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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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3
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12
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14
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15
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16
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17
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18
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19
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20
+ name: MTEB AmazonCounterfactualClassification (en)
21
+ type: mteb/amazon_counterfactual
22
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23
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24
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25
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26
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31
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33
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34
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35
+ name: MTEB AmazonPolarityClassification
36
+ type: mteb/amazon_polarity
37
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38
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39
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40
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41
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49
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50
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51
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52
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53
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54
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55
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202
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+ split: test
2191
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2192
+ metrics:
2193
+ - type: map
2194
+ value: 55.946944700355395
2195
+ - type: mrr
2196
+ value: 56.97151398438164
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: 31.541657650692905
2208
+ - type: cos_sim_spearman
2209
+ value: 31.605804192286303
2210
+ - type: dot_pearson
2211
+ value: 28.26905996736398
2212
+ - type: dot_spearman
2213
+ value: 27.864801765851187
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: 0.22599999999999998
2225
+ - type: map_at_10
2226
+ value: 1.8870000000000002
2227
+ - type: map_at_100
2228
+ value: 9.78
2229
+ - type: map_at_1000
2230
+ value: 22.514
2231
+ - type: map_at_3
2232
+ value: 0.6669999999999999
2233
+ - type: map_at_5
2234
+ value: 1.077
2235
+ - type: mrr_at_1
2236
+ value: 82.0
2237
+ - type: mrr_at_10
2238
+ value: 89.86699999999999
2239
+ - type: mrr_at_100
2240
+ value: 89.86699999999999
2241
+ - type: mrr_at_1000
2242
+ value: 89.86699999999999
2243
+ - type: mrr_at_3
2244
+ value: 89.667
2245
+ - type: mrr_at_5
2246
+ value: 89.667
2247
+ - type: ndcg_at_1
2248
+ value: 79.0
2249
+ - type: ndcg_at_10
2250
+ value: 74.818
2251
+ - type: ndcg_at_100
2252
+ value: 53.715999999999994
2253
+ - type: ndcg_at_1000
2254
+ value: 47.082
2255
+ - type: ndcg_at_3
2256
+ value: 82.134
2257
+ - type: ndcg_at_5
2258
+ value: 79.81899999999999
2259
+ - type: precision_at_1
2260
+ value: 82.0
2261
+ - type: precision_at_10
2262
+ value: 78.0
2263
+ - type: precision_at_100
2264
+ value: 54.48
2265
+ - type: precision_at_1000
2266
+ value: 20.518
2267
+ - type: precision_at_3
2268
+ value: 87.333
2269
+ - type: precision_at_5
2270
+ value: 85.2
2271
+ - type: recall_at_1
2272
+ value: 0.22599999999999998
2273
+ - type: recall_at_10
2274
+ value: 2.072
2275
+ - type: recall_at_100
2276
+ value: 13.013
2277
+ - type: recall_at_1000
2278
+ value: 43.462
2279
+ - type: recall_at_3
2280
+ value: 0.695
2281
+ - type: recall_at_5
2282
+ value: 1.139
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: 2.328
2294
+ - type: map_at_10
2295
+ value: 9.795
2296
+ - type: map_at_100
2297
+ value: 15.801000000000002
2298
+ - type: map_at_1000
2299
+ value: 17.23
2300
+ - type: map_at_3
2301
+ value: 4.734
2302
+ - type: map_at_5
2303
+ value: 6.644
2304
+ - type: mrr_at_1
2305
+ value: 30.612000000000002
2306
+ - type: mrr_at_10
2307
+ value: 46.902
2308
+ - type: mrr_at_100
2309
+ value: 47.495
2310
+ - type: mrr_at_1000
2311
+ value: 47.495
2312
+ - type: mrr_at_3
2313
+ value: 41.156
2314
+ - type: mrr_at_5
2315
+ value: 44.218
2316
+ - type: ndcg_at_1
2317
+ value: 28.571
2318
+ - type: ndcg_at_10
2319
+ value: 24.806
2320
+ - type: ndcg_at_100
2321
+ value: 36.419000000000004
2322
+ - type: ndcg_at_1000
2323
+ value: 47.272999999999996
2324
+ - type: ndcg_at_3
2325
+ value: 25.666
2326
+ - type: ndcg_at_5
2327
+ value: 25.448999999999998
2328
+ - type: precision_at_1
2329
+ value: 30.612000000000002
2330
+ - type: precision_at_10
2331
+ value: 23.061
2332
+ - type: precision_at_100
2333
+ value: 7.714
2334
+ - type: precision_at_1000
2335
+ value: 1.484
2336
+ - type: precision_at_3
2337
