--- language: - zh tags: - mteb - llama-cpp - gguf-my-repo base_model: chuxin-llm/Chuxin-Embedding model-index: - name: Chuxin-Embedding results: - task: type: Retrieval dataset: name: MTEB CmedqaRetrieval (default) type: C-MTEB/CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: map_at_1 value: 33.391999999999996 - type: map_at_10 value: 48.715 - type: map_at_100 value: 50.381 - type: map_at_1000 value: 50.456 - type: map_at_3 value: 43.708999999999996 - type: map_at_5 value: 46.405 - type: mrr_at_1 value: 48.612 - type: mrr_at_10 value: 58.67099999999999 - type: mrr_at_100 value: 59.38 - type: mrr_at_1000 value: 59.396 - type: mrr_at_3 value: 55.906 - type: mrr_at_5 value: 57.421 - type: ndcg_at_1 value: 48.612 - type: ndcg_at_10 value: 56.581 - type: ndcg_at_100 value: 62.422999999999995 - type: ndcg_at_1000 value: 63.476 - type: ndcg_at_3 value: 50.271 - type: ndcg_at_5 value: 52.79899999999999 - type: precision_at_1 value: 48.612 - type: precision_at_10 value: 11.995000000000001 - type: precision_at_100 value: 1.696 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 27.465 - type: precision_at_5 value: 19.675 - type: recall_at_1 value: 33.391999999999996 - type: recall_at_10 value: 69.87100000000001 - type: recall_at_100 value: 93.078 - type: recall_at_1000 value: 99.55199999999999 - type: recall_at_3 value: 50.939 - type: recall_at_5 value: 58.714 - type: main_score value: 56.581 - task: type: Retrieval dataset: name: MTEB CovidRetrieval (default) type: C-MTEB/CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: map_at_1 value: 71.918 - type: map_at_10 value: 80.609 - type: map_at_100 value: 80.796 - type: map_at_1000 value: 80.798 - type: map_at_3 value: 79.224 - type: map_at_5 value: 79.96 - type: mrr_at_1 value: 72.076 - type: mrr_at_10 value: 80.61399999999999 - type: mrr_at_100 value: 80.801 - type: mrr_at_1000 value: 80.803 - type: mrr_at_3 value: 79.276 - type: mrr_at_5 value: 80.025 - type: ndcg_at_1 value: 72.076 - type: ndcg_at_10 value: 84.286 - type: ndcg_at_100 value: 85.14500000000001 - type: ndcg_at_1000 value: 85.21 - type: ndcg_at_3 value: 81.45400000000001 - type: ndcg_at_5 value: 82.781 - type: precision_at_1 value: 72.076 - type: precision_at_10 value: 9.663 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 29.398999999999997 - type: precision_at_5 value: 18.335 - type: recall_at_1 value: 71.918 - type: recall_at_10 value: 95.574 - type: recall_at_100 value: 99.473 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 87.82900000000001 - type: recall_at_5 value: 90.991 - type: main_score value: 84.286 - task: type: Retrieval dataset: name: MTEB DuRetrieval (default) type: C-MTEB/DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: map_at_1 value: 25.019999999999996 - type: map_at_10 value: 77.744 - type: map_at_100 value: 80.562 - type: map_at_1000 value: 80.60300000000001 - type: map_at_3 value: 52.642999999999994 - type: map_at_5 value: 67.179 - type: mrr_at_1 value: 86.5 - type: mrr_at_10 value: 91.024 - type: mrr_at_100 value: 91.09 - type: mrr_at_1000 value: 91.093 - type: mrr_at_3 value: 90.558 - type: mrr_at_5 value: 90.913 - type: ndcg_at_1 value: 86.5 - type: ndcg_at_10 value: 85.651 - type: ndcg_at_100 value: 88.504 - type: ndcg_at_1000 value: 88.887 - type: ndcg_at_3 value: 82.707 - type: ndcg_at_5 value: 82.596 - type: precision_at_1 value: 86.5 - type: precision_at_10 value: 41.595 - type: precision_at_100 value: 4.7940000000000005 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 