--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers - llama-cpp - gguf-my-repo language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit base_model: intfloat/multilingual-e5-large model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - type: recall_at_5 value: 67.71000000000001 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.30960650674069 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.427074197498996 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.05942144105269 - type: cos_sim_spearman value: 82.51212105850809 - type: euclidean_pearson value: 81.95639829909122 - type: euclidean_spearman value: 82.3717564144213 - type: manhattan_pearson value: 81.79273425468256 - type: manhattan_spearman value: 82.20066817871039 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.46764091858039 - type: f1 value: 99.37717466945023 - type: precision value: 99.33194154488518 - type: recall value: 99.46764091858039 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.29407880255337 - type: f1 value: 98.11248073959938 - type: precision value: 98.02443319392472 - type: recall value: 98.29407880255337 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.79009352268791 - type: f1 value: 97.5176076665512 - type: precision value: 97.38136473848286 - type: recall value: 97.79009352268791 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.26276987888363 - type: f1 value: 99.20133403545726 - type: precision value: 99.17500438827453 - type: recall value: 99.26276987888363 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.72727272727273 - type: f1 value: 84.67672206031433 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.34220182511161 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 33.4987096128766 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 25.558249999999997 - type: map_at_10 value: 34.44425000000001 - type: map_at_100 value: 35.59833333333333 - type: map_at_1000 value: 35.706916666666665 - type: map_at_3 value: 31.691749999999995 - type: map_at_5 value: 33.252916666666664 - type: mrr_at_1 value: 30.252666666666666 - type: mrr_at_10 value: 38.60675 - type: mrr_at_100 value: 39.42666666666666 - type: mrr_at_1000 value: 39.48408333333334 - type: mrr_at_3 value: 36.17441666666665 - type: mrr_at_5 value: 37.56275 - type: ndcg_at_1 value: 30.252666666666666 - type: ndcg_at_10 value: 39.683 - type: ndcg_at_100 value: 44.68541666666667 - type: ndcg_at_1000 value: 46.94316666666668 - type: ndcg_at_3 value: 34.961749999999995 - type: ndcg_at_5 value: 37.215666666666664 - type: precision_at_1 value: 30.252666666666666 - type: precision_at_10 value: 6.904166666666667 - type: precision_at_100 value: 1.0989999999999995 - type: precision_at_1000 value: 0.14733333333333334 - type: precision_at_3 value: 16.037666666666667 - type: precision_at_5 value: 11.413583333333333 - type: recall_at_1 value: 25.558249999999997 - type: recall_at_10 value: 51.13341666666666 - type: recall_at_100 value: 73.08366666666667 - type: recall_at_1000 value: 88.79483333333334 - type: recall_at_3 value: 37.989083333333326 - type: recall_at_5 value: 43.787833333333325 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 10.338 - type: map_at_10 value: 18.360000000000003 - type: map_at_100 value: 19.942 - type: map_at_1000 value: 20.134 - type: map_at_3 value: 15.174000000000001 - type: map_at_5 value: 16.830000000000002 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 33.768 - type: mrr_at_100 value: 34.707 - type: mrr_at_1000 value: 34.766000000000005 - type: mrr_at_3 value: 30.977 - type: mrr_at_5 value: 32.528 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 25.733 - type: ndcg_at_100 value: 32.288 - type: ndcg_at_1000 value: 35.992000000000004 - type: ndcg_at_3 value: 20.866 - type: ndcg_at_5 value: 22.612 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 8.124 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 15.679000000000002 - type: precision_at_5 value: 12.117 - type: recall_at_1 value: 10.338 - type: recall_at_10 value: 31.154 - type: recall_at_100 value: 54.161 - type: recall_at_1000 value: 75.21900000000001 - type: recall_at_3 value: 19.427 - type: recall_at_5 value: 24.214 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.498 - type: map_at_10 value: 19.103 - type: map_at_100 value: 27.375 - type: map_at_1000 value: 28.981 - type: map_at_3 value: 13.764999999999999 - type: map_at_5 value: 15.950000000000001 - type: mrr_at_1 value: 65.5 - type: mrr_at_10 value: 74.53800000000001 - type: mrr_at_100 value: 74.71799999999999 - type: mrr_at_1000 value: 74.725 - type: mrr_at_3 value: 72.792 - type: mrr_at_5 value: 73.554 - type: ndcg_at_1 value: 53.37499999999999 - type: ndcg_at_10 value: 41.286 - type: ndcg_at_100 value: 45.972 - type: ndcg_at_1000 value: 53.123 - type: ndcg_at_3 value: 46.172999999999995 - type: ndcg_at_5 value: 43.033 - type: precision_at_1 value: 65.5 - type: precision_at_10 value: 32.725 - type: precision_at_100 value: 10.683 - type: precision_at_1000 value: 1.978 - type: precision_at_3 value: 50 - type: precision_at_5 value: 41.349999999999994 - type: recall_at_1 value: 8.498 - type: recall_at_10 value: 25.070999999999998 - type: recall_at_100 value: 52.383 - type: recall_at_1000 value: 74.91499999999999 - type: recall_at_3 value: 15.207999999999998 - type: recall_at_5 value: 18.563 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.5 - type: f1 value: 41.93833713984145 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 67.914 - type: map_at_10 value: 78.10000000000001 - type: map_at_100 value: 78.333 - type: map_at_1000 value: 78.346 - type: map_at_3 value: 76.626 - type: map_at_5 value: 77.627 - type: mrr_at_1 value: 72.74199999999999 - type: mrr_at_10 value: 82.414 - type: mrr_at_100 value: 82.511 - type: mrr_at_1000 value: 82.513 - type: mrr_at_3 value: 81.231 - type: mrr_at_5 value: 82.065 - type: ndcg_at_1 value: 72.74199999999999 - type: ndcg_at_10 value: 82.806 - type: ndcg_at_100 value: 83.677 - type: ndcg_at_1000 value: 83.917 - type: ndcg_at_3 value: 80.305 - type: ndcg_at_5 value: 81.843 - type: precision_at_1 value: 72.74199999999999 - type: precision_at_10 value: 10.24 - type: precision_at_100 value: 1.089 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 31.268 - type: precision_at_5 value: 19.706000000000003 - type: recall_at_1 value: 67.914 - type: recall_at_10 value: 92.889 - type: recall_at_100 value: 96.42699999999999 - type: recall_at_1000 value: 97.92 - type: recall_at_3 value: 86.21 - type: recall_at_5 value: 90.036 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.166 - type: map_at_10 value: 35.57 - type: map_at_100 value: 37.405 - type: map_at_1000 value: 37.564 - type: map_at_3 value: 30.379 - type: map_at_5 value: 33.324 - type: mrr_at_1 value: 43.519000000000005 - type: mrr_at_10 value: 51.556000000000004 - type: mrr_at_100 value: 52.344 - type: mrr_at_1000 value: 52.373999999999995 - type: mrr_at_3 value: 48.868 - type: mrr_at_5 value: 50.319 - type: ndcg_at_1 value: 43.519000000000005 - type: ndcg_at_10 value: 43.803 - type: ndcg_at_100 value: 50.468999999999994 - type: ndcg_at_1000 value: 53.111 - type: ndcg_at_3 value: 38.893 - type: ndcg_at_5 value: 40.653 - type: precision_at_1 value: 43.519000000000005 - type: precision_at_10 value: 12.253 - type: precision_at_100 value: 1.931 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 25.617 - type: precision_at_5 value: 19.383 - type: recall_at_1 value: 22.166 - type: recall_at_10 value: 51.6 - type: recall_at_100 value: 76.574 - type: recall_at_1000 value: 92.192 - type: recall_at_3 value: 34.477999999999994 - type: recall_at_5 value: 41.835 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.041 - type: map_at_10 value: 62.961999999999996 - type: map_at_100 value: 63.79899999999999 - type: map_at_1000 value: 63.854 - type: map_at_3 value: 59.399 - type: map_at_5 value: 61.669 - type: mrr_at_1 value: 78.082 - type: mrr_at_10 value: 84.321 - type: mrr_at_100 value: 84.49600000000001 - type: mrr_at_1000 value: 84.502 - type: mrr_at_3 value: 83.421 - type: mrr_at_5 value: 83.977 - type: ndcg_at_1 value: 78.082 - type: ndcg_at_10 value: 71.229 - type: ndcg_at_100 value: 74.10900000000001 - type: ndcg_at_1000 value: 75.169 - type: ndcg_at_3 value: 66.28699999999999 - type: ndcg_at_5 value: 69.084 - type: precision_at_1 value: 78.082 - type: precision_at_10 value: 14.993 - type: precision_at_100 value: 1.7239999999999998 