metadata
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
- 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
- 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
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 76.4
- type: recall_at_5
value: 84
- 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
- type: recall_at_1000
value: 100
- 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
- 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
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 87.4
- type: recall_at_5
value: 92.7
- type: main_score
value: 83.83500000000001
lagoon999/Chuxin-Embedding-Q4_K_M-GGUF
This model was converted to GGUF format from chuxin-llm/Chuxin-Embedding 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 lagoon999/Chuxin-Embedding-Q4_K_M-GGUF --hf-file chuxin-embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo lagoon999/Chuxin-Embedding-Q4_K_M-GGUF --hf-file chuxin-embedding-q4_k_m.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 lagoon999/Chuxin-Embedding-Q4_K_M-GGUF --hf-file chuxin-embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo lagoon999/Chuxin-Embedding-Q4_K_M-GGUF --hf-file chuxin-embedding-q4_k_m.gguf -c 2048