74.233 - type: precision_at_5 value: 63.68000000000001 - type: recall_at_1 value: 25.019999999999996 - type: recall_at_10 value: 88.114 - type: recall_at_100 value: 97.442 - type: recall_at_1000 value: 99.39099999999999 - type: recall_at_3 value: 55.397 - type: recall_at_5 value: 73.095 - type: main_score value: 85.651 - task: type: Retrieval dataset: name: MTEB EcomRetrieval (default) type: C-MTEB/EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 55.60000000000001 - type: map_at_10 value: 67.891 - type: map_at_100 value: 68.28699999999999 - type: map_at_1000 value: 68.28699999999999 - type: map_at_3 value: 64.86699999999999 - type: map_at_5 value: 66.652 - type: mrr_at_1 value: 55.60000000000001 - type: mrr_at_10 value: 67.891 - type: mrr_at_100 value: 68.28699999999999 - type: mrr_at_1000 value: 68.28699999999999 - type: mrr_at_3 value: 64.86699999999999 - type: mrr_at_5 value: 66.652 - type: ndcg_at_1 value: 55.60000000000001 - type: ndcg_at_10 value: 74.01100000000001 - type: ndcg_at_100 value: 75.602 - type: ndcg_at_1000 value: 75.602 - type: ndcg_at_3 value: 67.833 - type: ndcg_at_5 value: 71.005 - type: precision_at_1 value: 55.60000000000001 - type: precision_at_10 value: 9.33 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 25.467000000000002 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 55.60000000000001 - type: recall_at_10 value: 93.30000000000001 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 76.4 - type: recall_at_5 value: 84.0 - type: main_score value: 74.01100000000001 - task: type: Retrieval dataset: name: MTEB MMarcoRetrieval (default) type: C-MTEB/MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: map_at_1 value: 66.24799999999999 - type: map_at_10 value: 75.356 - type: map_at_100 value: 75.653 - type: map_at_1000 value: 75.664 - type: map_at_3 value: 73.515 - type: map_at_5 value: 74.67099999999999 - type: mrr_at_1 value: 68.496 - type: mrr_at_10 value: 75.91499999999999 - type: mrr_at_100 value: 76.17399999999999 - type: mrr_at_1000 value: 76.184 - type: mrr_at_3 value: 74.315 - type: mrr_at_5 value: 75.313 - type: ndcg_at_1 value: 68.496 - type: ndcg_at_10 value: 79.065 - type: ndcg_at_100 value: 80.417 - type: ndcg_at_1000 value: 80.72399999999999 - type: ndcg_at_3 value: 75.551 - type: ndcg_at_5 value: 77.505 - type: precision_at_1 value: 68.496 - type: precision_at_10 value: 9.563 - type: precision_at_100 value: 1.024 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.391 - type: precision_at_5 value: 18.086 - type: recall_at_1 value: 66.24799999999999 - type: recall_at_10 value: 89.97 - type: recall_at_100 value: 96.13199999999999 - type: recall_at_1000 value: 98.551 - type: recall_at_3 value: 80.624 - type: recall_at_5 value: 85.271 - type: main_score value: 79.065 - task: type: Retrieval dataset: name: MTEB MedicalRetrieval (default) type: C-MTEB/MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: map_at_1 value: 61.8 - type: map_at_10 value: 71.101 - type: map_at_100 value: 71.576 - type: map_at_1000 value: 71.583 - type: map_at_3 value: 68.867 - type: map_at_5 value: 70.272 - type: mrr_at_1 value: 61.9 - type: mrr_at_10 value: 71.158 - type: mrr_at_100 value: 71.625 - type: mrr_at_1000 value: 71.631 - type: mrr_at_3 value: 68.917 - type: mrr_at_5 value: 70.317 - type: ndcg_at_1 value: 61.8 - type: ndcg_at_10 value: 75.624 - type: ndcg_at_100 value: 77.702 - type: ndcg_at_1000 value: 77.836 - type: ndcg_at_3 value: 71.114 - type: ndcg_at_5 value: 73.636 - type: precision_at_1 value: 61.8 - type: precision_at_10 value: 8.98 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 25.867 - type: precision_at_5 value: 16.74 - type: recall_at_1 value: 61.8 - type: recall_at_10 value: 89.8 - type: recall_at_100 value: 99.