---
title: All-MiniLM-L6-v2-Embedding-GGUF
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF.md"
repository: AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF
last_updated: 2024-09-02
license: apache-2.0
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
model_type:
  - bert
architectures:
  - BertModel
base_model:
  - sentence-transformers/all-MiniLM-L6-v2
base_model_relation: quantized
library_name:
  - sentence-transformers
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2396
stars: 0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - gguf
---

# All-MiniLM-L6-v2-Embedding-GGUF

> All-MiniLM-L6-v2-Embedding-GGUF - AI-ModelScope 在 ModelScope 开源的模型。All-MiniLM-L6-v2-GGUF

AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF 是 ModelScope 魔搭社区上的feature-extraction模型，采用 apache-2.0 许可，基于 sentence-transformers/all-MiniLM-L6-v2 构建，可用 deploy_task emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF
- **License**: apache-2.0
- **Tasks**: feature-extraction
- **Base model**: sentence-transformers/all-MiniLM-L6-v2
- **Inference backends**: deploy_task emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: sentence-transformers, feature-extraction, sentence-similarity, transformers, gguf
- **Downloads**: 2396
- **Stars**: 0
- **Last updated**: 2024-09-02

Source: https://www.modelscope.cn/models/AI-ModelScope/All-MiniLM-L6-v2-Embedding-GGUF

---

<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->

# All-MiniLM-L6-v2-GGUF

## Original Model

[sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)

## Run with LlamaEdge

- LlamaEdge version: [v0.8.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.8.2) and above

- Context size: `384`

- Vector size: `256`

- Run as LlamaEdge service

  ```bash
  wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf \
    llama-api-server.wasm \
    --prompt-template llama-2-chat \
    --ctx-size 256 \
    --model-name all-MiniLM-L6-v2
  ```

## Quantized GGUF Models

| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [all-MiniLM-L6-v2-Q2_K.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q2_K.gguf)     | Q2_K   | 2 | 19.2 MB| smallest, significant quality loss - not recommended for most purposes |
| [all-MiniLM-L6-v2-Q3_K_L.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q3_K_L.gguf) | Q3_K_L | 3 | 20.5 MB| small, substantial quality loss |
| [all-MiniLM-L6-v2-Q3_K_M.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q3_K_M.gguf) | Q3_K_M | 3 | 19.9 MB| very small, high quality loss |
| [all-MiniLM-L6-v2-Q3_K_S.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q3_K_S.gguf) | Q3_K_S | 3 | 19.2 MB| very small, high quality loss |
| [all-MiniLM-L6-v2-Q4_0.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q4_0.gguf)     | Q4_0   | 4 | 19.7 MB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [all-MiniLM-L6-v2-Q4_K_M.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q4_K_M.gguf) | Q4_K_M | 4 | 21 MB| medium, balanced quality - recommended |
| [all-MiniLM-L6-v2-Q4_K_S.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q4_K_S.gguf) | Q4_K_S | 4 | 20.7 MB| small, greater quality loss |
| [all-MiniLM-L6-v2-Q5_0.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q5_0.gguf)     | Q5_0   | 5 | 21 MB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [all-MiniLM-L6-v2-Q5_K_M.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q5_K_M.gguf) | Q5_K_M | 5 | 21.7 MB| large, very low quality loss - recommended |
| [all-MiniLM-L6-v2-Q5_K_S.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q5_K_S.gguf) | Q5_K_S | 5 | 21.5 MB| large, low quality loss - recommended |
| [all-MiniLM-L6-v2-Q6_K.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q6_K.gguf)     | Q6_K   | 6 | 24.2 MB| very large, extremely low quality loss |
| [all-MiniLM-L6-v2-Q8_0.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-Q8_0.gguf)     | Q8_0   | 8 | 25 MB| very large, extremely low quality loss - not recommended |
| [all-MiniLM-L6-v2-ggml-model-f16.gguf](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/blob/main/all-MiniLM-L6-v2-ggml-model-f16.gguf)     | Q8_0   | 8 | 45.9 MB| very large, extremely low quality loss - not recommended |

*Quantized with llama.cpp b2334*