metadata
tags:
- mteb
- llama-cpp
- gguf-my-repo
license: cc-by-nc-4.0
library_name: sentence-transformers
base_model: TencentBAC/Conan-embedding-v1
model-index:
- name: conan-embedding
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 56.613572467148856
- type: cos_sim_spearman
value: 60.66446211824284
- type: euclidean_pearson
value: 58.42080485872613
- type: euclidean_spearman
value: 59.82750030458164
- type: manhattan_pearson
value: 58.39885271199772
- type: manhattan_spearman
value: 59.817749720366734
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 56.60530380552331
- type: cos_sim_spearman
value: 58.63822441736707
- type: euclidean_pearson
value: 62.18551665180664
- type: euclidean_spearman
value: 58.23168804495912
- type: manhattan_pearson
value: 62.17191480770053
- type: manhattan_spearman
value: 58.22556219601401
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.308
- type: f1
value: 46.927458607895126
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 72.6472074172711
- type: cos_sim_spearman
value: 74.50748447236577
- type: euclidean_pearson
value: 72.51833296451854
- type: euclidean_spearman
value: 73.9898922606105
- type: manhattan_pearson
value: 72.50184948939338
- type: manhattan_spearman
value: 73.97797921509638
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 60.63545326048343
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 52.64834762325994
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 91.38528814655234
- type: mrr
value: 93.35857142857144
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 89.72084678877096
- type: mrr
value: 91.74380952380953
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.987
- type: map_at_10
value: 40.675
- type: map_at_100
value: 42.495
- type: map_at_1000
value: 42.596000000000004
- type: map_at_3
value: 36.195
- type: map_at_5
value: 38.704
- type: mrr_at_1
value: 41.21
- type: mrr_at_10
value: 49.816
- type: mrr_at_100
value: 50.743
- type: mrr_at_1000
value: 50.77700000000001
- type: mrr_at_3
value: 47.312
- type: mrr_at_5
value: 48.699999999999996
- type: ndcg_at_1
value: 41.21
- type: ndcg_at_10
value: 47.606
- type: ndcg_at_100
value: 54.457
- type: ndcg_at_1000
value: 56.16100000000001
- type: ndcg_at_3
value: 42.108000000000004
- type: ndcg_at_5
value: 44.393
- type: precision_at_1
value: 41.21
- type: precision_at_10
value: 10.593
- type: precision_at_100
value: 1.609
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 23.881
- type: precision_at_5
value: 17.339
- type: recall_at_1
value: 26.987
- type: recall_at_10
value: 58.875
- type: recall_at_100
value: 87.023
- type: recall_at_1000
value: 98.328
- type: recall_at_3
value: 42.265
- type: recall_at_5
value: 49.334
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 85.91701743836441
- type: cos_sim_ap
value: 92.53650618807644
- type: cos_sim_f1
value: 86.80265975431082
- type: cos_sim_precision
value: 83.79025239338556
- type: cos_sim_recall
value: 90.039747486556
- type: dot_accuracy
value: 77.17378232110643
- type: dot_ap
value: 85.40244368166546
- type: dot_f1
value: 79.03038001481951
- type: dot_precision
value: 72.20502901353966
- type: dot_recall
value: 87.2808043020809
- type: euclidean_accuracy
value: 84.65423932651834
- type: euclidean_ap
value: 91.47775530034588
- type: euclidean_f1
value: 85.64471499723298
- type: euclidean_precision
value: 81.31567885666246
- type: euclidean_recall
value: 90.46060322656068
- type: manhattan_accuracy
value: 84.58208057726999
- type: manhattan_ap
value: 91.46228709402014
- type: manhattan_f1
value: 85.6631626034444
- type: manhattan_precision
value: 82.10075026795283
- type: manhattan_recall
value: 89.5487491232172
- type: max_accuracy
value: 85.91701743836441
- type: max_ap
value: 92.53650618807644
- type: max_f1
value: 86.80265975431082
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 83.693
- type: map_at_10
value: 90.098
- type: map_at_100
value: 90.145
- type: map_at_1000
value: 90.146
- type: map_at_3
value: 89.445
- type: map_at_5
