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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: llmrails/ember-v1
3
+ language: en
4
+ license: mit
5
+ tags:
6
+ - mteb
7
+ - sentence-transformers
8
+ - feature-extraction
9
+ - sentence-similarity
10
+ - transformers
11
+ - llama-cpp
12
+ - gguf-my-repo
13
+ model-index:
14
+ - name: ember_v1
15
+ results:
16
+ - task:
17
+ type: Classification
18
+ dataset:
19
+ name: MTEB AmazonCounterfactualClassification (en)
20
+ type: mteb/amazon_counterfactual
21
+ config: en
22
+ split: test
23
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
24
+ metrics:
25
+ - type: accuracy
26
+ value: 76.05970149253731
27
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28
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29
+ - type: f1
30
+ value: 69.8824007294685
31
+ - task:
32
+ type: Classification
33
+ dataset:
34
+ name: MTEB AmazonPolarityClassification
35
+ type: mteb/amazon_polarity
36
+ config: default
37
+ split: test
38
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
39
+ metrics:
40
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41
+ value: 91.977
42
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43
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44
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45
+ value: 91.9524133311038
46
+ - task:
47
+ type: Classification
48
+ dataset:
49
+ name: MTEB AmazonReviewsClassification (en)
50
+ type: mteb/amazon_reviews_multi
51
+ config: en
52
+ split: test
53
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
54
+ metrics:
55
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56
+ value: 47.938
57
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58
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59
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60
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61
+ dataset:
62
+ name: MTEB ArguAna
63
+ type: arguana
64
+ config: default
65
+ split: test
66
+ revision: None
67
+ metrics:
68
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69
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70
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71
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72
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74
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78
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113
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116
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118
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119
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120
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122
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127
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129
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130
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131
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132
+ type: mteb/arxiv-clustering-p2p
133
+ config: default
134
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135
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136
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137
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138
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139
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140
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141
+ dataset:
142
+ name: MTEB ArxivClusteringS2S
143
+ type: mteb/arxiv-clustering-s2s
144
+ config: default
145
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146
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
147
+ metrics:
148
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149
+ value: 43.142844164856136
150
+ - task:
151
+ type: Reranking
152
+ dataset:
153
+ name: MTEB AskUbuntuDupQuestions
154
+ type: mteb/askubuntudupquestions-reranking
155
+ config: default
156
+ split: test
157
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
158
+ metrics:
159
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160
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161
