---
title: gte-Qwen2-1.5B-instruct-GGUF
canonical_url: "https://www.modelscope.ai/models/second-state/gte-Qwen2-1.5B-instruct-GGUF"
md_url: "https://www.modelscope.ai/models/second-state/gte-Qwen2-1.5B-instruct-GGUF.md"
repository: second-state/gte-Qwen2-1.5B-instruct-GGUF
last_updated: 2026-11-11
license: apache-2.0
pipeline_tag: sentence-similarity
tasks:
  - sentence-similarity
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - Alibaba-NLP/gte-Qwen2-1.5B-instruct
base_model_relation: quantized
library_name:
  - sentence-transformers
  - gguf
  - pytorch
frameworks:
  - pytorch
downloads: 293
stars: 0
tags:
  - mteb
  - sentence-transformers
  - transformers
  - Qwen2
  - sentence-similarity
  - gguf
---

# gte-Qwen2-1.5B-instruct-GGUF

> gte-Qwen2-1.5B-instruct-GGUF - An open-source model by second-state on ModelScope. gte-Qwen2-1.5B-instruct-GGUF

second-state/gte-Qwen2-1.5B-instruct-GGUF is a sentence-similarity model on ModelScope. licensed under apache-2.0. derived from Alibaba-NLP/gte-Qwen2-1.5B-instruct.

- **Repository**: second-state/gte-Qwen2-1.5B-instruct-GGUF
- **License**: apache-2.0
- **Tasks**: sentence-similarity
- **Base model**: Alibaba-NLP/gte-Qwen2-1.5B-instruct
- **Tags**: mteb, sentence-transformers, transformers, Qwen2, sentence-similarity, gguf
- **Downloads**: 293
- **Stars**: 0
- **Last updated**: 2026-11-11

Source: https://www.modelscope.ai/models/second-state/gte-Qwen2-1.5B-instruct-GGUF

---

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# gte-Qwen2-1.5B-instruct-GGUF

## Original Model

[Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)

## Run with LlamaEdge

- LlamaEdge version: [v0.12.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.12.2) and above

- Prompt template

  - Prompt type: `embedding`

- Context size: `32000`

- Run as LlamaEdge service

  ```bash
  wasmedge --dir .:. --nn-preload default:GGML:AUTO:gte-Qwen2-1.5B-instruct-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template embedding \
    --ctx-size 32000 \
    --model-name gte-Qwen2-1.5B-instruct
  ```

## Quantized GGUF Models

| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [gte-Qwen2-1.5B-instruct-Q2_K.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q2_K.gguf)     | Q2_K   | 2 | 752 MB| smallest, significant quality loss - not recommended for most purposes |
| [gte-Qwen2-1.5B-instruct-Q3_K_L.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 980 MB| small, substantial quality loss |
| [gte-Qwen2-1.5B-instruct-Q3_K_M.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 924 MB| very small, high quality loss |
| [gte-Qwen2-1.5B-instruct-Q3_K_S.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 861 MB| very small, high quality loss |
| [gte-Qwen2-1.5B-instruct-Q4_0.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q4_0.gguf)     | Q4_0   | 4 | 1.07 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [gte-Qwen2-1.5B-instruct-Q4_K_M.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 1.12 GB| medium, balanced quality - recommended |
| [gte-Qwen2-1.5B-instruct-Q4_K_S.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 1.07 GB| small, greater quality loss |
| [gte-Qwen2-1.5B-instruct-Q5_0.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q5_0.gguf)     | Q5_0   | 5 | 1.26 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [gte-Qwen2-1.5B-instruct-Q5_K_M.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 1.28 GB| large, very low quality loss - recommended |
| [gte-Qwen2-1.5B-instruct-Q5_K_S.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 1.26 GB| large, low quality loss - recommended |
| [gte-Qwen2-1.5B-instruct-Q6_K.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q6_K.gguf)     | Q6_K   | 6 | 1.46 GB| very large, extremely low quality loss |
| [gte-Qwen2-1.5B-instruct-Q8_0.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-Q8_0.gguf)     | Q8_0   | 8 | 1.89 GB| very large, extremely low quality loss - not recommended |
| [gte-Qwen2-1.5B-instruct-f16.gguf](https://huggingface.co/second-state/gte-Qwen2-1.5B-instruct-GGUF/blob/main/gte-Qwen2-1.5B-instruct-f16.gguf)       | f16    | 8 | 3.56 GB| very large, extremely low quality loss - not recommended |

*Quantized with llama.cpp b3259*