value: 89.935
- type: mrr_at_1
value: 83.878
- type: mrr_at_10
value: 90.007
- type: mrr_at_100
value: 90.045
- type: mrr_at_1000
value: 90.046
- type: mrr_at_3
value: 89.34
- type: mrr_at_5
value: 89.835
- type: ndcg_at_1
value: 84.089
- type: ndcg_at_10
value: 92.351
- type: ndcg_at_100
value: 92.54599999999999
- type: ndcg_at_1000
value: 92.561
- type: ndcg_at_3
value: 91.15299999999999
- type: ndcg_at_5
value: 91.968
- type: precision_at_1
value: 84.089
- type: precision_at_10
value: 10.011000000000001
- type: precision_at_100
value: 1.009
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 32.28
- type: precision_at_5
value: 19.789
- type: recall_at_1
value: 83.693
- type: recall_at_10
value: 99.05199999999999
- type: recall_at_100
value: 99.895
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 95.917
- type: recall_at_5
value: 97.893
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.924
- type: map_at_10
value: 81.392
- type: map_at_100
value: 84.209
- type: map_at_1000
value: 84.237
- type: map_at_3
value: 56.998000000000005
- type: map_at_5
value: 71.40100000000001
- type: mrr_at_1
value: 91.75
- type: mrr_at_10
value: 94.45
- type: mrr_at_100
value: 94.503
- type: mrr_at_1000
value: 94.505
- type: mrr_at_3
value: 94.258
- type: mrr_at_5
value: 94.381
- type: ndcg_at_1
value: 91.75
- type: ndcg_at_10
value: 88.53
- type: ndcg_at_100
value: 91.13900000000001
- type: ndcg_at_1000
value: 91.387
- type: ndcg_at_3
value: 87.925
- type: ndcg_at_5
value: 86.461
- type: precision_at_1
value: 91.75
- type: precision_at_10
value: 42.05
- type: precision_at_100
value: 4.827
- type: precision_at_1000
value: 0.48900000000000005
- type: precision_at_3
value: 78.55
- type: precision_at_5
value: 65.82000000000001
- type: recall_at_1
value: 26.924
- type: recall_at_10
value: 89.338
- type: recall_at_100
value: 97.856
- type: recall_at_1000
value: 99.11
- type: recall_at_3
value: 59.202999999999996
- type: recall_at_5
value: 75.642
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 54.800000000000004
- type: map_at_10
value: 65.613
- type: map_at_100
value: 66.185
- type: map_at_1000
value: 66.191
- type: map_at_3
value: 62.8
- type: map_at_5
value: 64.535
- type: mrr_at_1
value: 54.800000000000004
- type: mrr_at_10
value: 65.613
- type: mrr_at_100
value: 66.185
- type: mrr_at_1000
value: 66.191
- type: mrr_at_3
value: 62.8
- type: mrr_at_5
value: 64.535
- type: ndcg_at_1
value: 54.800000000000004
- type: ndcg_at_10
value: 70.991
- type: ndcg_at_100
value: 73.434
- type: ndcg_at_1000
value: 73.587
- type: ndcg_at_3
value: 65.324
- type: ndcg_at_5
value: 68.431
- type: precision_at_1
value: 54.800000000000004
- type: precision_at_10
value: 8.790000000000001
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.2
- type: precision_at_5
value: 16.02
- type: recall_at_1
value: 54.800000000000004
- type: recall_at_10
value: 87.9
- type: recall_at_100
value: 98.6
- type: recall_at_1000
value: 99.8
- type: recall_at_3
value: 72.6
- type: recall_at_5
value: 80.10000000000001
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 51.94305502116199
- type: f1
value: 39.82197338426721
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 90.31894934333957
- type: ap
value: 63.89821836499594
- type: f1
value: 85.93687177603624
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 73.18906216730208
- type: cos_sim_spearman
value: 79.44570226735877
- type: euclidean_pearson
value: 78.8105072242798
- type: euclidean_spearman
value: 79.15605680863212
- type: manhattan_pearson
value: 78.80576507484064
- type: manhattan_spearman
value: 79.14625534068364
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 41.58107192600853
- type: mrr
value: 41.37063492063492
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 68.33
- type: map_at_10
value: 78.261
- type: map_at_100
value: 78.522
- type: map_at_1000
value: 78.527
- type: map_at_3
value: 76.236
- type: map_at_5
value: 77.557
- type: mrr_at_1
value: 70.602
- type: mrr_at_10
value: 78.779