+ - type: mrr
162
+ value: 77.85560392208592
163
+ - task:
164
+ type: STS
165
+ dataset:
166
+ name: MTEB BIOSSES
167
+ type: mteb/biosses-sts
168
+ config: default
169
+ split: test
170
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
171
+ metrics:
172
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173
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174
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175
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176
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177
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178
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179
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182
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183
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184
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185
+ type: Classification
186
+ dataset:
187
+ name: MTEB Banking77Classification
188
+ type: mteb/banking77
189
+ config: default
190
+ split: test
191
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
192
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193
+ - type: accuracy
194
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195
+ - type: f1
196
+ value: 87.87886687103833
197
+ - task:
198
+ type: Clustering
199
+ dataset:
200
+ name: MTEB BiorxivClusteringP2P
201
+ type: mteb/biorxiv-clustering-p2p
202
+ config: default
203
+ split: test
204
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
205
+ metrics:
206
+ - type: v_measure
207
+ value: 38.84335510371379
208
+ - task:
209
+ type: Clustering
210
+ dataset:
211
+ name: MTEB BiorxivClusteringS2S
212
+ type: mteb/biorxiv-clustering-s2s
213
+ config: default
214
+ split: test
215
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
216
+ metrics:
217
+ - type: v_measure
218
+ value: 36.377963093857005
219
+ - task:
220
+ type: Retrieval
221
+ dataset:
222
+ name: MTEB CQADupstackAndroidRetrieval
223
+ type: BeIR/cqadupstack
224
+ config: default
225
+ split: test
226
+ revision: None
227
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228
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229
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230
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232
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+ value: 55.852215301355066
2194
+ - type: mrr
2195
+ value: 56.85527809608691
2196
+ - task:
2197
+ type: Summarization
2198
+ dataset:
2199
+ name: MTEB SummEval
2200
+ type: mteb/summeval
2201
+ config: default
2202
+ split: test
2203
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2204
+ metrics:
2205
+ - type: cos_sim_pearson
2206
+ value: 31.21442519856758
2207
+ - type: cos_sim_spearman
2208
+ value: 30.822536216936825
2209
+ - type: dot_pearson
2210
+ value: 28.661325528121807
2211
+ - type: dot_spearman
2212
+ value: 28.1435226478879
2213
+ - task:
2214
+ type: Retrieval
2215
+ dataset:
2216
+ name: MTEB TRECCOVID
2217
+ type: trec-covid
2218
+ config: default
2219
+ split: test
2220
+ revision: None
2221
+ metrics:
2222
+ - type: map_at_1
2223
+ value: 0.183
2224
+ - type: map_at_10
2225
+ value: 1.526
2226
+ - type: map_at_100
2227
+ value: 7.915
2228
+ - type: map_at_1000
2229
+ value: 19.009
2230
+ - type: map_at_3
2231
+ value: 0.541
2232
+ - type: map_at_5
2233
+ value: 0.8659999999999999
2234
+ - type: mrr_at_1
2235
+ value: 68
2236
+ - type: mrr_at_10
2237
+ value: 81.186
2238
+ - type: mrr_at_100
2239
+ value: 81.186
2240
+ - type: mrr_at_1000
2241
+ value: 81.186
2242
+ - type: mrr_at_3
2243
+ value: 80
2244
+ - type: mrr_at_5
2245
+ value: 80.9
2246
+ - type: ndcg_at_1
2247
+ value: 64
2248
+ - type: ndcg_at_10
2249
+ value: 64.13799999999999
2250
+ - type: ndcg_at_100
2251
+ value: 47.632000000000005
2252
+ - type: ndcg_at_1000
2253
+ value: 43.037
2254
+ - type: ndcg_at_3
2255
+ value: 67.542
2256
+ - type: ndcg_at_5
2257
+ value: 67.496
2258
+ - type: precision_at_1
2259
+ value: 68
2260
+ - type: precision_at_10
2261
+ value: 67.80000000000001
2262
+ - type: precision_at_100
