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…
Runs locally from ~8.84 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | TULLUS/Qwen1.5-MoE-A2.7B-GGUF |
|---|---|
| Author | TULLUS |
| Pipeline | text-generation |
| License | other |
| Base model | Qwen/Qwen1.5-MoE-A2.7B |
| Last modified | 2026-10-07T05:22:03.000Z |
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 |
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- 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:
Run TULLUS/Qwen1.5-MoE-A2.7B-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models