Instructions to use unsloth/Qwen2.5-Omni-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Qwen2.5-Omni-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("unsloth/Qwen2.5-Omni-7B-GGUF")
model = AutoModel.from_pretrained("unsloth/Qwen2.5-Omni-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/Qwen2.5-Omni-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 unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

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 unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

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 unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use unsloth/Qwen2.5-Omni-7B-GGUF with Ollama:
```
ollama run hf.co/unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
```
Unsloth Desktop
Docker Model Runner
How to use unsloth/Qwen2.5-Omni-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/Qwen2.5-Omni-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Qwen2.5-Omni-7B-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Qwen2.5-Omni-7B-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Atomic Chat

Qwen2.5-Omni-7B-GGUF / Qwen2.5-Omni-7B-UD-Q3_K_XL.gguf

danielhanchen

Add files using upload-large-folder tool

3d6e4dd verified over 1 year ago

History Blame GGUF Editor new Delete

4 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8fdf3eef597555236b1685f3129f154228ff742c0785c16dd36f1813e27dfa4d
Size of remote file:: 4 GB
SHA256:: 74070b9bc0cac94592caab9ded029801488540c3b07e4604567b65ed8242695f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.