- type: mrr_at_100
value: 79.00500000000001
- type: mrr_at_1000
value: 79.01
- type: mrr_at_3
value: 77.037
- type: mrr_at_5
value: 78.157
- type: ndcg_at_1
value: 70.602
- type: ndcg_at_10
value: 82.254
- type: ndcg_at_100
value: 83.319
- type: ndcg_at_1000
value: 83.449
- type: ndcg_at_3
value: 78.46
- type: ndcg_at_5
value: 80.679
- type: precision_at_1
value: 70.602
- type: precision_at_10
value: 9.989
- type: precision_at_100
value: 1.05
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.598999999999997
- type: precision_at_5
value: 18.948
- type: recall_at_1
value: 68.33
- type: recall_at_10
value: 94.00800000000001
- type: recall_at_100
value: 98.589
- type: recall_at_1000
value: 99.60799999999999
- type: recall_at_3
value: 84.057
- type: recall_at_5
value: 89.32900000000001
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 78.13718897108272
- type: f1
value: 74.07613180855328
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.20040349697376
- type: f1
value: 85.05282136519973
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 56.8
- type: map_at_10
value: 64.199
- type: map_at_100
value: 64.89
- type: map_at_1000
value: 64.917
- type: map_at_3
value: 62.383
- type: map_at_5
value: 63.378
- type: mrr_at_1
value: 56.8
- type: mrr_at_10
value: 64.199
- type: mrr_at_100
value: 64.89
- type: mrr_at_1000
value: 64.917
- type: mrr_at_3
value: 62.383
- type: mrr_at_5
value: 63.378
- type: ndcg_at_1
value: 56.8
- type: ndcg_at_10
value: 67.944
- type: ndcg_at_100
value: 71.286
- type: ndcg_at_1000
value: 71.879
- type: ndcg_at_3
value: 64.163
- type: ndcg_at_5
value: 65.96600000000001
- type: precision_at_1
value: 56.8
- type: precision_at_10
value: 7.9799999999999995
- type: precision_at_100
value: 0.954
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 23.1
- type: precision_at_5
value: 14.74
- type: recall_at_1
value: 56.8
- type: recall_at_10
value: 79.80000000000001
- type: recall_at_100
value: 95.39999999999999
- type: recall_at_1000
value: 99.8
- type: recall_at_3
value: 69.3
- type: recall_at_5
value: 73.7
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 78.57666666666667
- type: f1
value: 78.23373528202681
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 85.43584190579317
- type: cos_sim_ap
value: 90.76665640338129
- type: cos_sim_f1
value: 86.5021770682148
- type: cos_sim_precision
value: 79.82142857142858
- type: cos_sim_recall
value: 94.40337909186906
- type: dot_accuracy
value: 78.66811044937737
- type: dot_ap
value: 85.84084363880804
- type: dot_f1
value: 80.10075566750629
- type: dot_precision
value: 76.58959537572254
- type: dot_recall
value: 83.9493136219641
- type: euclidean_accuracy
value: 84.46128857606931
- type: euclidean_ap
value: 88.62351100230491
- type: euclidean_f1
value: 85.7709469509172
- type: euclidean_precision
value: 80.8411214953271
- type: euclidean_recall
value: 91.34107708553326
- type: manhattan_accuracy
value: 84.51543042772063
- type: manhattan_ap
value: 88.53975607870393
- type: manhattan_f1
value: 85.75697211155378
- type: manhattan_precision
value: 81.14985862393968
- type: manhattan_recall
value: 90.91869060190075
- type: max_accuracy
value: 85.43584190579317
- type: max_ap
value: 90.76665640338129
- type: max_f1
value: 86.5021770682148
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 95.06999999999998
- type: ap
value: 93.45104559324996
- type: f1
value: 95.06036329426092
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 40.01998290519605
- type: cos_sim_spearman
value: 46.5989769986853
- type: euclidean_pearson
value: 45.37905883182924
- type: euclidean_spearman
value: 46.22213849806378
- type: manhattan_pearson
value: 45.40925124776211
- type: manhattan_spearman
value: 46.250705124226386
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 42.719516197112526
- type: cos_sim_spearman
value: 44.57507789581106
- type: euclidean_pearson
value: 35.73062264160721
- type: euclidean_spearman
value: 40.473523909913695
- type: manhattan_pearson
value: 35.69868964086357
