Instructions to use unsloth/Qwen2.5-Omni-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download Qwen2.5-Omni-7B-UD-Q6_K_XL.gguf from unsloth/Qwen2.5-Omni-7B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 6.96 GB
-
https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF/resolve/main/Qwen2.5-Omni-7B-UD-Q6_K_XL.gguf
- Command line
-
hf download hf://unsloth/Qwen2.5-Omni-7B-GGUF/Qwen2.5-Omni-7B-UD-Q6_K_XL.gguf
-
curl -L -o Qwen2.5-Omni-7B-UD-Q6_K_XL.gguf https://huggingface.co/unsloth/Qwen2.5-Omni-7B-GGUF/resolve/main/Qwen2.5-Omni-7B-UD-Q6_K_XL.gguf
6.96 GB
- Xet hash:
- ed709d81e80552ac8ac7799a04b102fdb765f8248630f7ca09018b8a2b3617bb
- Size of remote file:
- 6.96 GB
- SHA256:
- 37a40a164d16e7e3b52bcdbd0b79823720039663af7150d167461f4ef1b6c645
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