Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
Instructions to use OmnipotentFool/Aurvion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
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 OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
|
Download llama.cpp/docs/backend/snapdragon/linux.md from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 2.16 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/docs/backend/snapdragon/linux.md
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/docs/backend/snapdragon/linux.md
-
curl -L -o linux.md https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/docs/backend/snapdragon/linux.md
2.16 kB
| # Snapdragon-based Linux devices | |
| ## Docker Setup | |
| The easiest way to build llama.cpp for a Snapdragon-based Linux device is using the toolchain Docker image (see [github.com/snapdragon-toolchain](https://github.com/snapdragon-toolchain)). | |
| This image includes OpenCL SDK, Hexagon SDK, CMake, and the ARM64 Linux cross-compilation toolchain. | |
| Cross-compilation is supported on **Linux X86** hosts. The resulting binaries are deployed to and run on the target **Qualcomm Snapdragon ARM64 Linux** device. | |
| ``` | |
| ~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.1 | |
| [d]/> cd /workspace | |
| ``` | |
| Note: The rest of the **Linux** build process assumes that you're running inside the toolchain container. | |
| ## How to Build | |
| Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets: | |
| ``` | |
| [d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json . | |
| [d]/workspace> cmake --preset arm64-linux-snapdragon-release -B build-snapdragon | |
| [d]/workspace> cmake --build build-snapdragon -j $(nproc) | |
| ``` | |
| To generate an installable "package" simply use cmake --install, then zip it: | |
| ``` | |
| [d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon | |
| [d]/workspace> zip -r pkg-snapdragon.zip pkg-snapdragon | |
| ``` | |
| ## How to Install | |
| For this step, you will deploy the built binaries and libraries to the target Linux device. Transfer `pkg-snapdragon.zip` to the target device, then unzip it and set up the environment variables: | |
| ``` | |
| $ unzip pkg-snapdragon.zip | |
| $ cd pkg-snapdragon | |
| $ export LD_LIBRARY_PATH=./lib | |
| $ export ADSP_LIBRARY_PATH=./lib | |
| ``` | |
| At this point, you should also download some models onto the device: | |
| ``` | |
| $ wget https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_0.gguf | |
| ``` | |
| ## How to Run | |
| Next, since we have setup the environment variables, we can run the llama-cli with the Hexagon backends: | |
| ``` | |
| $ ./bin/llama-cli -m Llama-3.2-3B-Instruct-Q4_0.gguf --device HTP0 -ngl 99 -p "what is the most popular cookie in the world?" | |
| ``` | |