Instructions to use OusiaResearch/Aureth-4B-Qwen3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use OusiaResearch/Aureth-4B-Qwen3.5 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="OusiaResearch/Aureth-4B-Qwen3.5", filename="Qwen3.5-4B-Base.F16-mmproj.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use OusiaResearch/Aureth-4B-Qwen3.5 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf OusiaResearch/Aureth-4B-Qwen3.5:F16 # Run inference directly in the terminal: llama-cli -hf OusiaResearch/Aureth-4B-Qwen3.5:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf OusiaResearch/Aureth-4B-Qwen3.5:F16 # Run inference directly in the terminal: llama-cli -hf OusiaResearch/Aureth-4B-Qwen3.5: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 OusiaResearch/Aureth-4B-Qwen3.5:F16 # Run inference directly in the terminal: ./llama-cli -hf OusiaResearch/Aureth-4B-Qwen3.5: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 OusiaResearch/Aureth-4B-Qwen3.5:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf OusiaResearch/Aureth-4B-Qwen3.5:F16
Use Docker
docker model run hf.co/OusiaResearch/Aureth-4B-Qwen3.5:F16
- LM Studio
- Jan
- Ollama
How to use OusiaResearch/Aureth-4B-Qwen3.5 with Ollama:
ollama run hf.co/OusiaResearch/Aureth-4B-Qwen3.5:F16
- Unsloth Studio new
How to use OusiaResearch/Aureth-4B-Qwen3.5 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OusiaResearch/Aureth-4B-Qwen3.5 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for OusiaResearch/Aureth-4B-Qwen3.5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OusiaResearch/Aureth-4B-Qwen3.5 to start chatting
- Docker Model Runner
How to use OusiaResearch/Aureth-4B-Qwen3.5 with Docker Model Runner:
docker model run hf.co/OusiaResearch/Aureth-4B-Qwen3.5:F16
- Lemonade
How to use OusiaResearch/Aureth-4B-Qwen3.5 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OusiaResearch/Aureth-4B-Qwen3.5:F16
Run and chat with the model
lemonade run user.Aureth-4B-Qwen3.5-F16
List all available models
lemonade list
File size: 1,301 Bytes
84e08c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | {
"image_processor": {
"data_format": "channels_first",
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Qwen2VLImageProcessorFast",
"image_std": [
0.5,
0.5,
0.5
],
"merge_size": 2,
"patch_size": 16,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 16777216,
"shortest_edge": 65536
},
"temporal_patch_size": 2
},
"processor_class": "Qwen3VLProcessor",
"video_processor": {
"data_format": "channels_first",
"default_to_square": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": true,
"fps": 2,
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"max_frames": 768,
"merge_size": 2,
"min_frames": 4,
"patch_size": 16,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"size": {
"longest_edge": 234881024,
"shortest_edge": 4096
},
"temporal_patch_size": 2,
"video_processor_type": "Qwen3VLVideoProcessor"
}
}
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