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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
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3
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4
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14
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15
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16
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17
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18
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19
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20
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21
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22
+ type: mteb/amazon_counterfactual
23
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24
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25
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26
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27
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31
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35
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36
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37
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38
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39
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40
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51
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52
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54
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55
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+ value: 53.09098824553766
2708
+ - type: dot_recall
2709
+ value: 64.35356200527704
2710
+ - type: euclidean_accuracy
2711
+ value: 82.52369315133814
2712
+ - type: euclidean_ap
2713
+ value: 62.34857756663386
2714
+ - type: euclidean_f1
2715
+ value: 58.18225190839694
2716
+ - type: euclidean_precision
2717
+ value: 53.09098824553766
2718
+ - type: euclidean_recall
2719
+ value: 64.35356200527704
2720
+ - type: manhattan_accuracy
2721
+ value: 82.49389044525243
2722
+ - type: manhattan_ap
2723
+ value: 62.32245347238179
2724
+ - type: manhattan_f1
2725
+ value: 58.206309819213054
2726
+ - type: manhattan_precision
2727
+ value: 52.70704044511021
2728
+ - type: manhattan_recall
2729
+ value: 64.9868073878628
2730
+ - type: max_accuracy
2731
+ value: 82.52369315133814
2732
+ - type: max_ap
2733
+ value: 62.34858091376534
2734
+ - type: max_f1
2735
+ value: 58.206309819213054
2736
+ - task:
2737
+ type: PairClassification
2738
+ dataset:
2739
+ name: MTEB TwitterURLCorpus
2740
+ type: mteb/twitterurlcorpus-pairclassification
2741
+ config: default
2742
+ split: test
2743
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2744
+ metrics:
2745
+ - type: cos_sim_accuracy
2746
+ value: 88.34555827220863
2747
+ - type: cos_sim_ap
2748
+ value: 84.84152481680071
2749
+ - type: cos_sim_f1
2750
+ value: 76.860456739428
2751
+ - type: cos_sim_precision
2752
+ value: 72.21470150263978
2753
+ - type: cos_sim_recall
2754
+ value: 82.14505697566985
2755
+ - type: dot_accuracy
2756
+ value: 88.34555827220863
2757
+ - type: dot_ap
2758
+ value: 84.84152743322608
2759
+ - type: dot_f1
2760
+ value: 76.860456739428
2761
+ - type: dot_precision
2762
+ value: 72.21470150263978
2763
+ - type: dot_recall
2764
+ value: 82.14505697566985
2765
+ - type: euclidean_accuracy
2766
+ value: 88.34555827220863
2767
+ - type: euclidean_ap
2768
+ value: 84.84152589453169
2769
+ - type: euclidean_f1
2770
+ value: 76.860456739428
2771
+ - type: euclidean_precision
2772
+ value: 72.21470150263978
2773
+ - type: euclidean_recall
2774
+ value: 82.14505697566985
2775
+ - type: manhattan_accuracy
2776
+ value: 88.38242713548337
2777
+ - type: manhattan_ap
2778
+ value: 84.8112124970968
2779
+ - type: manhattan_f1
2780
+ value: 76.83599206057487
2781
+ - type: manhattan_precision
2782
+ value: 73.51244900829934
2783
+ - type: manhattan_recall
2784
+ value: 80.47428395441946
2785
+ - type: max_accuracy
2786
+ value: 88.38242713548337
2787
+ - type: max_ap
2788
+ value: 84.84152743322608
2789
+ - type: max_f1
2790
+ value: 76.860456739428
2791
+ - task:
2792
+ type: Clustering
2793
+ dataset:
2794
+ name: MTEB WikiCitiesClustering
2795
+ type: jinaai/cities_wiki_clustering
2796
+ config: default
2797
+ split: test
2798
+ revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
2799
+ metrics:
2800
+ - type: v_measure
2801
+ value: 85.5314389263015
2802
+ ---
2803
+
2804
+ # radia/snowflake-arctic-embed-l-Q4_K_M-GGUF
2805
+ This model was converted to GGUF format from [`Snowflake/snowflake-arctic-embed-l`](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
2806
+ Refer to the [original model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) for more details on the model.
2807
+
2808
+ ## Use with llama.cpp
2809
+ Install llama.cpp through brew (works on Mac and Linux)
2810
+
2811
+ ```bash
2812
+ brew install llama.cpp
2813
+
2814
+ ```
2815
+ Invoke the llama.cpp server or the CLI.
2816
+
2817
+ ### CLI:
2818
+ ```bash
2819
+ llama --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -p "The meaning to life and the universe is"
2820
+ ```
2821
+
2822
+ ### Server:
2823
+ ```bash
2824
+ llama-server --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -c 2048
2825
+ ```
2826
+
2827
+ 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.
2828
+
2829
+ Step 1: Clone llama.cpp from GitHub.
2830
+ ```
2831
+ git clone https://github.com/ggerganov/llama.cpp
2832
+ ```
2833
+
2834
+ 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).
2835
+ ```
2836
+ cd llama.cpp && LLAMA_CURL=1 make
2837
+ ```
2838
+
2839
+ Step 3: Run inference through the main binary.
2840
+ ```
2841
+ ./main --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -p "The meaning to life and the universe is"
2842
+ ```
2843
+ or
2844
+ ```
2845
+ ./server --hf-repo radia/snowflake-arctic-embed-l-Q4_K_M-GGUF --hf-file snowflake-arctic-embed-l-q4_k_m.gguf -c 2048
2846
+ ```