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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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17
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19
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20
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21
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22
+ name: MTEB AmazonCounterfactualClassification (en)
23
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24
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25
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26
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28
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35
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36
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37
+ name: MTEB AmazonPolarityClassification
38
+ type: mteb/amazon_polarity
39
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40
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41
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42
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53
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2197
+ - type: mrr
2198
+ value: 51.19071492233257
2199
+ - task:
2200
+ type: Summarization
2201
+ dataset:
2202
+ name: MTEB SummEval
2203
+ type: mteb/summeval
2204
+ config: default
2205
+ split: test
2206
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2207
+ metrics:
2208
+ - type: cos_sim_pearson
2209
+ value: 30.198022505886435
2210
+ - type: cos_sim_spearman
2211
+ value: 30.40170257939193
2212
+ - type: dot_pearson
2213
+ value: 30.198015316402614
2214
+ - type: dot_spearman
2215
+ value: 30.40170257939193
2216
+ - task:
2217
+ type: Retrieval
2218
+ dataset:
2219
+ name: MTEB TRECCOVID
2220
+ type: trec-covid
2221
+ config: default
2222
+ split: test
2223
+ revision: None
2224
+ metrics:
2225
+ - type: map_at_1
2226
+ value: 0.242
2227
+ - type: map_at_10
2228
+ value: 2.17
2229
+ - type: map_at_100
2230
+ value: 12.221
2231
+ - type: map_at_1000
2232
+ value: 28.63
2233
+ - type: map_at_3
2234
+ value: 0.728
2235
+ - type: map_at_5
2236
+ value: 1.185
2237
+ - type: mrr_at_1
2238
+ value: 94
2239
+ - type: mrr_at_10
2240
+ value: 97
2241
+ - type: mrr_at_100
2242
+ value: 97
2243
+ - type: mrr_at_1000
2244
+ value: 97
2245
+ - type: mrr_at_3
2246
+ value: 97
2247
+ - type: mrr_at_5
2248
+ value: 97
2249
+ - type: ndcg_at_1
2250
+ value: 89
2251
+ - type: ndcg_at_10
2252
+ value: 82.30499999999999
2253
+ - type: ndcg_at_100
2254
+ value: 61.839999999999996
2255
+ - type: ndcg_at_1000
2256
+ value: 53.381
2257
+ - type: ndcg_at_3
2258
+ value: 88.877
2259
+ - type: ndcg_at_5
2260
+ value: 86.05199999999999
2261
+ - type: precision_at_1
2262
+ value: 94
2263
+ - type: precision_at_10
2264
+ value: 87
2265
+ - type: precision_at_100
2266
+ value: 63.38
2267
+ - type: precision_at_1000
2268
+ value: 23.498
2269
+ - type: precision_at_3
2270
+ value: 94
2271
+ - type: precision_at_5
2272
+ value: 92
2273
+ - type: recall_at_1
2274
+ value: 0.242
2275
+ - type: recall_at_10
2276
+ value: 2.302
2277
+ - type: recall_at_100
2278
+ value: 14.979000000000001
2279
+ - type: recall_at_1000
2280
+ value: 49.638
2281
+ - type: recall_at_3
2282
+ value: 0.753
2283
+ - type: recall_at_5
2284
+ value: 1.226
2285
+ - task:
2286
+ type: Retrieval
2287
+ dataset:
2288
+ name: MTEB Touche2020
2289
+ type: webis-touche2020
2290
+ config: default
2291
+ split: test
2292
+ revision: None
2293
+ metrics:
2294
+ - type: map_at_1
2295
+ value: 3.006
2296