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 77.60000000000001 - type: recall_at_5 value: 83.7 - type: main_score value: 75.624 - task: type: Retrieval dataset: name: MTEB T2Retrieval (default) type: C-MTEB/T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: map_at_1 value: 27.173000000000002 - type: map_at_10 value: 76.454 - type: map_at_100 value: 80.021 - type: map_at_1000 value: 80.092 - type: map_at_3 value: 53.876999999999995 - type: map_at_5 value: 66.122 - type: mrr_at_1 value: 89.519 - type: mrr_at_10 value: 92.091 - type: mrr_at_100 value: 92.179 - type: mrr_at_1000 value: 92.183 - type: mrr_at_3 value: 91.655 - type: mrr_at_5 value: 91.94 - type: ndcg_at_1 value: 89.519 - type: ndcg_at_10 value: 84.043 - type: ndcg_at_100 value: 87.60900000000001 - type: ndcg_at_1000 value: 88.32799999999999 - type: ndcg_at_3 value: 85.623 - type: ndcg_at_5 value: 84.111 - type: precision_at_1 value: 89.519 - type: precision_at_10 value: 41.760000000000005 - type: precision_at_100 value: 4.982 - type: precision_at_1000 value: 0.515 - type: precision_at_3 value: 74.944 - type: precision_at_5 value: 62.705999999999996 - type: recall_at_1 value: 27.173000000000002 - type: recall_at_10 value: 82.878 - type: recall_at_100 value: 94.527 - type: recall_at_1000 value: 98.24199999999999 - type: recall_at_3 value: 55.589 - type: recall_at_5 value: 69.476 - type: main_score value: 84.043 - task: type: Retrieval dataset: name: MTEB VideoRetrieval (default) type: C-MTEB/VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: map_at_1 value: 70.1 - type: map_at_10 value: 79.62 - type: map_at_100 value: 79.804 - type: map_at_1000 value: 79.804 - type: map_at_3 value: 77.81700000000001 - type: map_at_5 value: 79.037 - type: mrr_at_1 value: 70.1 - type: mrr_at_10 value: 79.62 - type: mrr_at_100 value: 79.804 - type: mrr_at_1000 value: 79.804 - type: mrr_at_3 value: 77.81700000000001 - type: mrr_at_5 value: 79.037 - type: ndcg_at_1 value: 70.1 - type: ndcg_at_10 value: 83.83500000000001 - type: ndcg_at_100 value: 84.584 - type: ndcg_at_1000 value: 84.584 - type: ndcg_at_3 value: 80.282 - type: ndcg_at_5 value: 82.472 - type: precision_at_1 value: 70.1 - type: precision_at_10 value: 9.68 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 29.133 - type: precision_at_5 value: 18.54 - type: recall_at_1 value: 70.1 - type: recall_at_10 value: 96.8 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 87.4 - type: recall_at_5 value: 92.7 - type: main_score value: 83.83500000000001 --- # lagoon999/Chuxin-Embedding-Q8_0-GGUF This model was converted to GGUF format from [`chuxin-llm/Chuxin-Embedding`](https://huggingface.co/chuxin-llm/Chuxin-Embedding) 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/chuxin-llm/Chuxin-Embedding) 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 lagoon999/Chuxin-Embedding-Q8_0-GGUF --hf-file chuxin-embedding-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo lagoon999/Chuxin-Embedding-Q8_0-GGUF --hf-file chuxin-embedding-q8_0.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 lagoon999/Chuxin-Embedding-Q8_0-GGUF --hf-file chuxin-embedding-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo lagoon999/Chuxin-Embedding-Q8_0-GGUF --hf-file chuxin-embedding-q8_0.gguf -c 2048 ```