---
title: E5-Mistral-7B-Instruct-Embedding-GGUF
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF.md"
repository: AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF
last_updated: 2024-11-21
license: mit
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
model_type:
  - mistral
architectures:
  - MistralModel
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2265
stars: 1
tags:
  - gguf
---

# E5-Mistral-7B-Instruct-Embedding-GGUF

> E5-Mistral-7B-Instruct-Embedding-GGUF - AI-ModelScope 在 ModelScope 开源的模型。E5-Mistral-7B-Instruct-Embedding-GGUF

AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF 是 ModelScope 魔搭社区上的feature-extraction模型，采用 mit 许可，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF
- **License**: mit
- **Tasks**: feature-extraction
- **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: gguf
- **Downloads**: 2265
- **Stars**: 1
- **Last updated**: 2024-11-21

Source: https://www.modelscope.cn/models/AI-ModelScope/E5-Mistral-7B-Instruct-Embedding-GGUF

---

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# E5-Mistral-7B-Instruct-Embedding-GGUF

## Original Model

[intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)

## Run with LlamaEdge

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

- Prompt template

  - Prompt type: `embedding`

- Context size: `4096`

- Run as LlamaEdge service

  ```bash
  wasmedge --dir .:. --nn-preload default:GGML:AUTO:e5-mistral-7b-instruct-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template embedding \
    --ctx-size 4096 \
    --model-name e5-mistral-7b-instruct
  ```

## Quantized GGUF Models

| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [e5-mistral-7b-instruct-Q2_K.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q2_K.gguf)     | Q2_K   | 2 | 2.72 GB| smallest, significant quality loss - not recommended for most purposes |
| [e5-mistral-7b-instruct-Q3_K_L.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| small, substantial quality loss |
| [e5-mistral-7b-instruct-Q3_K_M.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| very small, high quality loss |
| [e5-mistral-7b-instruct-Q3_K_S.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| very small, high quality loss |
| [e5-mistral-7b-instruct-Q4_0.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q4_0.gguf)     | Q4_0   | 4 | 4.11 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [e5-mistral-7b-instruct-Q4_K_M.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| medium, balanced quality - recommended |
| [e5-mistral-7b-instruct-Q4_K_S.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| small, greater quality loss |
| [e5-mistral-7b-instruct-Q5_0.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q5_0.gguf)     | Q5_0   | 5 | 5.00 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [e5-mistral-7b-instruct-Q5_K_M.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| large, very low quality loss - recommended |
| [e5-mistral-7b-instruct-Q5_K_S.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| large, low quality loss - recommended |
| [e5-mistral-7b-instruct-Q6_K.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q6_K.gguf)     | Q6_K   | 6 | 5.94 GB| very large, extremely low quality loss |
| [e5-mistral-7b-instruct-Q8_0.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-Q8_0.gguf)     | Q8_0   | 8 |  7.7 GB| very large, extremely low quality loss - not recommended |
| [e5-mistral-7b-instruct-f16.gguf](https://huggingface.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF/blob/main/e5-mistral-7b-instruct-f16.gguf)       | f16    | 8 | 14.5 GB| very large, extremely low quality loss - not recommended |

*Quantized with llama.cpp b2334*