- type: manhattan_spearman
value: 40.46349925372903
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.340118285801104
- type: cos_sim_spearman
value: 67.72781908620632
- type: euclidean_pearson
value: 63.161965746091596
- type: euclidean_spearman
value: 67.36825684340769
- type: manhattan_pearson
value: 63.089863788261425
- type: manhattan_spearman
value: 67.40868898995384
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 79.1646360962365
- type: cos_sim_spearman
value: 81.24426700767087
- type: euclidean_pearson
value: 79.43826409936123
- type: euclidean_spearman
value: 79.71787965300125
- type: manhattan_pearson
value: 79.43377784961737
- type: manhattan_spearman
value: 79.69348376886967
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 68.35595092507496
- type: mrr
value: 79.00244892585788
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.588
- type: map_at_10
value: 75.327
- type: map_at_100
value: 79.095
- type: map_at_1000
value: 79.163
- type: map_at_3
value: 52.637
- type: map_at_5
value: 64.802
- type: mrr_at_1
value: 88.103
- type: mrr_at_10
value: 91.29899999999999
- type: mrr_at_100
value: 91.408
- type: mrr_at_1000
value: 91.411
- type: mrr_at_3
value: 90.801
- type: mrr_at_5
value: 91.12700000000001
- type: ndcg_at_1
value: 88.103
- type: ndcg_at_10
value: 83.314
- type: ndcg_at_100
value: 87.201
- type: ndcg_at_1000
value: 87.83999999999999
- type: ndcg_at_3
value: 84.408
- type: ndcg_at_5
value: 83.078
- type: precision_at_1
value: 88.103
- type: precision_at_10
value: 41.638999999999996
- type: precision_at_100
value: 5.006
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 73.942
- type: precision_at_5
value: 62.056
- type: recall_at_1
value: 26.588
- type: recall_at_10
value: 82.819
- type: recall_at_100
value: 95.334
- type: recall_at_1000
value: 98.51299999999999
- type: recall_at_3
value: 54.74
- type: recall_at_5
value: 68.864
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 55.029
- type: f1
value: 53.043617905026764
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 77.83675116835911
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 74.19701455865277
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.7
- type: map_at_10
value: 75.593
- type: map_at_100
value: 75.863
- type: map_at_1000
value: 75.863
- type: map_at_3
value: 73.63300000000001
- type: map_at_5
value: 74.923
- type: mrr_at_1
value: 64.7
- type: mrr_at_10
value: 75.593
- type: mrr_at_100
value: 75.863
- type: mrr_at_1000
value: 75.863
- type: mrr_at_3
value: 73.63300000000001
- type: mrr_at_5
value: 74.923
- type: ndcg_at_1
value: 64.7
- type: ndcg_at_10
value: 80.399
- type: ndcg_at_100
value: 81.517
- type: ndcg_at_1000
value: 81.517
- type: ndcg_at_3
value: 76.504
- type: ndcg_at_5
value: 78.79899999999999
- type: precision_at_1
value: 64.7
- type: precision_at_10
value: 9.520000000000001
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.266999999999996
- type: precision_at_5
value: 18.060000000000002
- type: recall_at_1
value: 64.7
- type: recall_at_10
value: 95.19999999999999
- type: recall_at_100
value: 100
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 84.8
- type: recall_at_5
value: 90.3
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 89.69999999999999
- type: ap
value: 75.91371640164184
- type: f1
value: 88.34067777698694
Saco93/Conan-embedding-v1-Q4_K_S-GGUF
This model was converted to GGUF format from TencentBAC/Conan-embedding-v1 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Saco93/Conan-embedding-v1-Q4_K_S-GGUF --hf-file conan-embedding-v1-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Saco93/Conan-embedding-v1-Q4_K_S-GGUF --hf-file conan-embedding-v1-q4_k_s.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps 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 Saco93/Conan-embedding-v1-Q4_K_S-GGUF --hf-file conan-embedding-v1-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Saco93/Conan-embedding-v1-Q4_K_S-GGUF --hf-file conan-embedding-v1-q4_k_s.gguf -c 2048