---
language:
- zh
base_model: OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0
pipeline_tag: feature-extraction
tags:
- mteb
- sentence-transformers
- llama-cpp
- gguf-my-repo
model-index:
- name: Ops-MoA-Yuan-embedding-1.0
  results:
  - task:
      type: Retrieval
    dataset:
      name: MTEB CmedqaRetrieval
      type: C-MTEB/CmedqaRetrieval
      config: default
      split: dev
      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
    metrics:
    - type: ndcg_at_10
      value: 51.461
  - task:
      type: Retrieval
    dataset:
      name: MTEB CovidRetrieval
      type: C-MTEB/CovidRetrieval
      config: default
      split: dev
      revision: 1271c7809071a13532e05f25fb53511ffce77117
    metrics:
    - type: ndcg_at_10
      value: 93.2
  - task:
      type: Retrieval
    dataset:
      name: MTEB DuRetrieval
      type: C-MTEB/DuRetrieval
      config: default
      split: dev
      revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
    metrics:
    - type: ndcg_at_10
      value: 89.84
  - task:
      type: Retrieval
    dataset:
      name: MTEB EcomRetrieval
      type: C-MTEB/EcomRetrieval
      config: default
      split: dev
      revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
    metrics:
    - type: ndcg_at_10
      value: 71.084
  - task:
      type: Retrieval
    dataset:
      name: MTEB MMarcoRetrieval
      type: C-MTEB/MMarcoRetrieval
      config: default
      split: dev
      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
    metrics:
    - type: ndcg_at_10
      value: 82.43
  - task:
      type: Retrieval
    dataset:
      name: MTEB MedicalRetrieval
      type: C-MTEB/MedicalRetrieval
      config: default
      split: dev
      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
    metrics:
    - type: ndcg_at_10
      value: 74.848
  - task:
      type: Retrieval
    dataset:
      name: MTEB T2Retrieval
      type: C-MTEB/T2Retrieval
      config: default
      split: dev
      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
    metrics:
    - type: ndcg_at_10
      value: 85.784
  - task:
      type: Retrieval
    dataset:
      name: MTEB VideoRetrieval
      type: C-MTEB/VideoRetrieval
      config: default
      split: dev
      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
    metrics:
    - type: ndcg_at_10
      value: 79.513
---

# 6san/Ops-MoA-Yuan-embedding-1.0-Q8_0-GGUF
This model was converted to GGUF format from [`OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0`](https://huggingface.co/OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0) 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/OpenSearch-AI/Ops-MoA-Yuan-embedding-1.0) 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 6san/Ops-MoA-Yuan-embedding-1.0-Q8_0-GGUF --hf-file ops-moa-yuan-embedding-1.0-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo 6san/Ops-MoA-Yuan-embedding-1.0-Q8_0-GGUF --hf-file ops-moa-yuan-embedding-1.0-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 6san/Ops-MoA-Yuan-embedding-1.0-Q8_0-GGUF --hf-file ops-moa-yuan-embedding-1.0-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo 6san/Ops-MoA-Yuan-embedding-1.0-Q8_0-GGUF --hf-file ops-moa-yuan-embedding-1.0-q8_0.gguf -c 2048
```