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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: infgrad/stella-base-en-v2
3
+ language:
4
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5
+ license: mit
6
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7
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8
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9
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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
+ - name: stella-base-en-v2
15
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16
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17
+ type: Classification
18
+ dataset:
19
+ name: MTEB AmazonCounterfactualClassification (en)
20
+ type: mteb/amazon_counterfactual
21
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22
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23
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24
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25
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27
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29
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30
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31
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32
+ type: Classification
33
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34
+ name: MTEB AmazonPolarityClassification
35
+ type: mteb/amazon_polarity
36
+ config: default
37
+ split: test
38
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
39
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40
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41
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42
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43
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44
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45
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46
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48
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49
+ name: MTEB AmazonReviewsClassification (en)
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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56
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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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63
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64
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65
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66
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67
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68
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69
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70
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138
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140
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142
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144
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146
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147
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148
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149
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150
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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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157
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158
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161
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162
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164
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165
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166
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167
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169
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171
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173
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185
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186
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187
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188
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190
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191
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195
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197
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199
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200
+ name: MTEB BiorxivClusteringP2P
201
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202
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203
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204
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205
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206
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209
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210
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211
+ name: MTEB BiorxivClusteringS2S
212
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213
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214
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215
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216
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217
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218
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220
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221
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222
+ name: MTEB CQADupstackAndroidRetrieval
223
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224
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225
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226
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227
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228
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+ - type: mrr
2195
+ value: 54.64886522790935
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: 30.852448373051395
2207
+ - type: cos_sim_spearman
2208
+ value: 32.51821499493775
2209
+ - type: dot_pearson
2210
+ value: 30.390650062190456
2211
+ - type: dot_spearman
2212
+ value: 30.588836159667636
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: 0.198
2224
+ - type: map_at_10
2225
+ value: 1.51
2226
+ - type: map_at_100
2227
+ value: 8.882
2228
+ - type: map_at_1000
2229
+ value: 22.181
2230
+ - type: map_at_3
2231
+ value: 0.553
2232
+ - type: map_at_5
2233
+ value: 0.843