+ - type: map_at_10
2297
+ value: 11.805
2298
+ - type: map_at_100
2299
+ value: 18.146
2300
+ - type: map_at_1000
2301
+ value: 19.788
2302
+ - type: map_at_3
2303
+ value: 5.914
2304
+ - type: map_at_5
2305
+ value: 8.801
2306
+ - type: mrr_at_1
2307
+ value: 40.816
2308
+ - type: mrr_at_10
2309
+ value: 56.36600000000001
2310
+ - type: mrr_at_100
2311
+ value: 56.721999999999994
2312
+ - type: mrr_at_1000
2313
+ value: 56.721999999999994
2314
+ - type: mrr_at_3
2315
+ value: 52.041000000000004
2316
+ - type: mrr_at_5
2317
+ value: 54.796
2318
+ - type: ndcg_at_1
2319
+ value: 37.755
2320
+ - type: ndcg_at_10
2321
+ value: 29.863
2322
+ - type: ndcg_at_100
2323
+ value: 39.571
2324
+ - type: ndcg_at_1000
2325
+ value: 51.385999999999996
2326
+ - type: ndcg_at_3
2327
+ value: 32.578
2328
+ - type: ndcg_at_5
2329
+ value: 32.351
2330
+ - type: precision_at_1
2331
+ value: 40.816
2332
+ - type: precision_at_10
2333
+ value: 26.531
2334
+ - type: precision_at_100
2335
+ value: 7.796
2336
+ - type: precision_at_1000
2337
+ value: 1.555
2338
+ - type: precision_at_3
2339
+ value: 32.653
2340
+ - type: precision_at_5
2341
+ value: 33.061
2342
+ - type: recall_at_1
2343
+ value: 3.006
2344
+ - type: recall_at_10
2345
+ value: 18.738
2346
+ - type: recall_at_100
2347
+ value: 48.058
2348
+ - type: recall_at_1000
2349
+ value: 83.41300000000001
2350
+ - type: recall_at_3
2351
+ value: 7.166
2352
+ - type: recall_at_5
2353
+ value: 12.102
2354
+ - task:
2355
+ type: Classification
2356
+ dataset:
2357
+ name: MTEB ToxicConversationsClassification
2358
+ type: mteb/toxic_conversations_50k
2359
+ config: default
2360
+ split: test
2361
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2362
+ metrics:
2363
+ - type: accuracy
2364
+ value: 71.4178
2365
+ - type: ap
2366
+ value: 14.648781342150446
2367
+ - type: f1
2368
+ value: 55.07299194946378
2369
+ - task:
2370
+ type: Classification
2371
+ dataset:
2372
+ name: MTEB TweetSentimentExtractionClassification
2373
+ type: mteb/tweet_sentiment_extraction
2374
+ config: default
2375
+ split: test
2376
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2377
+ metrics:
2378
+ - type: accuracy
2379
+ value: 60.919637804187886
2380
+ - type: f1
2381
+ value: 61.24122013967399
2382
+ - task:
2383
+ type: Clustering
2384
+ dataset:
2385
+ name: MTEB TwentyNewsgroupsClustering
2386
+ type: mteb/twentynewsgroups-clustering
2387
+ config: default
2388
+ split: test
2389
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2390
+ metrics:
2391
+ - type: v_measure
2392
+ value: 49.207896583685695
2393
+ - task:
2394
+ type: PairClassification
2395
+ dataset:
2396
+ name: MTEB TwitterSemEval2015
2397
+ type: mteb/twittersemeval2015-pairclassification
2398
+ config: default
2399
+ split: test
2400
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2401
+ metrics:
2402
+ - type: cos_sim_accuracy
2403
+ value: 86.23114978840078
2404
+ - type: cos_sim_ap
2405
+ value: 74.26624727825818
2406
+ - type: cos_sim_f1
2407
+ value: 68.72377190817083
2408
+ - type: cos_sim_precision
2409
+ value: 64.56400742115028
2410
+ - type: cos_sim_recall
2411
+ value: 73.45646437994723
2412
+ - type: dot_accuracy
2413
+ value: 86.23114978840078
2414
+ - type: dot_ap
2415
+ value: 74.26624032659652
2416
+ - type: dot_f1
2417
+ value: 68.72377190817083
2418
+ - type: dot_precision
2419
+ value: 64.56400742115028
2420
+ - type: dot_recall
2421
+ value: 73.45646437994723
2422
+ - type: euclidean_accuracy
2423
+ value: 86.23114978840078
2424
+ - type: euclidean_ap
2425
+ value: 74.26624714480556
2426
