---
title: bge-large-en-v1.5-Q4_K_S-GGUF
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF.md"
repository: AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF
last_updated: 2024-09-02
license: mit
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
base_model:
  - BAAI/bge-large-en-v1.5
base_model_relation: quantized
library_name:
  - sentence-transformers
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 2253
stars: 0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - mteb
  - llama-cpp
  - gguf-my-repo
  - gguf
---

# bge-large-en-v1.5-Q4_K_S-GGUF

> bge-large-en-v1.5-Q4_K_S-GGUF - AI-ModelScope 在 ModelScope 开源的模型。archit28/bge-large-en-v1.5-Q4KS-GGUF This model was converted to GGUF format from BAAI/bge-large-en-v1.5 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card…

AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF 是 ModelScope 魔搭社区上的feature-extraction模型，采用 mit 许可，基于 BAAI/bge-large-en-v1.5 构建。

- **Repository**: AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF
- **License**: mit
- **Tasks**: feature-extraction
- **Base model**: BAAI/bge-large-en-v1.5
- **Tags**: sentence-transformers, feature-extraction, sentence-similarity, transformers, mteb, llama-cpp, gguf-my-repo, gguf
- **Downloads**: 2253
- **Stars**: 0
- **Last updated**: 2024-09-02

Source: https://www.modelscope.cn/models/AI-ModelScope/bge-large-en-v1.5-Q4_K_S-GGUF

---

# archit28/bge-large-en-v1.5-Q4_K_S-GGUF
This model was converted to GGUF format from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) 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-large-en-v1.5) 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 archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.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 archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo archit28/bge-large-en-v1.5-Q4_K_S-GGUF --hf-file bge-large-en-v1.5-q4_k_s.gguf -c 2048
```