2234
+ - type: mrr_at_1
2235
+ value: 74.0
2236
+ - type: mrr_at_10
2237
+ value: 84.89999999999999
2238
+ - type: mrr_at_100
2239
+ value: 84.89999999999999
2240
+ - type: mrr_at_1000
2241
+ value: 84.89999999999999
2242
+ - type: mrr_at_3
2243
+ value: 84.0
2244
+ - type: mrr_at_5
2245
+ value: 84.89999999999999
2246
+ - type: ndcg_at_1
2247
+ value: 68.0
2248
+ - type: ndcg_at_10
2249
+ value: 64.792
2250
+ - type: ndcg_at_100
2251
+ value: 51.37199999999999
2252
+ - type: ndcg_at_1000
2253
+ value: 47.392
2254
+ - type: ndcg_at_3
2255
+ value: 68.46900000000001
2256
+ - type: ndcg_at_5
2257
+ value: 67.084
2258
+ - type: precision_at_1
2259
+ value: 74.0
2260
+ - type: precision_at_10
2261
+ value: 69.39999999999999
2262
+ - type: precision_at_100
2263
+ value: 53.080000000000005
2264
+ - type: precision_at_1000
2265
+ value: 21.258
2266
+ - type: precision_at_3
2267
+ value: 76.0
2268
+ - type: precision_at_5
2269
+ value: 73.2
2270
+ - type: recall_at_1
2271
+ value: 0.198
2272
+ - type: recall_at_10
2273
+ value: 1.7950000000000002
2274
+ - type: recall_at_100
2275
+ value: 12.626999999999999
2276
+ - type: recall_at_1000
2277
+ value: 44.84
2278
+ - type: recall_at_3
2279
+ value: 0.611
2280
+ - type: recall_at_5
2281
+ value: 0.959
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: 1.4949999999999999
2293
+ - type: map_at_10
2294
+ value: 8.797
2295
+ - type: map_at_100
2296
+ value: 14.889
2297
+ - type: map_at_1000
2298
+ value: 16.309
2299
+ - type: map_at_3
2300
+ value: 4.389
2301
+ - type: map_at_5
2302
+ value: 6.776
2303
+ - type: mrr_at_1
2304
+ value: 18.367
2305
+ - type: mrr_at_10
2306
+ value: 35.844
2307
+ - type: mrr_at_100
2308
+ value: 37.119
2309
+ - type: mrr_at_1000
2310
+ value: 37.119
2311
+ - type: mrr_at_3
2312
+ value: 30.612000000000002
2313
+ - type: mrr_at_5
2314
+ value: 33.163
2315
+ - type: ndcg_at_1
2316
+ value: 16.326999999999998
2317
+ - type: ndcg_at_10
2318
+ value: 21.9
2319
+ - type: ndcg_at_100
2320
+ value: 34.705000000000005
2321
+ - type: ndcg_at_1000
2322
+ value: 45.709
2323
+ - type: ndcg_at_3
2324
+ value: 22.7
2325
+ - type: ndcg_at_5
2326
+ value: 23.197000000000003
2327
+ - type: precision_at_1
2328
+ value: 18.367
2329
+ - type: precision_at_10
2330
+ value: 21.02
2331
+ - type: precision_at_100
2332
+ value: 7.714
2333
+ - type: precision_at_1000
2334
+ value: 1.504
2335
+ - type: precision_at_3
2336
+ value: 26.531
2337
+ - type: precision_at_5
2338
+ value: 26.122
2339
+ - type: recall_at_1
2340
+ value: 1.4949999999999999
2341
+ - type: recall_at_10
2342
+ value: 15.504000000000001
2343
+ - type: recall_at_100
2344
+ value: 47.978
2345
+ - type: recall_at_1000
2346
+ value: 81.56
2347
+ - type: recall_at_3
2348
+ value: 5.569
2349
+ - type: recall_at_5
2350
+ value: 9.821
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: 72.99279999999999
2362
+ - type: ap
2363
+ value: 15.459189680101492
2364
+ - type: f1
2365
+ value: 56.33023271441895
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: 63.070175438596486
2377
+ - type: f1
2378
+ value: 63.28070758709465
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: 50.076231309703054
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: 87.21463908922931
2401
+ - type: cos_sim_ap
2402
+ value: 77.67287017966282
2403
+ - type: cos_sim_f1
2404
+ value: 70.34412955465588
2405
+ - type: cos_sim_precision
2406
+ value: 67.57413709285368
2407
+ - type: cos_sim_recall
2408
+ value: 73.35092348284961
2409
+ - type: dot_accuracy
2410
+ value: 85.04500208618943
2411
+ - type: dot_ap
2412
+ value: 70.4075203869744
2413
+ - type: dot_f1
2414
+ value: 66.18172537008678
2415
+ - type: dot_precision
2416
+ value: 64.08798813643104
2417
+ - type: dot_recall
2418
+ value: 68.41688654353561
2419
+ - type: euclidean_accuracy
2420
+ value: 87.17887584192646
2421
+ - type: euclidean_ap
2422
+ value: 77.5774128274464
2423
+ - type: euclidean_f1
2424
+ value: 70.09307972480777
2425
+ - type: euclidean_precision
2426
+ value: 71.70852884349986
2427
+ - type: euclidean_recall
2428
+ value: 68.54881266490766
2429
+ - type: manhattan_accuracy
2430
+ value: 87.28020504261787
2431
+ - type: manhattan_ap
2432
+ value: 77.57835820297892
2433
+ - type: manhattan_f1
2434
+ value: 70.23063591521131
2435
+ - type: manhattan_precision
2436
+ value: 70.97817299919159
2437
+ - type: manhattan_recall
2438
+ value: 69.49868073878628
2439
+ - type: max_accuracy
2440
+ value: 87.28020504261787
2441
+ - type: max_ap
2442
+ value: 77.67287017966282
2443
+ - type: max_f1
2444
+ value: 70.34412955465588
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: 88.96650754841464
2456
+ - type: cos_sim_ap
2457
+ value: 86.00185968965064
2458
+ - type: cos_sim_f1
2459
+ value: 77.95861256351718
2460
+ - type: cos_sim_precision
2461
+ value: 74.70712773465067
2462
+ - type: cos_sim_recall
2463
+ value: 81.50600554357868
2464
+ - type: dot_accuracy
2465
+ value: 87.36950362867233
2466
+ - type: dot_ap
2467
+ value: 82.22071181147555
2468
+ - type: dot_f1
2469
+ value: 74.85680716698488
2470
+ - type: dot_precision
2471
+ value: 71.54688377316114
2472
+ - type: dot_recall
2473
+ value: 78.48783492454572
2474
+ - type: euclidean_accuracy
2475
+ value: 88.99561454573679
2476
+ - type: euclidean_ap
2477
+ value: 86.15882097229648
2478
+ - type: euclidean_f1
2479
+ value: 78.18463125322332
2480
+ - type: euclidean_precision
2481
+ value: 74.95408956067241
2482
+ - type: euclidean_recall
2483
+ value: 81.70619032953496
2484
+ - type: manhattan_accuracy
2485
+ value: 88.96650754841464
2486
+ - type: manhattan_ap
2487
+ value: 86.13133111232099
2488
+ - type: manhattan_f1
2489
+ value: 78.10771470160115
2490
+ - type: manhattan_precision
2491
+ value: 74.05465084184377
2492
+ - type: manhattan_recall
2493
+ value: 82.63012011087157
2494
+ - type: max_accuracy
2495
+ value: 88.99561454573679
2496
+ - type: max_ap
2497
+ value: 86.15882097229648
2498
+ - type: max_f1
2499
+ value: 78.18463125322332
2500
+ ---
2501
+
2502
+ # djuna/stella-base-en-v2-Q5_K_M-GGUF
2503
+ This model was converted to GGUF format from [`infgrad/stella-base-en-v2`](https://huggingface.co/infgrad/stella-base-en-v2) 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/infgrad/stella-base-en-v2) 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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
2518
+ ```
2519
+
2520
+ ### Server:
2521
+ ```bash
2522
+ llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
2540
+ ```
2541
+ or
2542
+ ```
2543
+ ./llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -c 2048
2544
+ ```