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

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 Studio

How to use OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion 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 OmnipotentFool/Aurvion to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OmnipotentFool/Aurvion to start chatting

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 /conversion /maincoder.py

OmnipotentFool

Add files using upload-large-folder tool

0aa2ecf verified about 2 months ago

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423 Bytes

	from __future__ import annotations

	from .base import ModelBase, TextModel, gguf


	@ModelBase.register("MaincoderForCausalLM")
	class MaincoderModel(TextModel):
	model_arch = gguf.MODEL_ARCH.MAINCODER

	def set_gguf_parameters(self):
	super().set_gguf_parameters()

	if (head_dim := self.hparams.get("head_dim")) is not None:
	self.gguf_writer.add_rope_dimension_count(head_dim)