
1440
+ value: 57.0
1441
+ - type: ndcg_at_3
1442
+ value: 53.78
1443
+ - type: ndcg_at_5
1444
+ value: 52.62
1445
+ - type: ndcg_at_10
1446
+ value: 48.9
1447
+ - type: ndcg_at_30
1448
+ value: 44.2
1449
+ - type: ndcg_at_100
1450
+ value: 36.53
1451
+ - type: map_at_1
1452
+ value: 0.16
1453
+ - type: map_at_3
1454
+ value: 0.41
1455
+ - type: map_at_5
1456
+ value: 0.62
1457
+ - type: map_at_10
1458
+ value: 1.07
1459
+ - type: map_at_30
1460
+ value: 2.46
1461
+ - type: map_at_100
1462
+ value: 5.52
1463
+ - type: recall_at_1
1464
+ value: 0.16
1465
+ - type: recall_at_3
1466
+ value: 0.45
1467
+ - type: recall_at_5
1468
+ value: 0.72
1469
+ - type: recall_at_10
1470
+ value: 1.33
1471
+ - type: recall_at_30
1472
+ value: 3.46
1473
+ - type: recall_at_100
1474
+ value: 8.73
1475
+ - type: precision_at_1
1476
+ value: 62.0
1477
+ - type: precision_at_3
1478
+ value: 57.33
1479
+ - type: precision_at_5
1480
+ value: 56.0
1481
+ - type: precision_at_10
1482
+ value: 52.0
1483
+ - type: precision_at_30
1484
+ value: 46.2
1485
+ - type: precision_at_100
1486
+ value: 37.22
1487
+ - type: accuracy_at_3
1488
+ value: 82.0
1489
+ - type: accuracy_at_5
1490
+ value: 90.0
1491
+ - type: accuracy_at_10
1492
+ value: 92.0
1493
+ - task:
1494
+ type: Retrieval
1495
+ dataset:
1496
+ name: MTEB Touche2020
1497
+ type: webis-touche2020
1498
+ config: default
1499
+ split: test
1500
+ revision: None
1501
+ metrics:
1502
+ - type: ndcg_at_1
1503
+ value: 20.41
1504
+ - type: ndcg_at_3
1505
+ value: 17.62
1506
+ - type: ndcg_at_5
1507
+ value: 17.16
1508
+ - type: ndcg_at_10
1509
+ value: 17.09
1510
+ - type: ndcg_at_30
1511
+ value: 20.1
1512
+ - type: ndcg_at_100
1513
+ value: 26.33
1514
+ - type: map_at_1
1515
+ value: 2.15
1516
+ - type: map_at_3
1517
+ value: 3.59
1518
+ - type: map_at_5
1519
+ value: 5.07
1520
+ - type: map_at_10
1521
+ value: 6.95
1522
+ - type: map_at_30
1523
+ value: 9.01
1524
+ - type: map_at_100
1525
+ value: 10.54
1526
+ - type: recall_at_1
1527
+ value: 2.15
1528
+ - type: recall_at_3
1529
+ value: 4.5
1530
+ - type: recall_at_5
1531
+ value: 7.54
1532
+ - type: recall_at_10
1533
+ value: 12.46
1534
+ - type: recall_at_30
1535
+ value: 21.9
1536
+ - type: recall_at_100
1537
+ value: 36.58
1538
+ - type: precision_at_1
1539
+ value: 22.45
1540
+ - type: precision_at_3
1541
+ value: 19.05
1542
+ - type: precision_at_5
1543
+ value: 17.55
1544
+ - type: precision_at_10
1545
+ value: 15.51
1546
+ - type: precision_at_30
1547
+ value: 10.07
1548
+ - type: precision_at_100
1549
+ value: 5.57
1550
+ - type: accuracy_at_3
1551
+ value: 42.86
1552
+ - type: accuracy_at_5
1553
+ value: 53.06
1554
+ - type: accuracy_at_10
1555
+ value: 69.39
1556
+ ---
1557
+
1558
+ # twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF
1559
+ This model was converted to GGUF format from [`mixedbread-ai/mxbai-embed-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
1560
+ Refer to the [original model card](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) for more details on the model.
1561
+
1562
+ ## Use with llama.cpp
1563
+ Install llama.cpp through brew (works on Mac and Linux)
1564
+
1565
+ ```bash
1566
+ brew install llama.cpp
1567
+
1568
+ ```
1569
+ Invoke the llama.cpp server or the CLI.
1570
+
1571
+ ### CLI:
1572
+ ```bash
1573
+ llama-cli --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -p "The meaning to life and the universe is"
1574
+ ```
1575
+
1576
+ ### Server:
1577
+ ```bash
1578
+ llama-server --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -c 2048
1579
+ ```
1580
+
1581
+ Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
1582
+
1583
+ Step 1: Clone llama.cpp from GitHub.
1584
+ ```
1585
+ git clone https://github.com/ggerganov/llama.cpp
1586
+ ```
1587
+
1588
+ Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
1589
+ ```
1590
+ cd llama.cpp && LLAMA_CURL=1 make
1591
+ ```
1592
+
1593
+ Step 3: Run inference through the main binary.
1594
+ ```
1595
+ ./llama-cli --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -p "The meaning to life and the universe is"
1596
+ ```
1597
+ or
1598
+ ```
1599
+ ./llama-server --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -c 2048
1600
+ ```