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1
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18
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19
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20
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21
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22
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23
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24
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34
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35
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36
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37
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38
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39
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51
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2195
+ - type: mrr
2196
+ value: 56.28279910449028
2197
+ - task:
2198
+ type: Summarization
2199
+ dataset:
2200
+ name: MTEB SummEval
2201
+ type: mteb/summeval
2202
+ config: default
2203
+ split: test
2204
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2205
+ metrics:
2206
+ - type: cos_sim_pearson
2207
+ value: 30.723915667479673
2208
+ - type: cos_sim_spearman
2209
+ value: 32.029070449745234
2210
+ - type: dot_pearson
2211
+ value: 28.864944212481454
2212
+ - type: dot_spearman
2213
+ value: 27.939266999596725
2214
+ - task:
2215
+ type: Retrieval
2216
+ dataset:
2217
+ name: MTEB TRECCOVID
2218
+ type: trec-covid
2219
+ config: default
2220
+ split: test
2221
+ revision: None
2222
+ metrics:
2223
+ - type: map_at_1
2224
+ value: 0.231
2225
+ - type: map_at_10
2226
+ value: 1.949
2227
+ - type: map_at_100
2228
+ value: 10.023
2229
+ - type: map_at_1000
2230
+ value: 23.485
2231
+ - type: map_at_3
2232
+ value: 0.652
2233
+ - type: map_at_5
2234
+ value: 1.054
2235
+ - type: mrr_at_1
2236
+ value: 86
2237
+ - type: mrr_at_10
2238
+ value: 92.067
2239
+ - type: mrr_at_100
2240
+ value: 92.067
2241
+ - type: mrr_at_1000
2242
+ value: 92.067
2243
+ - type: mrr_at_3
2244
+ value: 91.667
2245
+ - type: mrr_at_5
2246
+ value: 92.067
2247
+ - type: ndcg_at_1
2248
+ value: 83
2249
+ - type: ndcg_at_10
2250
+ value: 76.32900000000001
2251
+ - type: ndcg_at_100
2252
+ value: 54.662
2253
+ - type: ndcg_at_1000
2254
+ value: 48.062
2255
+ - type: ndcg_at_3
2256
+ value: 81.827
2257
+ - type: ndcg_at_5
2258
+ value: 80.664
2259
+ - type: precision_at_1
2260
+ value: 86
2261
+ - type: precision_at_10
2262
+ value: 80
2263
+ - type: precision_at_100
2264
+ value: 55.48
2265
+ - type: precision_at_1000
2266
+ value: 20.938000000000002
2267
+ - type: precision_at_3
2268
+ value: 85.333
2269
+ - type: precision_at_5
2270
+ value: 84.39999999999999
2271
+ - type: recall_at_1
2272
+ value: 0.231
2273
+ - type: recall_at_10
2274
+ value: 2.158
2275
+ - type: recall_at_100
2276
+ value: 13.344000000000001
2277
+ - type: recall_at_1000
2278
+ value: 44.31
2279
+ - type: recall_at_3
2280
+ value: 0.6779999999999999
2281
+ - type: recall_at_5
2282
+ value: 1.13
2283
+ - task:
2284
+ type: Retrieval
2285
+ dataset:
2286
+ name: MTEB Touche2020
2287
+ type: webis-touche2020
2288
+ config: default
2289
+ split: test
2290
+ revision: None
2291
+ metrics:
2292
+ - type: map_at_1
2293
+ value: 2.524
2294
+ - type: map_at_10
2295
+ value: 10.183
2296
+ - type: map_at_100
2297
+ value: 16.625
2298
+ - type: map_at_1000
2299
