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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
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3
+ tags:
4
+ - mteb
5
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6
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10
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11
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12
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13
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14
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15
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16
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17
+ name: MTEB AmazonCounterfactualClassification (en)
18
+ type: mteb/amazon_counterfactual
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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30
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31
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32
+ name: MTEB AmazonPolarityClassification
33
+ type: mteb/amazon_polarity
34
+ config: default
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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40
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43
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45
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46
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47
+ name: MTEB AmazonReviewsClassification (en)
48
+ type: mteb/amazon_reviews_multi
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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58
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59
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60
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61
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62
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65
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66
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151
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152
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153
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154
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155
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156
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165
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186
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198
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199
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200
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201
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202
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210
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211
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215
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219
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220
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221
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222
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223
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224
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225
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+ split: test
2201
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2202
+ metrics:
2203
+ - type: cos_sim_pearson
2204
+ value: 30.783007208997144
2205
+ - type: cos_sim_spearman
2206
+ value: 30.373444721540533
2207
+ - type: dot_pearson
2208
+ value: 29.210604111143905
2209
+ - type: dot_spearman
2210
+ value: 29.98809758085659
2211
+ - task:
2212
+ type: Retrieval
2213
+ dataset:
2214
+ name: MTEB TRECCOVID
2215
+ type: trec-covid
2216
+ config: default
2217
+ split: test
2218
+ revision: None
2219
+ metrics:
2220
+ - type: map_at_1
2221
+ value: 0.234
2222
+ - type: map_at_10
2223
+ value: 1.894
2224
+ - type: map_at_100
2225
+ value: 1.894
2226
+ - type: map_at_1000
2227
+ value: 1.894
2228
+ - type: map_at_3
2229
+ value: 0.636
2230
+ - type: map_at_5
2231
+ value: 1.0
2232
+ - type: mrr_at_1
2233
+ value: 88.0
2234
+ - type: mrr_at_10
2235
+ value: 93.667
