---
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
```