+ value: 18.017
2300
+ - type: map_at_3
2301
+ value: 5.169
2302
+ - type: map_at_5
2303
+ value: 6.772
2304
+ - type: mrr_at_1
2305
+ value: 32.653
2306
+ - type: mrr_at_10
2307
+ value: 47.128
2308
+ - type: mrr_at_100
2309
+ value: 48.458
2310
+ - type: mrr_at_1000
2311
+ value: 48.473
2312
+ - type: mrr_at_3
2313
+ value: 44.897999999999996
2314
+ - type: mrr_at_5
2315
+ value: 45.306000000000004
2316
+ - type: ndcg_at_1
2317
+ value: 30.612000000000002
2318
+ - type: ndcg_at_10
2319
+ value: 24.928
2320
+ - type: ndcg_at_100
2321
+ value: 37.613
2322
+ - type: ndcg_at_1000
2323
+ value: 48.528
2324
+ - type: ndcg_at_3
2325
+ value: 28.829
2326
+ - type: ndcg_at_5
2327
+ value: 25.237
2328
+ - type: precision_at_1
2329
+ value: 32.653
2330
+ - type: precision_at_10
2331
+ value: 22.448999999999998
2332
+ - type: precision_at_100
2333
+ value: 8.02
2334
+ - type: precision_at_1000
2335
+ value: 1.537
2336
+ - type: precision_at_3
2337
+ value: 30.612000000000002
2338
+ - type: precision_at_5
2339
+ value: 24.490000000000002
2340
+ - type: recall_at_1
2341
+ value: 2.524
2342
+ - type: recall_at_10
2343
+ value: 16.38
2344
+ - type: recall_at_100
2345
+ value: 49.529
2346
+ - type: recall_at_1000
2347
+ value: 83.598
2348
+ - type: recall_at_3
2349
+ value: 6.411
2350
+ - type: recall_at_5
2351
+ value: 8.932
2352
+ - task:
2353
+ type: Classification
2354
+ dataset:
2355
+ name: MTEB ToxicConversationsClassification
2356
+ type: mteb/toxic_conversations_50k
2357
+ config: default
2358
+ split: test
2359
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2360
+ metrics:
2361
+ - type: accuracy
2362
+ value: 71.09020000000001
2363
+ - type: ap
2364
+ value: 14.451710060978993
2365
+ - type: f1
2366
+ value: 54.7874410609049
2367
+ - task:
2368
+ type: Classification
2369
+ dataset:
2370
+ name: MTEB TweetSentimentExtractionClassification
2371
+ type: mteb/tweet_sentiment_extraction
2372
+ config: default
2373
+ split: test
2374
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2375
+ metrics:
2376
+ - type: accuracy
2377
+ value: 59.745331069609506
2378
+ - type: f1
2379
+ value: 60.08387848592697
2380
+ - task:
2381
+ type: Clustering
2382
+ dataset:
2383
+ name: MTEB TwentyNewsgroupsClustering
2384
+ type: mteb/twentynewsgroups-clustering
2385
+ config: default
2386
+ split: test
2387
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2388
+ metrics:
2389
+ - type: v_measure
2390
+ value: 51.71549485462037
2391
+ - task:
2392
+ type: PairClassification
2393
+ dataset:
2394
+ name: MTEB TwitterSemEval2015
2395
+ type: mteb/twittersemeval2015-pairclassification
2396
+ config: default
2397
+ split: test
2398
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2399
+ metrics:
2400
+ - type: cos_sim_accuracy
2401
+ value: 87.39345532574357
2402
+ - type: cos_sim_ap
2403
+ value: 78.16796549696478
2404
+ - type: cos_sim_f1
2405
+ value: 71.27713276123171
2406
+ - type: cos_sim_precision
2407
+ value: 68.3115626511853
2408
+ - type: cos_sim_recall
2409
+ value: 74.51187335092348
2410
+ - type: dot_accuracy
2411
+ value: 85.12248912201228
2412
+ - type: dot_ap
2413
+ value: 69.26039256107077
2414
+ - type: dot_f1
2415
+ value: 65.04294321240867
2416
+ - type: dot_precision
2417
+ value: 63.251059586138126
2418
+ - type: dot_recall
2419
+ value: 66.93931398416886
2420
+ - type: euclidean_accuracy
2421
+ value: 87.07754664123503
2422
+ - type: euclidean_ap
2423
+ value: 77.7872176038945
