Instructions to use admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use admiralakber/Qwen2.5-Omni-3B-Q8_0-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 admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
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 admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
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 admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF with Ollama:
ollama run hf.co/admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
- Lemonade
How to use admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.Qwen2.5-Omni-3B-Q8_0-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
Download qwen2.5-omni-3b-q8_0.gguf from admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 3.62 GB
-
https://huggingface.co/admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF/resolve/main/qwen2.5-omni-3b-q8_0.gguf
- Command line
-
hf download hf://admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF/qwen2.5-omni-3b-q8_0.gguf
-
curl -L -o qwen2.5-omni-3b-q8_0.gguf https://huggingface.co/admiralakber/Qwen2.5-Omni-3B-Q8_0-GGUF/resolve/main/qwen2.5-omni-3b-q8_0.gguf
3.62 GB
- Xet hash:
- e52b0c2df6fa7df851ba8db9fc0758c2754c5ecd16b5d4fd869d184a08ae23e2
- Size of remote file:
- 3.62 GB
- SHA256:
- fa355cbb90e61403a24584384dd67dc8073466188b98d48be7e9a5485b45227a
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