2263
+ value: 48.980000000000004
2264
+ - type: precision_at_1000
2265
+ value: 19.036
2266
+ - type: precision_at_3
2267
+ value: 72
2268
+ - type: precision_at_5
2269
+ value: 71.2
2270
+ - type: recall_at_1
2271
+ value: 0.183
2272
+ - type: recall_at_10
2273
+ value: 1.799
2274
+ - type: recall_at_100
2275
+ value: 11.652999999999999
2276
+ - type: recall_at_1000
2277
+ value: 40.086
2278
+ - type: recall_at_3
2279
+ value: 0.5930000000000001
2280
+ - type: recall_at_5
2281
+ value: 0.983
2282
+ - task:
2283
+ type: Retrieval
2284
+ dataset:
2285
+ name: MTEB Touche2020
2286
+ type: webis-touche2020
2287
+ config: default
2288
+ split: test
2289
+ revision: None
2290
+ metrics:
2291
+ - type: map_at_1
2292
+ value: 2.29
2293
+ - type: map_at_10
2294
+ value: 9.489
2295
+ - type: map_at_100
2296
+ value: 15.051
2297
+ - type: map_at_1000
2298
+ value: 16.561999999999998
2299
+ - type: map_at_3
2300
+ value: 5.137
2301
+ - type: map_at_5
2302
+ value: 6.7989999999999995
2303
+ - type: mrr_at_1
2304
+ value: 28.571
2305
+ - type: mrr_at_10
2306
+ value: 45.699
2307
+ - type: mrr_at_100
2308
+ value: 46.461000000000006
2309
+ - type: mrr_at_1000
2310
+ value: 46.461000000000006
2311
+ - type: mrr_at_3
2312
+ value: 41.837
2313
+ - type: mrr_at_5
2314
+ value: 43.163000000000004
2315
+ - type: ndcg_at_1
2316
+ value: 23.469
2317
+ - type: ndcg_at_10
2318
+ value: 23.544999999999998
2319
+ - type: ndcg_at_100
2320
+ value: 34.572
2321
+ - type: ndcg_at_1000
2322
+ value: 46.035
2323
+ - type: ndcg_at_3
2324
+ value: 27.200000000000003
2325
+ - type: ndcg_at_5
2326
+ value: 25.266
2327
+ - type: precision_at_1
2328
+ value: 28.571
2329
+ - type: precision_at_10
2330
+ value: 22.041
2331
+ - type: precision_at_100
2332
+ value: 7.3469999999999995
2333
+ - type: precision_at_1000
2334
+ value: 1.484
2335
+ - type: precision_at_3
2336
+ value: 29.932
2337
+ - type: precision_at_5
2338
+ value: 26.531
2339
+ - type: recall_at_1
2340
+ value: 2.29
2341
+ - type: recall_at_10
2342
+ value: 15.895999999999999
2343
+ - type: recall_at_100
2344
+ value: 45.518
2345
+ - type: recall_at_1000
2346
+ value: 80.731
2347
+ - type: recall_at_3
2348
+ value: 6.433
2349
+ - type: recall_at_5
2350
+ value: 9.484
2351
+ - task:
2352
+ type: Classification
2353
+ dataset:
2354
+ name: MTEB ToxicConversationsClassification
2355
+ type: mteb/toxic_conversations_50k
2356
+ config: default
2357
+ split: test
2358
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2359
+ metrics:
2360
+ - type: accuracy
2361
+ value: 71.4178
2362
+ - type: ap
2363
+ value: 14.575240629602373
2364
+ - type: f1
2365
+ value: 55.02449563229096
2366
+ - task:
2367
+ type: Classification
2368
+ dataset:
2369
+ name: MTEB TweetSentimentExtractionClassification
2370
+ type: mteb/tweet_sentiment_extraction
2371
+ config: default
2372
+ split: test
2373
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2374
+ metrics:
2375
+ - type: accuracy
2376
+ value: 60.00282965478212
2377
+ - type: f1
2378
+ value: 60.34413028768773
2379
+ - task:
2380
+ type: Clustering
2381
+ dataset:
2382
+ name: MTEB TwentyNewsgroupsClustering
2383
+ type: mteb/twentynewsgroups-clustering
2384
+ config: default
2385
+ split: test
2386
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2387
+ metrics:
2388
+ - type: v_measure
2389
+ value: 50.409448342549936
2390
+ - task:
2391
+ type: PairClassification
2392
+ dataset:
2393
+ name: MTEB TwitterSemEval2015
2394
+ type: mteb/twittersemeval2015-pairclassification
2395
+ config: default
2396
+ split: test
2397
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2398
+ metrics:
2399
+ - type: cos_sim_accuracy
2400
+ value: 87.62591643321214
2401
+ - type: cos_sim_ap
2402
+ value: 79.28766491329633
2403
+ - type: cos_sim_f1
2404
+ value: 71.98772064466617
2405
+ - type: cos_sim_precision
2406
+ value: 69.8609731876862
2407
+ - type: cos_sim_recall
2408
+ value: 74.24802110817942
2409
+ - type: dot_accuracy
2410
+ value: 84.75293556654945
2411
+ - type: dot_ap
2412
+ value: 69.72705761174353
2413
+ - type: dot_f1
2414
+ value: 65.08692852543464
2415