2424
+ - type: euclidean_f1
2425
+ value: 70.85587801278899
2426
+ - type: euclidean_precision
2427
+ value: 66.3519115614924
2428
+ - type: euclidean_recall
2429
+ value: 76.01583113456465
2430
+ - type: manhattan_accuracy
2431
+ value: 87.07754664123503
2432
+ - type: manhattan_ap
2433
+ value: 77.7341400185556
2434
+ - type: manhattan_f1
2435
+ value: 70.80310880829015
2436
+ - type: manhattan_precision
2437
+ value: 69.54198473282443
2438
+ - type: manhattan_recall
2439
+ value: 72.1108179419525
2440
+ - type: max_accuracy
2441
+ value: 87.39345532574357
2442
+ - type: max_ap
2443
+ value: 78.16796549696478
2444
+ - type: max_f1
2445
+ value: 71.27713276123171
2446
+ - task:
2447
+ type: PairClassification
2448
+ dataset:
2449
+ name: MTEB TwitterURLCorpus
2450
+ type: mteb/twitterurlcorpus-pairclassification
2451
+ config: default
2452
+ split: test
2453
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2454
+ metrics:
2455
+ - type: cos_sim_accuracy
2456
+ value: 89.09457833663213
2457
+ - type: cos_sim_ap
2458
+ value: 86.33024314706873
2459
+ - type: cos_sim_f1
2460
+ value: 78.59623733719248
2461
+ - type: cos_sim_precision
2462
+ value: 74.13322413322413
2463
+ - type: cos_sim_recall
2464
+ value: 83.63104404065291
2465
+ - type: dot_accuracy
2466
+ value: 88.3086894089339
2467
+ - type: dot_ap
2468
+ value: 83.92225241805097
2469
+ - type: dot_f1
2470
+ value: 76.8721826377781
2471
+ - type: dot_precision
2472
+ value: 72.8168044077135
2473
+ - type: dot_recall
2474
+ value: 81.40591315060055
2475
+ - type: euclidean_accuracy
2476
+ value: 88.77052043311213
2477
+ - type: euclidean_ap
2478
+ value: 85.7410710218755
2479
+ - type: euclidean_f1
2480
+ value: 77.97705489398781
2481
+ - type: euclidean_precision
2482
+ value: 73.77713657598241
2483
+ - type: euclidean_recall
2484
+ value: 82.68401601478288
2485
+ - type: manhattan_accuracy
2486
+ value: 88.73753250281368
2487
+ - type: manhattan_ap
2488
+ value: 85.72867199072802
2489
+ - type: manhattan_f1
2490
+ value: 77.89774182922812
2491
+ - type: manhattan_precision
2492
+ value: 74.23787931635857
2493
+ - type: manhattan_recall
2494
+ value: 81.93717277486911
2495
+ - type: max_accuracy
2496
+ value: 89.09457833663213
2497
+ - type: max_ap
2498
+ value: 86.33024314706873
2499
+ - type: max_f1
2500
+ value: 78.59623733719248
2501
+ ---
2502
+
2503
+ # agier9/UAE-Large-V1-Q5_K_S-GGUF
2504
+ This model was converted to GGUF format from [`WhereIsAI/UAE-Large-V1`](https://huggingface.co/WhereIsAI/UAE-Large-V1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2505
+ Refer to the [original model card](https://huggingface.co/WhereIsAI/UAE-Large-V1) for more details on the model.
2506
+ ## Use with llama.cpp
2507
+ Install llama.cpp through brew.
2508
+ ```bash
2509
+ brew install ggerganov/ggerganov/llama.cpp
2510
+ ```
2511
+ Invoke the llama.cpp server or the CLI.
2512
+ CLI:
2513
+ ```bash
2514
+ llama-cli --hf-repo agier9/UAE-Large-V1-Q5_K_S-GGUF --model uae-large-v1-q5_k_s.gguf -p "The meaning to life and the universe is"
2515
+ ```
2516
+ Server:
2517
+ ```bash
2518
+ llama-server --hf-repo agier9/UAE-Large-V1-Q5_K_S-GGUF --model uae-large-v1-q5_k_s.gguf -c 2048
2519
+ ```
2520
+ 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.
2521
+ ```
2522
+ git clone https://github.com/ggerganov/llama.cpp && \
2523
+ cd llama.cpp && \
2524
+ make && \
2525
+ ./main -m uae-large-v1-q5_k_s.gguf -n 128
2526
+ ```