--- base_model: BeastyZ/e5-R-mistral-7b datasets: - BeastyZ/E5-R language: - en library_name: transformers license: apache-2.0 tags: - mteb - llama-cpp - gguf-my-repo model-index: - name: e5-R-mistral-7b results: - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: None metrics: - type: map_at_1 value: 33.57 - type: map_at_10 value: 49.952000000000005 - type: map_at_100 value: 50.673 - type: map_at_1000 value: 50.674 - type: map_at_3 value: 44.915 - type: map_at_5 value: 47.876999999999995 - type: mrr_at_1 value: 34.211000000000006 - type: mrr_at_10 value: 50.19 - type: mrr_at_100 value: 50.905 - type: mrr_at_1000 value: 50.906 - type: mrr_at_3 value: 45.128 - type: mrr_at_5 value: 48.097 - type: ndcg_at_1 value: 33.57 - type: ndcg_at_10 value: 58.994 - type: ndcg_at_100 value: 61.806000000000004 - type: ndcg_at_1000 value: 61.824999999999996 - type: ndcg_at_3 value: 48.681000000000004 - type: ndcg_at_5 value: 54.001 - type: precision_at_1 value: 33.57 - type: precision_at_10 value: 8.784 - type: precision_at_100 value: 0.9950000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.867 - type: precision_at_5 value: 14.495 - type: recall_at_1 value: 33.57 - type: recall_at_10 value: 87.83800000000001 - type: recall_at_100 value: 99.502 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 59.602 - type: recall_at_5 value: 72.475 - type: main_score value: 58.994 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 24.75 - type: map_at_10 value: 34.025 - type: map_at_100 value: 35.126000000000005 - type: map_at_1000 value: 35.219 - type: map_at_3 value: 31.607000000000003 - type: map_at_5 value: 32.962 - type: mrr_at_1 value: 27.357 - type: mrr_at_10 value: 36.370999999999995 - type: mrr_at_100 value: 37.364000000000004 - type: mrr_at_1000 value: 37.423 - type: mrr_at_3 value: 34.288000000000004 - type: mrr_at_5 value: 35.434 - type: ndcg_at_1 value: 27.357 - type: ndcg_at_10 value: 46.593999999999994 - type: ndcg_at_100 value: 44.317 - type: ndcg_at_1000 value: 46.475 - type: ndcg_at_3 value: 34.473 - type: ndcg_at_5 value: 36.561 - type: precision_at_1 value: 27.357 - type: precision_at_10 value: 6.081 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 14.911 - type: precision_at_5 value: 10.24 - type: recall_at_1 value: 24.75 - type: recall_at_10 value: 51.856 - type: recall_at_100 value: 76.44300000000001 - type: recall_at_1000 value: 92.078 - type: recall_at_3 value: 39.427 - type: recall_at_5 value: 44.639 - type: main_score value: 46.593999999999994 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 16.436 - type: map_at_10 value: 29.693 - type: map_at_100 value: 32.179 - type: map_at_1000 value: 32.353 - type: map_at_3 value: 24.556 - type: map_at_5 value: 27.105 - type: mrr_at_1 value: 37.524 - type: mrr_at_10 value: 51.475 - type: mrr_at_100 value: 52.107000000000006 - type: mrr_at_1000 value: 52.123 - type: mrr_at_3 value: 48.35 - type: mrr_at_5 value: 50.249 - type: ndcg_at_1 value: 37.524 - type: ndcg_at_10 value: 40.258 - type: ndcg_at_100 value: 48.364000000000004 - type: ndcg_at_1000 value: 51.031000000000006 - type: ndcg_at_3 value: 33.359 - type: ndcg_at_5 value: 35.573 - type: precision_at_1 value: 37.524 - type: precision_at_10 value: 12.886000000000001 - type: precision_at_100 value: 2.169 - type: precision_at_1000 value: 0.268 - type: precision_at_3 value: 25.624000000000002 - type: precision_at_5 value: 19.453 - type: recall_at_1 value: 16.436 - type: recall_at_10 value: 47.77 - type: recall_at_100 value: 74.762 - type: recall_at_1000 value: 89.316 - type: recall_at_3 value: 30.508000000000003 - type: recall_at_5 