2236
+ - type: mrr_at_100
2237
+ value: 93.667
2238
+ - type: mrr_at_1000
2239
+ value: 93.667
2240
+ - type: mrr_at_3
2241
+ value: 93.667
2242
+ - type: mrr_at_5
2243
+ value: 93.667
2244
+ - type: ndcg_at_1
2245
+ value: 85.0
2246
+ - type: ndcg_at_10
2247
+ value: 74.798
2248
+ - type: ndcg_at_100
2249
+ value: 16.462
2250
+ - type: ndcg_at_1000
2251
+ value: 7.0889999999999995
2252
+ - type: ndcg_at_3
2253
+ value: 80.754
2254
+ - type: ndcg_at_5
2255
+ value: 77.319
2256
+ - type: precision_at_1
2257
+ value: 88.0
2258
+ - type: precision_at_10
2259
+ value: 78.0
2260
+ - type: precision_at_100
2261
+ value: 7.8
2262
+ - type: precision_at_1000
2263
+ value: 0.7799999999999999
2264
+ - type: precision_at_3
2265
+ value: 83.333
2266
+ - type: precision_at_5
2267
+ value: 80.80000000000001
2268
+ - type: recall_at_1
2269
+ value: 0.234
2270
+ - type: recall_at_10
2271
+ value: 2.093
2272
+ - type: recall_at_100
2273
+ value: 2.093
2274
+ - type: recall_at_1000
2275
+ value: 2.093
2276
+ - type: recall_at_3
2277
+ value: 0.662
2278
+ - type: recall_at_5
2279
+ value: 1.0739999999999998
2280
+ - task:
2281
+ type: Retrieval
2282
+ dataset:
2283
+ name: MTEB Touche2020
2284
+ type: webis-touche2020
2285
+ config: default
2286
+ split: test
2287
+ revision: None
2288
+ metrics:
2289
+ - type: map_at_1
2290
+ value: 2.703
2291
+ - type: map_at_10
2292
+ value: 10.866000000000001
2293
+ - type: map_at_100
2294
+ value: 10.866000000000001
2295
+ - type: map_at_1000
2296
+ value: 10.866000000000001
2297
+ - type: map_at_3
2298
+ value: 5.909
2299
+ - type: map_at_5
2300
+ value: 7.35
2301
+ - type: mrr_at_1
2302
+ value: 36.735
2303
+ - type: mrr_at_10
2304
+ value: 53.583000000000006
2305
+ - type: mrr_at_100
2306
+ value: 53.583000000000006
2307
+ - type: mrr_at_1000
2308
+ value: 53.583000000000006
2309
+ - type: mrr_at_3
2310
+ value: 49.32
2311
+ - type: mrr_at_5
2312
+ value: 51.769
2313
+ - type: ndcg_at_1
2314
+ value: 34.694
2315
+ - type: ndcg_at_10
2316
+ value: 27.926000000000002
2317
+ - type: ndcg_at_100
2318
+ value: 22.701
2319
+ - type: ndcg_at_1000
2320
+ value: 22.701
2321
+ - type: ndcg_at_3
2322
+ value: 32.073
2323
+ - type: ndcg_at_5
2324
+ value: 28.327999999999996
2325
+ - type: precision_at_1
2326
+ value: 36.735
2327
+ - type: precision_at_10
2328
+ value: 24.694
2329
+ - type: precision_at_100
2330
+ value: 2.469
2331
+ - type: precision_at_1000
2332
+ value: 0.247
2333
+ - type: precision_at_3
2334
+ value: 31.973000000000003
2335
+ - type: precision_at_5
2336
+ value: 26.939
2337
+ - type: recall_at_1
2338
+ value: 2.703
2339
+ - type: recall_at_10
2340
+ value: 17.702
2341
+ - type: recall_at_100
2342
+ value: 17.702
2343
+ - type: recall_at_1000
2344
+ value: 17.702
2345
+ - type: recall_at_3
2346
+ value: 7.208
2347
+ - type: recall_at_5
2348
+ value: 9.748999999999999
2349
+ - task:
2350
+ type: Classification
2351
+ dataset:
2352
+ name: MTEB ToxicConversationsClassification
2353
+ type: mteb/toxic_conversations_50k
2354
+ config: default
2355
+ split: test
2356
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2357
+ metrics:
2358
+ - type: accuracy
2359
+ value: 70.79960000000001
2360
+ - type: ap
2361
+ value: 15.467565415565815
2362
+ - type: f1
2363
+ value: 55.28639823443618
2364
+ - task:
2365
+ type: Classification
2366
+ dataset:
2367
+ name: MTEB TweetSentimentExtractionClassification
2368
+ type: mteb/tweet_sentiment_extraction
2369
+ config: default
2370
+ split: test
2371
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2372
+ metrics:
2373
+ - type: accuracy
2374
+ value: 64.7792869269949
2375
+ - type: f1
2376
+ value: 65.08597154774318
2377
+ - task:
2378
+ type: Clustering
2379
+ dataset:
2380
+ name: MTEB TwentyNewsgroupsClustering
2381
+ type: mteb/twentynewsgroups-clustering
2382
+ config: default
2383
+ split: test
2384
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2385
+ metrics:
2386
+ - type: v_measure
2387
+ value: 55.70352297774293
2388
+ - task:
2389
+ type: PairClassification
2390
+ dataset:
2391
+ name: MTEB TwitterSemEval2015
2392
+ type: mteb/twittersemeval2015-pairclassification
2393
+ config: default
2394
+ split: test
2395
