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+---
+base_model: harheem/bge-m3-nvidia-ko-v1
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dataset_size:1K<n<10K
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+- llama-cpp
+- gguf-my-repo
+widget:
+- source_sentence: 하이브리다이저란 무엇인가요?
+  sentences:
+  - 하이퍼바이저는 보안에서 어떤 역할을 합니까?
+  - 지난 몇 년간 CUDA 생태계는 어떻게 발전해 왔나요?
+  - 로컬 메모리 액세스 성능을 결정하는 요소는 무엇입니까?
+- source_sentence: 임시 구독의 용도는 무엇입니까?
+  sentences:
+  - 메모리 액세스 최적화에서 프리패치의 역할은 무엇입니까?
+  - CUDA 인식 MPI는 확장 측면에서 어떻게 작동합니까?
+  - CUDA 8이 해결하는 계산상의 과제에는 어떤 것이 있습니까?
+- source_sentence: '''saxpy''는 무엇을 뜻하나요?'
+  sentences:
+  - CUDA C/C++의 맥락에서 SAXPY는 무엇입니까?
+  - Numba는 다른 GPU 가속 방법과 어떻게 다른가요?
+  - 장치 LTO는 CUDA 애플리케이션에 어떤 이점을 제공합니까?
+- source_sentence: USD/Hydra란 무엇인가요?
+  sentences:
+  - 쿠다란 무엇인가요?
+  - y 미분 계산에 사용되는 접근 방식의 단점은 무엇입니까?
+  - Pascal 아키텍처는 통합 메모리를 어떻게 개선합니까?
+- source_sentence: CUDAcast란 무엇인가요?
+  sentences:
+  - CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요?
+  - 이 게시물에 기여한 것으로 인정받은 사람은 누구입니까?
+  - WSL 2에서 NVML의 목적은 무엇입니까?
+model-index:
+- name: BGE base Financial Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5443037974683544
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7749648382559775
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8523206751054853
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9409282700421941
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5443037974683544
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2583216127519925
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17046413502109703
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09409282700421939
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5443037974683544
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7749648382559775
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8523206751054853
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9409282700421941
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7411108924386547
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.677065054807671
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6802131506478553
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5386779184247539
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7749648382559775
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8593530239099859
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9451476793248945
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5386779184247539
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2583216127519925
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17187060478199717
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09451476793248943
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5386779184247539
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7749648382559775
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8593530239099859
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9451476793248945
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7413571133247474
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6759917844306029
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.678939165210132
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.540084388185654
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7791842475386779
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8621659634317862
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9423347398030942
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.540084388185654
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.25972808251289264
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1724331926863572
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09423347398030943
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.540084388185654
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7791842475386779
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8621659634317862
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9423347398030942
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7403981257690416
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6756379344986938
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6787046866761269
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.5218002812939522
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7679324894514767
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8635724331926864
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9367088607594937
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.5218002812939522
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2559774964838256
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17271448663853725
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09367088607594935
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.5218002812939522
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7679324894514767
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8635724331926864
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9367088607594937
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7305864977688176
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6641673922264634
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6671648971944116
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.509142053445851
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7426160337552743
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8284106891701828
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9310829817158931
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.509142053445851
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24753867791842477
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16568213783403654
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09310829817158929
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.509142053445851
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7426160337552743
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8284106891701828
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9310829817158931
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.7135661304090457
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6444829549259928
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6474431148702396
+      name: Cosine Map@100
+---
+
+# hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF
+This model was converted to GGUF format from [`harheem/bge-m3-nvidia-ko-v1`](https://huggingface.co/harheem/bge-m3-nvidia-ko-v1) 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/harheem/bge-m3-nvidia-ko-v1) 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 hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-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 hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo hongkeon/bge-m3-nvidia-ko-v1-Q4_K_M-GGUF --hf-file bge-m3-nvidia-ko-v1-q4_k_m.gguf -c 2048
+```