README.md

---
license: mit
language:
- en
- zh
tags:
- mteb
- llama-cpp
- gguf-my-repo
pipeline_tag: feature-extraction
base_model: BAAI/bge-reranker-large
model-index:
- name: bge-reranker-base
  results:
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv1
      type: C-MTEB/CMedQAv1-reranking
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 81.27206722525007
    - type: mrr
      value: 84.14238095238095
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv2
      type: C-MTEB/CMedQAv2-reranking
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 84.10369934291236
    - type: mrr
      value: 86.79376984126984
  - task:
      type: Reranking
    dataset:
      name: MTEB MMarcoReranking
      type: C-MTEB/Mmarco-reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 35.4600511272538
    - type: mrr
      value: 34.60238095238095
  - task:
      type: Reranking
    dataset:
      name: MTEB T2Reranking
      type: C-MTEB/T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 67.27728847727172
    - type: mrr
      value: 77.1315192743764
---

# DrRos/bge-reranker-large-Q4_K_M-GGUF
This model was converted to GGUF format from [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) 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/BAAI/bge-reranker-large) 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 DrRos/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo DrRos/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-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 DrRos/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo DrRos/bge-reranker-large-Q4_K_M-GGUF --hf-file bge-reranker-large-q4_k_m.gguf -c 2048
```