value: 37.346000000000004 - type: main_score value: 40.258 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: None metrics: - type: map_at_1 value: 10.147 - type: map_at_10 value: 24.631 - type: map_at_100 value: 35.657 - type: map_at_1000 value: 37.824999999999996 - type: map_at_3 value: 16.423 - type: map_at_5 value: 19.666 - type: mrr_at_1 value: 76.5 - type: mrr_at_10 value: 82.793 - type: mrr_at_100 value: 83.015 - type: mrr_at_1000 value: 83.021 - type: mrr_at_3 value: 81.75 - type: mrr_at_5 value: 82.375 - type: ndcg_at_1 value: 64.75 - type: ndcg_at_10 value: 51.031000000000006 - type: ndcg_at_100 value: 56.005 - type: ndcg_at_1000 value: 63.068000000000005 - type: ndcg_at_3 value: 54.571999999999996 - type: ndcg_at_5 value: 52.66499999999999 - type: precision_at_1 value: 76.5 - type: precision_at_10 value: 42.15 - type: precision_at_100 value: 13.22 - type: precision_at_1000 value: 2.5989999999999998 - type: precision_at_3 value: 58.416999999999994 - type: precision_at_5 value: 52.2 - type: recall_at_1 value: 10.147 - type: recall_at_10 value: 30.786 - type: recall_at_100 value: 62.873000000000005 - type: recall_at_1000 value: 85.358 - type: recall_at_3 value: 17.665 - type: recall_at_5 value: 22.088 - type: main_score value: 51.031000000000006 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: None metrics: - type: map_at_1 value: 78.52900000000001 - type: map_at_10 value: 87.24199999999999 - type: map_at_100 value: 87.446 - type: map_at_1000 value: 87.457 - type: map_at_3 value: 86.193 - type: map_at_5 value: 86.898 - type: mrr_at_1 value: 84.518 - type: mrr_at_10 value: 90.686 - type: mrr_at_100 value: 90.73 - type: mrr_at_1000 value: 90.731 - type: mrr_at_3 value: 90.227 - type: mrr_at_5 value: 90.575 - type: ndcg_at_1 value: 84.518 - type: ndcg_at_10 value: 90.324 - type: ndcg_at_100 value: 90.96300000000001 - type: ndcg_at_1000 value: 91.134 - type: ndcg_at_3 value: 88.937 - type: ndcg_at_5 value: 89.788 - type: precision_at_1 value: 84.518 - type: precision_at_10 value: 10.872 - type: precision_at_100 value: 1.1440000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 34.108 - type: precision_at_5 value: 21.154999999999998 - type: recall_at_1 value: 78.52900000000001 - type: recall_at_10 value: 96.123 - type: recall_at_100 value: 98.503 - type: recall_at_1000 value: 99.518 - type: recall_at_3 value: 92.444 - type: recall_at_5 value: 94.609 - type: main_score value: 90.324 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 29.38 - type: map_at_10 value: 50.28 - type: map_at_100 value: 52.532999999999994 - type: map_at_1000 value: 52.641000000000005 - type: map_at_3 value: 43.556 - type: map_at_5 value: 47.617 - type: mrr_at_1 value: 56.79 - type: mrr_at_10 value: 65.666 - type: mrr_at_100 value: 66.211 - type: mrr_at_1000 value: 66.226 - type: mrr_at_3 value: 63.452 - type: mrr_at_5 value: 64.895 - type: ndcg_at_1 value: 56.79 - type: ndcg_at_10 value: 58.68 - type: ndcg_at_100 value: 65.22 - type: ndcg_at_1000 value: 66.645 - type: ndcg_at_3 value: 53.981 - type: ndcg_at_5 value: 55.95 - type: precision_at_1 value: 56.79 - type: precision_at_10 value: 16.311999999999998 - type: precision_at_100 value: 2.316 - type: precision_at_1000 value: 0.258 - type: precision_at_3 value: 36.214 - type: precision_at_5 value: 27.067999999999998 - type: recall_at_1 value: 29.38 - type: recall_at_10 value: 66.503 - type: recall_at_100 value: 89.885 - type: recall_at_1000 value: 97.954 - type: recall_at_3 value: 48.866 - type: recall_at_5 value: 57.60999999999999 - type: main_score value: 58.68 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 