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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
```
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