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 42.737 - type: precision_at_5 value: 27.843 - type: recall_at_1 value: 39.041 - type: recall_at_10 value: 74.96300000000001 - type: recall_at_100 value: 86.199 - type: recall_at_1000 value: 93.228 - type: recall_at_3 value: 64.105 - type: recall_at_5 value: 69.608 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.23160000000001 - type: ap value: 85.5674856808308 - type: f1 value: 90.18033354786317 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 24.091 - type: map_at_10 value: 36.753 - type: map_at_100 value: 37.913000000000004 - type: map_at_1000 value: 37.958999999999996 - type: map_at_3 value: 32.818999999999996 - type: map_at_5 value: 35.171 - type: mrr_at_1 value: 24.742 - type: mrr_at_10 value: 37.285000000000004 - type: mrr_at_100 value: 38.391999999999996 - type: mrr_at_1000 value: 38.431 - type: mrr_at_3 value: 33.440999999999995 - type: mrr_at_5 value: 35.75 - type: ndcg_at_1 value: 24.742 - type: ndcg_at_10 value: 43.698 - type: ndcg_at_100 value: 49.145 - type: ndcg_at_1000 value: 50.23800000000001 - type: ndcg_at_3 value: 35.769 - type: ndcg_at_5 value: 39.961999999999996 - type: precision_at_1 value: 24.742 - type: precision_at_10 value: 6.7989999999999995 - type: precision_at_100 value: 0.95 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 15.096000000000002 - type: precision_at_5 value: 11.183 - type: recall_at_1 value: 24.091 - type: recall_at_10 value: 65.068 - type: recall_at_100 value: 89.899 - type: recall_at_1000 value: 98.16 - type: recall_at_3 value: 43.68 - type: recall_at_5 value: 53.754999999999995 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.66621067031465 - type: f1 value: 93.49622853272142 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (de) type: mteb/mtop_domain config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.94702733164272 - type: f1 value: 91.17043441745282 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (es) type: mteb/mtop_domain config: es split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 92.20146764509674 - type: f1 value: 91.98359080555608 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.99780770435328 - type: f1 value: 89.19746342724068 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (hi) type: mteb/mtop_domain config: hi split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.78486912871998 - type: f1 value: 89.24578823628642 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (th) type: mteb/mtop_domain config: th split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.74502712477394 - type: f1 value: 89.00297573881542 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.9046967624259 - type: f1 value: 59.36787125785957 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (de) type: mteb/mtop_intent config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - 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type: euclidean_ap value: 75.47512772621097 - type: euclidean_f1 value: 69.413872536473 - type: euclidean_precision value: 67.39562624254472 - type: euclidean_recall value: 71.55672823218997 - type: manhattan_accuracy value: 86.52917684925792 - type: manhattan_ap value: 75.34000110496703 - type: manhattan_f1 value: 69.28489190226429 - type: manhattan_precision value: 67.24608889992551 - type: manhattan_recall value: 71.45118733509234 - type: max_accuracy value: 86.60666388508076 - type: max_ap value: 75.47512772621097 - type: max_f1 value: 69.413872536473 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.01695967710637 - type: cos_sim_ap value: 85.8298270742901 - type: cos_sim_f1 value: 78.46988128389272 - type: cos_sim_precision value: 74.86017897091722 - type: cos_sim_recall value: 82.44533415460425 - type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 --- # nnch/multilingual-e5-large-Q4_K_M-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -c 2048 ``` 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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` 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). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo nnch/multilingual-e5-large-Q4_K_M-GGUF --hf-file multilingual-e5-large-q4_k_m.gguf -c 2048 ```