42.134 - type: map_at_10 value: 73.412 - type: map_at_100 value: 74.144 - type: map_at_1000 value: 74.181 - type: map_at_3 value: 70.016 - type: map_at_5 value: 72.174 - type: mrr_at_1 value: 84.267 - type: mrr_at_10 value: 89.18599999999999 - type: mrr_at_100 value: 89.29599999999999 - type: mrr_at_1000 value: 89.298 - type: mrr_at_3 value: 88.616 - type: mrr_at_5 value: 88.957 - type: ndcg_at_1 value: 84.267 - type: ndcg_at_10 value: 80.164 - type: ndcg_at_100 value: 82.52199999999999 - type: ndcg_at_1000 value: 83.176 - type: ndcg_at_3 value: 75.616 - type: ndcg_at_5 value: 78.184 - type: precision_at_1 value: 84.267 - type: precision_at_10 value: 16.916 - type: precision_at_100 value: 1.872 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 49.71 - type: precision_at_5 value: 31.854 - type: recall_at_1 value: 42.134 - type: recall_at_10 value: 84.578 - type: recall_at_100 value: 93.606 - type: recall_at_1000 value: 97.86 - type: recall_at_3 value: 74.564 - type: recall_at_5 value: 79.635 - type: main_score value: 80.164 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 22.276 - type: map_at_10 value: 35.493 - type: map_at_100 value: 36.656 - type: map_at_1000 value: 36.699 - type: map_at_3 value: 31.320999999999998 - type: map_at_5 value: 33.772999999999996 - type: mrr_at_1 value: 22.966 - type: mrr_at_10 value: 36.074 - type: mrr_at_100 value: 37.183 - type: mrr_at_1000 value: 37.219 - type: mrr_at_3 value: 31.984 - type: mrr_at_5 value: 34.419 - type: ndcg_at_1 value: 22.966 - type: ndcg_at_10 value: 42.895 - type: ndcg_at_100 value: 48.453 - type: ndcg_at_1000 value: 49.464999999999996 - type: ndcg_at_3 value: 34.410000000000004 - type: ndcg_at_5 value: 38.78 - type: precision_at_1 value: 22.966 - type: precision_at_10 value: 6.88 - type: precision_at_100 value: 0.966 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.785 - type: precision_at_5 value: 11.074 - type: recall_at_1 value: 22.276 - type: recall_at_10 value: 65.756 - type: recall_at_100 value: 91.34100000000001 - type: recall_at_1000 value: 98.957 - type: recall_at_3 value: 42.67 - type: recall_at_5 value: 53.161 - type: main_score value: 42.895 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.188999999999999 - type: map_at_10 value: 16.176 - type: map_at_100 value: 20.504 - type: map_at_1000 value: 22.203999999999997 - type: map_at_3 value: 11.766 - type: map_at_5 value: 13.655999999999999 - type: mrr_at_1 value: 55.418 - type: mrr_at_10 value: 62.791 - type: mrr_at_100 value: 63.339 - type: mrr_at_1000 value: 63.369 - type: mrr_at_3 value: 60.99099999999999 - type: mrr_at_5 value: 62.059 - type: ndcg_at_1 value: 53.715 - type: ndcg_at_10 value: 41.377 - type: ndcg_at_100 value: 37.999 - type: ndcg_at_1000 value: 46.726 - type: ndcg_at_3 value: 47.262 - type: ndcg_at_5 value: 44.708999999999996 - type: precision_at_1 value: 55.108000000000004 - type: precision_at_10 value: 30.154999999999998 - type: precision_at_100 value: 9.582 - type: precision_at_1000 value: 2.2720000000000002 - type: precision_at_3 value: 43.55 - type: precision_at_5 value: 38.204 - type: recall_at_1 value: 7.188999999999999 - type: recall_at_10 value: 20.655 - type: recall_at_100 value: 38.068000000000005 - type: recall_at_1000 value: 70.208 - type: recall_at_3 value: 12.601 - type: recall_at_5 value: 15.573999999999998 - type: main_score value: 41.377 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: None metrics: - type: map_at_1 value: 46.017 - type: map_at_10 value: 62.910999999999994 - type: map_at_100 value: 63.526 - type: map_at_1000 value: 63.536 - type: map_at_3 value: 