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2396
+ metrics:
2397
+ - type: cos_sim_accuracy
2398
+ value: 88.27561542588067
2399
+ - type: cos_sim_ap
2400
+ value: 81.08262141256193
2401
+ - type: cos_sim_f1
2402
+ value: 73.82341501361338
2403
+ - type: cos_sim_precision
2404
+ value: 72.5720112159062
2405
+ - type: cos_sim_recall
2406
+ value: 75.11873350923483
2407
+ - type: dot_accuracy
2408
+ value: 86.66030875603504
2409
+ - type: dot_ap
2410
+ value: 76.6052349228621
2411
+ - type: dot_f1
2412
+ value: 70.13897280966768
2413
+ - type: dot_precision
2414
+ value: 64.70457079152732
2415
+ - type: dot_recall
2416
+ value: 76.56992084432717
2417
+ - type: euclidean_accuracy
2418
+ value: 88.37098408535495
2419
+ - type: euclidean_ap
2420
+ value: 81.12515230092113
2421
+ - type: euclidean_f1
2422
+ value: 74.10338225909379
2423
+ - type: euclidean_precision
2424
+ value: 71.76761433868974
2425
+ - type: euclidean_recall
2426
+ value: 76.59630606860158
2427
+ - type: manhattan_accuracy
2428
+ value: 88.34118137926924
2429
+ - type: manhattan_ap
2430
+ value: 80.95751834536561
2431
+ - type: manhattan_f1
2432
+ value: 73.9119496855346
2433
+ - type: manhattan_precision
2434
+ value: 70.625
2435
+ - type: manhattan_recall
2436
+ value: 77.5197889182058
2437
+ - type: max_accuracy
2438
+ value: 88.37098408535495
2439
+ - type: max_ap
2440
+ value: 81.12515230092113
2441
+ - type: max_f1
2442
+ value: 74.10338225909379
2443
+ - task:
2444
+ type: PairClassification
2445
+ dataset:
2446
+ name: MTEB TwitterURLCorpus
2447
+ type: mteb/twitterurlcorpus-pairclassification
2448
+ config: default
2449
+ split: test
2450
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2451
+ metrics:
2452
+ - type: cos_sim_accuracy
2453
+ value: 89.79896767182831
2454
+ - type: cos_sim_ap
2455
+ value: 87.40071784061065
2456
+ - type: cos_sim_f1
2457
+ value: 79.87753144712087
2458
+ - type: cos_sim_precision
2459
+ value: 76.67304015296367
2460
+ - type: cos_sim_recall
2461
+ value: 83.3615645210964
2462
+ - type: dot_accuracy
2463
+ value: 88.95486474948578
2464
+ - type: dot_ap
2465
+ value: 86.00227979119943
2466
+ - type: dot_f1
2467
+ value: 78.54601474525914
2468
+ - type: dot_precision
2469
+ value: 75.00525394045535
2470
+ - type: dot_recall
2471
+ value: 82.43763473975977
2472
+ - type: euclidean_accuracy
2473
+ value: 89.7892653393876
2474
+ - type: euclidean_ap
2475
+ value: 87.42174706480819
2476
+ - type: euclidean_f1
2477
+ value: 80.07283321194465
2478
+ - type: euclidean_precision
2479
+ value: 75.96738529574351
2480
+ - type: euclidean_recall
2481
+ value: 84.6473668001232
2482
+ - type: manhattan_accuracy
2483
+ value: 89.8474793340319
2484
+ - type: manhattan_ap
2485
+ value: 87.47814292587448
2486
+ - type: manhattan_f1
2487
+ value: 80.15461150280949
2488
+ - type: manhattan_precision
2489
+ value: 74.88798234468
2490
+ - type: manhattan_recall
2491
+ value: 86.21804742839544
2492
+ - type: max_accuracy
2493
+ value: 89.8474793340319
2494
+ - type: max_ap
2495
+ value: 87.47814292587448
2496
+ - type: max_f1
2497
+ value: 80.15461150280949
2498
+ ---
2499
+
2500
+ # GregorBiswanger/GritLM-7B-Q4_K_M-GGUF
2501
+ This model was converted to GGUF format from [`GritLM/GritLM-7B`](https://huggingface.co/GritLM/GritLM-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2502
+ Refer to the [original model card](https://huggingface.co/GritLM/GritLM-7B) for more details on the model.
2503
+ ## Use with llama.cpp
2504
+
2505
+ Install llama.cpp through brew.
2506
+
2507
+ ```bash
2508
+ brew install ggerganov/ggerganov/llama.cpp
2509
+ ```
2510
+ Invoke the llama.cpp server or the CLI.
2511
+
2512
+ CLI:
2513
+
2514
+ ```bash
2515
+ llama-cli --hf-repo GregorBiswanger/GritLM-7B-Q4_K_M-GGUF --model gritlm-7b.Q4_K_M.gguf -p "The meaning to life and the universe is"
2516
+ ```
2517
+
2518
+ Server:
2519
+
2520
+ ```bash
2521
+ llama-server --hf-repo GregorBiswanger/GritLM-7B-Q4_K_M-GGUF --model gritlm-7b.Q4_K_M.gguf -c 2048
2522
+ ```
2523
+
2524
+ 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.
2525
+
2526
+ ```
2527
+ git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m gritlm-7b.Q4_K_M.gguf -n 128
2528
+ ```