Instructions to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Use Docker

docker model run hf.co/TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

LM Studio
Jan

vLLM

How to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TULLUS/Qwen1.5-MoE-A2.7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TULLUS/Qwen1.5-MoE-A2.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Ollama
How to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with Ollama:
```
ollama run hf.co/TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
```
Unsloth Desktop
Docker Model Runner
How to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16
```

Lemonade

How to use TULLUS/Qwen1.5-MoE-A2.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TULLUS/Qwen1.5-MoE-A2.7B-GGUF:F16

Run and chat with the model

lemonade run user.Qwen1.5-MoE-A2.7B-GGUF-F16

List all available models

lemonade list

Atomic Chat

Qwen1.5-MoE-A2.7B-GGUF / README.md

TULLUS

Update README.md

58db932 verified 2 days ago

preview code

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metadata

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

HF-> F16.gguf converted with a fresh kaggle cookbook :]

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"

TULLUS
/

Qwen1.5-MoE-A2.7B-GGUF