59.077999999999996 - type: map_at_5 value: 61.521 - type: mrr_at_1 value: 51.68000000000001 - type: mrr_at_10 value: 65.149 - type: mrr_at_100 value: 65.542 - type: mrr_at_1000 value: 65.55 - type: mrr_at_3 value: 62.49 - type: mrr_at_5 value: 64.178 - type: ndcg_at_1 value: 51.651 - type: ndcg_at_10 value: 69.83500000000001 - type: ndcg_at_100 value: 72.18 - type: ndcg_at_1000 value: 72.393 - type: ndcg_at_3 value: 63.168 - type: ndcg_at_5 value: 66.958 - type: precision_at_1 value: 51.651 - type: precision_at_10 value: 10.626 - type: precision_at_100 value: 1.195 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 28.012999999999998 - type: precision_at_5 value: 19.09 - type: recall_at_1 value: 46.017 - type: recall_at_10 value: 88.345 - type: recall_at_100 value: 98.129 - type: recall_at_1000 value: 99.696 - type: recall_at_3 value: 71.531 - type: recall_at_5 value: 80.108 - type: main_score value: 69.83500000000001 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics: - type: map_at_1 value: 72.473 - type: map_at_10 value: 86.72800000000001 - type: map_at_100 value: 87.323 - type: map_at_1000 value: 87.332 - type: map_at_3 value: 83.753 - type: map_at_5 value: 85.627 - type: mrr_at_1 value: 83.39 - type: mrr_at_10 value: 89.149 - type: mrr_at_100 value: 89.228 - type: mrr_at_1000 value: 89.229 - type: mrr_at_3 value: 88.335 - type: mrr_at_5 value: 88.895 - type: ndcg_at_1 value: 83.39 - type: ndcg_at_10 value: 90.109 - type: ndcg_at_100 value: 91.09 - type: ndcg_at_1000 value: 91.13900000000001 - type: ndcg_at_3 value: 87.483 - type: ndcg_at_5 value: 88.942 - type: precision_at_1 value: 83.39 - type: precision_at_10 value: 13.711 - type: precision_at_100 value: 1.549 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.342999999999996 - type: precision_at_5 value: 25.188 - type: recall_at_1 value: 72.473 - type: recall_at_10 value: 96.57 - type: recall_at_100 value: 99.792 - type: recall_at_1000 value: 99.99900000000001 - type: recall_at_3 value: 88.979 - type: recall_at_5 value: 93.163 - type: main_score value: 90.109 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.598 - type: map_at_10 value: 11.405999999999999 - type: map_at_100 value: 13.447999999999999 - type: map_at_1000 value: 13.758999999999999 - type: map_at_3 value: 8.332 - type: map_at_5 value: 9.709 - type: mrr_at_1 value: 22.6 - type: mrr_at_10 value: 32.978 - type: mrr_at_100 value: 34.149 - type: mrr_at_1000 value: 34.213 - type: mrr_at_3 value: 29.7 - type: mrr_at_5 value: 31.485000000000003 - type: ndcg_at_1 value: 22.6 - type: ndcg_at_10 value: 19.259999999999998 - type: ndcg_at_100 value: 27.21 - type: ndcg_at_1000 value: 32.7 - type: ndcg_at_3 value: 18.445 - type: ndcg_at_5 value: 15.812000000000001 - type: precision_at_1 value: 22.6 - type: precision_at_10 value: 9.959999999999999 - type: precision_at_100 value: 2.139 - type: precision_at_1000 value: 0.345 - type: precision_at_3 value: 17.299999999999997 - type: precision_at_5 value: 13.719999999999999 - type: recall_at_1 value: 4.598 - type: recall_at_10 value: 20.186999999999998 - type: recall_at_100 value: 43.362 - type: recall_at_1000 value: 70.11800000000001 - type: recall_at_3 value: 10.543 - type: recall_at_5 value: 13.923 - type: main_score value: 19.259999999999998 - task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: None metrics: - type: map_at_1 value: 65.467 - type: map_at_10 value: 74.935 - type: map_at_100 value: 75.395 - type: map_at_1000 value: 75.412 - type: map_at_3 value: 72.436 - type: map_at_5 value: 73.978 - type: mrr_at_1 value: 68.667 - type: mrr_at_10 value: 76.236 - type: mrr_at_100 value: 76.537 - type: mrr_at_1000 value: 76.55499999999999 - type: mrr_at_3 value: 74.722 - type: mrr_at_5 value: 75.639 - type: ndcg_at_1 value: 68.667 - type: ndcg_at_10 value: 78.92099999999999 - type: ndcg_at_100 value: 80.645 - type: ndcg_at_1000 value: 81.045 - type: ndcg_at_3 value: 75.19500000000001 - type: ndcg_at_5 value: 77.114 - type: precision_at_1 value: 68.667 - type: precision_at_10 value: 10.133000000000001 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.889 - type: precision_at_5 value: 18.8 - type: recall_at_1 value: 65.467 - type: recall_at_10 value: 89.517 - type: recall_at_100 value: 97 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 79.72200000000001 - type: recall_at_5 value: 84.511 - type: main_score value: 78.92099999999999 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: mteb/trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.244 - type: map_at_10 value: 2.183 - type: map_at_100 value: 13.712 - type: map_at_1000 value: 33.147 - type: map_at_3 value: 0.7270000000000001 - type: map_at_5 value: 1.199 - type: mrr_at_1 value: 94 - type: mrr_at_10 value: 97 - type: mrr_at_100 value: 97 - type: mrr_at_1000 value: 97 - type: mrr_at_3 value: 97 - type: mrr_at_5 value: 97 - type: ndcg_at_1 value: 92 - type: ndcg_at_10 value: 84.399 - type: ndcg_at_100 value: 66.771 - type: ndcg_at_1000 value: 59.092 - type: ndcg_at_3 value: 89.173 - type: ndcg_at_5 value: 88.52600000000001 - type: precision_at_1 value: 94 - type: precision_at_10 value: 86.8 - type: precision_at_100 value: 68.24 - type: precision_at_1000 value: 26.003999999999998 - type: precision_at_3 value: 92.667 - type: precision_at_5 value: 92.4 - type: recall_at_1 value: 0.244 - type: recall_at_10 value: 2.302 - type: recall_at_100 value: 16.622 - type: recall_at_1000 value: 55.175 - type: recall_at_3 value: 0.748 - type: recall_at_5 value: 1.247 - type: main_score value: 84.399 - task: type: Retrieval dataset: name: MTEB Touche2020 type: mteb/touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.707 - type: map_at_10 value: 10.917 - type: map_at_100 value: 16.308 - type: map_at_1000 value: 17.953 - type: map_at_3 value: 5.65 - type: map_at_5 value: 7.379 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 49.745 - type: mrr_at_100 value: 50.309000000000005 - type: mrr_at_1000 value: 50.32 - type: mrr_at_3 value: 44.897999999999996 - type: mrr_at_5 value: 48.061 - type: ndcg_at_1 value: 33.672999999999995 - type: ndcg_at_10 value: 26.894000000000002 - type: ndcg_at_100 value: 37.423 - type: ndcg_at_1000 value: 49.376999999999995 - type: ndcg_at_3 value: 30.456 - type: ndcg_at_5 value: 27.772000000000002 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 23.878 - type: precision_at_100 value: 7.489999999999999 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 31.293 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 2.707 - type: recall_at_10 value: 18.104 - type: recall_at_100 value: 46.93 - type: recall_at_1000 value: 83.512 - type: recall_at_3 value: 6.622999999999999 - type: recall_at_5 value: 10.051 - type: main_score value: 26.894000000000002 --- # VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF This model was converted to GGUF format from [`BeastyZ/e5-R-mistral-7b`](https://huggingface.co/BeastyZ/e5-R-mistral-7b) 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/BeastyZ/e5-R-mistral-7b) 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 VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-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 VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -c 2048 ```