+ - type: dot_precision
2416
+ value: 63.57232704402516
2417
+ - type: dot_recall
2418
+ value: 66.6754617414248
2419
+ - type: euclidean_accuracy
2420
+ value: 87.44710019669786
2421
+ - type: euclidean_ap
2422
+ value: 79.11021477292638
2423
+ - type: euclidean_f1
2424
+ value: 71.5052389470994
2425
+ - type: euclidean_precision
2426
+ value: 69.32606541129832
2427
+ - type: euclidean_recall
2428
+ value: 73.82585751978891
2429
+ - type: manhattan_accuracy
2430
+ value: 87.42325803182929
2431
+ - type: manhattan_ap
2432
+ value: 79.05094494327616
2433
+ - type: manhattan_f1
2434
+ value: 71.36333985649055
2435
+ - type: manhattan_precision
2436
+ value: 70.58064516129032
2437
+ - type: manhattan_recall
2438
+ value: 72.16358839050132
2439
+ - type: max_accuracy
2440
+ value: 87.62591643321214
2441
+ - type: max_ap
2442
+ value: 79.28766491329633
2443
+ - type: max_f1
2444
+ value: 71.98772064466617
2445
+ - task:
2446
+ type: PairClassification
2447
+ dataset:
2448
+ name: MTEB TwitterURLCorpus
2449
+ type: mteb/twitterurlcorpus-pairclassification
2450
+ config: default
2451
+ split: test
2452
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2453
+ metrics:
2454
+ - type: cos_sim_accuracy
2455
+ value: 88.85202002561415
2456
+ - type: cos_sim_ap
2457
+ value: 85.9835303311168
2458
+ - type: cos_sim_f1
2459
+ value: 78.25741142443962
2460
+ - type: cos_sim_precision
2461
+ value: 73.76635768811342
2462
+ - type: cos_sim_recall
2463
+ value: 83.3307668617185
2464
+ - type: dot_accuracy
2465
+ value: 88.20584468506229
2466
+ - type: dot_ap
2467
+ value: 83.591632302697
2468
+ - type: dot_f1
2469
+ value: 76.81739705396173
2470
+ - type: dot_precision
2471
+ value: 73.45275728837373
2472
+ - type: dot_recall
2473
+ value: 80.50508161379734
2474
+ - type: euclidean_accuracy
2475
+ value: 88.64633057787093
2476
+ - type: euclidean_ap
2477
+ value: 85.25705123182283
2478
+ - type: euclidean_f1
2479
+ value: 77.18535726329199
2480
+ - type: euclidean_precision
2481
+ value: 75.17699437997226
2482
+ - type: euclidean_recall
2483
+ value: 79.30397289805975
2484
+ - type: manhattan_accuracy
2485
+ value: 88.63274731245392
2486
+ - type: manhattan_ap
2487
+ value: 85.2376825633018
2488
+ - type: manhattan_f1
2489
+ value: 77.15810785937788
2490
+ - type: manhattan_precision
2491
+ value: 73.92255061014319
2492
+ - type: manhattan_recall
2493
+ value: 80.68986757006468
2494
+ - type: max_accuracy
2495
+ value: 88.85202002561415
2496
+ - type: max_ap
2497
+ value: 85.9835303311168
2498
+ - type: max_f1
2499
+ value: 78.25741142443962
2500
+ ---
2501
+
2502
+ # Santyyy/ember-v1-Q8_0-GGUF
2503
+ This model was converted to GGUF format from [`llmrails/ember-v1`](https://huggingface.co/llmrails/ember-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2504
+ Refer to the [original model card](https://huggingface.co/llmrails/ember-v1) for more details on the model.
2505
+
2506
+ ## Use with llama.cpp
2507
+ Install llama.cpp through brew (works on Mac and Linux)
2508
+
2509
+ ```bash
2510
+ brew install llama.cpp
2511
+
2512
+ ```
2513
+ Invoke the llama.cpp server or the CLI.
2514
+
2515
+ ### CLI:
2516
+ ```bash
2517
+ llama-cli --hf-repo Santyyy/ember-v1-Q8_0-GGUF --hf-file ember-v1-q8_0.gguf -p "The meaning to life and the universe is"
2518
+ ```
2519
+
2520
+ ### Server:
2521
+ ```bash
2522
+ llama-server --hf-repo Santyyy/ember-v1-Q8_0-GGUF --hf-file ember-v1-q8_0.gguf -c 2048
2523
+ ```
2524
+
2525
+ 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.
2526
+
2527
+ Step 1: Clone llama.cpp from GitHub.
2528
+ ```
2529
+ git clone https://github.com/ggerganov/llama.cpp
2530
+ ```
2531
+
2532
+ 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).
2533
+ ```
2534
+ cd llama.cpp && LLAMA_CURL=1 make
2535
+ ```
2536
+
2537
+ Step 3: Run inference through the main binary.
2538
+ ```
2539
+ ./llama-cli --hf-repo Santyyy/ember-v1-Q8_0-GGUF --hf-file ember-v1-q8_0.gguf -p "The meaning to life and the universe is"
2540
+ ```
2541
+ or
2542
+ ```
2543
+ ./llama-server --hf-repo Santyyy/ember-v1-Q8_0-GGUF --hf-file ember-v1-q8_0.gguf -c 2048
2544
+ ```