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

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

Llama new
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

Aurvion / llama.cpp /ci /README.md

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# CI

This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to
cover hardware configurations that are not available from Github-hosted runners and/or require more computational
resource than normally available.

It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:

```bash
mkdir tmp

# CPU-only build
bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with CUDA support
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with SYCL support
source /opt/intel/oneapi/setvars.sh
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# etc.
```

# Adding self-hosted runners

- Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label
- Request a runner token from `ggml-org` (for example, via a comment in the PR or email)
- Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners))
- Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env

	# CI

	This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to
	cover hardware configurations that are not available from Github-hosted runners and/or require more computational
	resource than normally available.

	It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:

	```bash
	mkdir tmp

	# CPU-only build
	bash ./ci/run.sh ./tmp/results ./tmp/mnt

	# with CUDA support
	GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

	# with SYCL support
	source /opt/intel/oneapi/setvars.sh
	GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

	# with MUSA support
	GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

	# etc.
	```

	# Adding self-hosted runners

	- Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label
	- Request a runner token from `ggml-org` (for example, via a comment in the PR or email)
	- Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners))
	- Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env