+ value: 26.531
2338
+ - type: precision_at_5
2339
+ value: 26.122
2340
+ - type: recall_at_1
2341
+ value: 2.328
2342
+ - type: recall_at_10
2343
+ value: 16.524
2344
+ - type: recall_at_100
2345
+ value: 47.179
2346
+ - type: recall_at_1000
2347
+ value: 81.22200000000001
2348
+ - type: recall_at_3
2349
+ value: 5.745
2350
+ - type: recall_at_5
2351
+ value: 9.339
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: 70.9142
2363
+ - type: ap
2364
+ value: 14.335574772555415
2365
+ - type: f1
2366
+ value: 54.62839595194111
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: 59.94340690435768
2378
+ - type: f1
2379
+ value: 60.286487936731916
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: 51.26597708987974
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: 87.48882398521786
2402
+ - type: cos_sim_ap
2403
+ value: 79.04326607602204
2404
+ - type: cos_sim_f1
2405
+ value: 71.64566826860633
2406
+ - type: cos_sim_precision
2407
+ value: 70.55512918905092
2408
+ - type: cos_sim_recall
2409
+ value: 72.77044854881267
2410
+ - type: dot_accuracy
2411
+ value: 84.19264469213805
2412
+ - type: dot_ap
2413
+ value: 67.96360043562528
2414
+ - type: dot_f1
2415
+ value: 64.06418393006827
2416
+ - type: dot_precision
2417
+ value: 58.64941898706424
2418
+ - type: dot_recall
2419
+ value: 70.58047493403694
2420
+ - type: euclidean_accuracy
2421
+ value: 87.45902127913214
2422
+ - type: euclidean_ap
2423
+ value: 78.9742237648272
2424
+ - type: euclidean_f1
2425
+ value: 71.5553235908142
2426
+ - type: euclidean_precision
2427
+ value: 70.77955601445535
2428
+ - type: euclidean_recall
2429
+ value: 72.34828496042216
2430
+ - type: manhattan_accuracy
2431
+ value: 87.41729749061214
2432
+ - type: manhattan_ap
2433
+ value: 78.90073137580596
2434
+ - type: manhattan_f1
2435
+ value: 71.3942611553533
2436
+ - type: manhattan_precision
2437
+ value: 68.52705653967483
2438
+ - type: manhattan_recall
2439
+ value: 74.51187335092348
2440
+ - type: max_accuracy
2441
+ value: 87.48882398521786
2442
+ - type: max_ap
2443
+ value: 79.04326607602204
2444
+ - type: max_f1
2445
+ value: 71.64566826860633
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: 88.68125897465751
2457
+ - type: cos_sim_ap
2458
+ value: 85.6003454431979
2459
+ - type: cos_sim_f1
2460
+ value: 77.6957163958641
2461
+ - type: cos_sim_precision
2462
+ value: 73.0110366307807
2463
+ - type: cos_sim_recall
2464
+ value: 83.02279026793964
2465
+ - type: dot_accuracy
2466
+ value: 87.7672992587418
2467
+ - type: dot_ap
2468
+ value: 82.4971301112899
2469
+ - type: dot_f1
2470
+ value: 75.90528233151184
2471
+ - type: dot_precision
2472
+ value: 72.0370626469368
2473
+ - type: dot_recall
2474
+ value: 80.21250384970742
2475
+ - type: euclidean_accuracy
2476
+ value: 88.4503434625684
2477
+ - type: euclidean_ap
2478
+ value: 84.91949884748384
2479
+ - type: euclidean_f1
2480
+ value: 76.92365018444684
2481
+ - type: euclidean_precision
2482
+ value: 74.53245721712759
2483
+ - type: euclidean_recall
2484
+ value: 79.47336002463813
2485
+ - type: manhattan_accuracy
2486
+ value: 88.47556952691427
2487
+ - type: manhattan_ap
2488
+ value: 84.8963689101517
2489
+ - type: manhattan_f1
2490
+ value: 76.85901249256395
2491
+ - type: manhattan_precision
2492
+ value: 74.31693989071039
2493
+ - type: manhattan_recall
2494
+ value: 79.58115183246073
2495
+ - type: max_accuracy
2496
+ value: 88.68125897465751
2497
+ - type: max_ap
2498
+ value: 85.6003454431979
2499
+ - type: max_f1
2500
+ value: 77.6957163958641
2501
+ ---
2502
+
2503
+ # sabafallah/bge-large-en-v1.5-Q4_K_M-GGUF
2504
+ This model was converted to GGUF format from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) 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/BAAI/bge-large-en-v1.5) for more details on the model.
2506
+
2507
+ ## Use with llama.cpp
2508
+ Install llama.cpp through brew (works on Mac and Linux)
2509
+
2510
+ ```bash
2511
+ brew install llama.cpp
2512
+
2513
+ ```
2514
+ Invoke the llama.cpp server or the CLI.
2515
+
2516
+ ### CLI:
2517
+ ```bash
2518
+ llama-cli --hf-repo sabafallah/bge-large-en-v1.5-Q4_K_M-GGUF --hf-file bge-large-en-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
2519
+ ```
2520
+
2521
+ ### Server:
2522
+ ```bash
2523
+ llama-server --hf-repo sabafallah/bge-large-en-v1.5-Q4_K_M-GGUF --hf-file bge-large-en-v1.5-q4_k_m.gguf -c 2048
2524
+ ```
2525
+
2526
+ 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.
2527
+
2528
+ Step 1: Clone llama.cpp from GitHub.
2529
+ ```
2530
+ git clone https://github.com/ggerganov/llama.cpp
2531
+ ```
2532
+
2533
+ 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).
2534
+ ```
2535
+ cd llama.cpp && LLAMA_CURL=1 make
2536
+ ```
2537
+
2538
+ Step 3: Run inference through the main binary.
2539
+ ```
2540
+ ./llama-cli --hf-repo sabafallah/bge-large-en-v1.5-Q4_K_M-GGUF --hf-file bge-large-en-v1.5-q4_k_m.gguf -p "The meaning to life and the universe is"
2541
+ ```
2542
+ or
2543
+ ```
2544
+ ./llama-server --hf-repo sabafallah/bge-large-en-v1.5-Q4_K_M-GGUF --hf-file bge-large-en-v1.5-q4_k_m.gguf -c 2048
2545
+ ```