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TULLUS/Qwen1.5-MoE-A2.7B-GGUF overview

I am moving the other quants into this repo as well.. Qwen1.5 MoE A2.7B Introduction Qwen1.5 MoE is a transformer based MoE decoder only language model pretrai…

gguftext-generationqwen2_moemixture-of-expertsconversationalpretrainedbase_model:Qwen/Qwen1.5-MoE-A2.7Bbase_model:quantized:Qwen/Qwen1.5-MoE-A2.7Blicense:otherendpoints_compatibleregion:us

Runs locally from ~8.84 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
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Repository Files & Downloads

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen1.5-MoE-A2-7B-f16.ggufGGUFF1626.68 GBDownload
qwen1.5-moe-a2.7b-q4_k_m.ggufGGUFQ4_K_M8.84 GBDownload

Model Details

Model IDTULLUS/Qwen1.5-MoE-A2.7B-GGUF
AuthorTULLUS
Pipelinetext-generation
Licenseother
Base modelQwen/Qwen1.5-MoE-A2.7B
Last modified2026-10-07T05:22:03.000Z

Model README

---

license: other

license_name: tongyi-qianwen

license_link: https://huggingface.co

base_model: Qwen/Qwen1.5-MoE-A2.7B

tags:

  • text-generation
  • qwen2_moe
  • mixture-of-experts
  • conversational
  • pretrained

---

  • I am moving the other quants into this repo as well..

---

Qwen1.5-MoE-A2.7B

Introduction

Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data.

For more details, please refer to our blog post and GitHub repo.

Model Details

Qwen1.5-MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, Qwen1.5-MoE-A2.7B is upcycled from Qwen-1.8B. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while achieving comparable performance to Qwen1.5-7B, it only requires 25% of the training resources. We also observed that the inference speed is 1.74 times that of Qwen1.5-7B.

Requirements

The code of Qwen1.5-MoE has been in the latest Hugging face transformers and we advise you to build from source with command pip install git+https://github.com/huggingface/transformers, or you might encounter the following error:

KeyError: 'qwen2_moe'.

Usage

markdown

📊 Quantization Comparison

| Quantization | File Size | Recommendation |

|--------------|-----------|------------------------------------|

| F16 | ~28.6 GB | Best precision (for high-end GPUs) |

| Q5_K_M | | Best balance of logic and size |

| Q4_K_M | ~9.5 GB | Recommended for most users |

| IQ4_XS | | Best for low-RAM devices |

---

  • Adding The FNG's

TULLUS/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF

This model was converted to GGUF format from TULLUS/Qwen1.5-MoE-A2.7B using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo TULLUS/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

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