+ - type: euclidean_f1
2427
+ value: 68.72377190817083
2428
+ - type: euclidean_precision
2429
+ value: 64.56400742115028
2430
+ - type: euclidean_recall
2431
+ value: 73.45646437994723
2432
+ - type: manhattan_accuracy
2433
+ value: 86.16558383501221
2434
+ - type: manhattan_ap
2435
+ value: 74.2091943976357
2436
+ - type: manhattan_f1
2437
+ value: 68.64221520524654
2438
+ - type: manhattan_precision
2439
+ value: 63.59135913591359
2440
+ - type: manhattan_recall
2441
+ value: 74.5646437994723
2442
+ - type: max_accuracy
2443
+ value: 86.23114978840078
2444
+ - type: max_ap
2445
+ value: 74.26624727825818
2446
+ - type: max_f1
2447
+ value: 68.72377190817083
2448
+ - task:
2449
+ type: PairClassification
2450
+ dataset:
2451
+ name: MTEB TwitterURLCorpus
2452
+ type: mteb/twitterurlcorpus-pairclassification
2453
+ config: default
2454
+ split: test
2455
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2456
+ metrics:
2457
+ - type: cos_sim_accuracy
2458
+ value: 89.3681841114604
2459
+ - type: cos_sim_ap
2460
+ value: 86.65166387498546
2461
+ - type: cos_sim_f1
2462
+ value: 79.02581944698774
2463
+ - type: cos_sim_precision
2464
+ value: 75.35796605434099
2465
+ - type: cos_sim_recall
2466
+ value: 83.06898675700647
2467
+ - type: dot_accuracy
2468
+ value: 89.3681841114604
2469
+ - type: dot_ap
2470
+ value: 86.65166019802056
2471
+ - type: dot_f1
2472
+ value: 79.02581944698774
2473
+ - type: dot_precision
2474
+ value: 75.35796605434099
2475
+ - type: dot_recall
2476
+ value: 83.06898675700647
2477
+ - type: euclidean_accuracy
2478
+ value: 89.3681841114604
2479
+ - type: euclidean_ap
2480
+ value: 86.65166462876266
2481
+ - type: euclidean_f1
2482
+ value: 79.02581944698774
2483
+ - type: euclidean_precision
2484
+ value: 75.35796605434099
2485
+ - type: euclidean_recall
2486
+ value: 83.06898675700647
2487
+ - type: manhattan_accuracy
2488
+ value: 89.36624364497226
2489
+ - type: manhattan_ap
2490
+ value: 86.65076471274106
2491
+ - type: manhattan_f1
2492
+ value: 79.07408783532733
2493
+ - type: manhattan_precision
2494
+ value: 76.41102972856527
2495
+ - type: manhattan_recall
2496
+ value: 81.92947336002464
2497
+ - type: max_accuracy
2498
+ value: 89.3681841114604
2499
+ - type: max_ap
2500
+ value: 86.65166462876266
2501
+ - type: max_f1
2502
+ value: 79.07408783532733
2503
+ ---
2504
+
2505
+ # YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF
2506
+ This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2507
+ Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model.
2508
+
2509
+ ## Use with llama.cpp
2510
+ Install llama.cpp through brew (works on Mac and Linux)
2511
+
2512
+ ```bash
2513
+ brew install llama.cpp
2514
+
2515
+ ```
2516
+ Invoke the llama.cpp server or the CLI.
2517
+
2518
+ ### CLI:
2519
+ ```bash
2520
+ llama-cli --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
2521
+ ```
2522
+
2523
+ ### Server:
2524
+ ```bash
2525
+ llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
2526
+ ```
2527
+
2528
+ 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.
2529
+
2530
+ Step 1: Clone llama.cpp from GitHub.
2531
+ ```
2532
+ git clone https://github.com/ggerganov/llama.cpp
2533
+ ```
2534
+
2535
+ 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).
2536
+ ```
2537
+ cd llama.cpp && LLAMA_CURL=1 make
2538
+ ```
2539
+
2540
+ Step 3: Run inference through the main binary.
2541
+ ```
2542
+ ./llama-cli --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
2543
+ ```
2544
+ or
2545
+ ```
2546
+ ./llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
2547
+ ```