Instructions to use CodexCon-OS/Amethyst-Core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodexCon-OS/Amethyst-Core with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CodexCon-OS/Amethyst-Core",
	filename="amethyst-arc-1b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CodexCon-OS/Amethyst-Core 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 CodexCon-OS/Amethyst-Core:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CodexCon-OS/Amethyst-Core:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodexCon-OS/Amethyst-Core:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CodexCon-OS/Amethyst-Core: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 CodexCon-OS/Amethyst-Core:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CodexCon-OS/Amethyst-Core: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 CodexCon-OS/Amethyst-Core:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodexCon-OS/Amethyst-Core:Q4_K_M

Use Docker

docker model run hf.co/CodexCon-OS/Amethyst-Core:Q4_K_M

LM Studio
Jan

vLLM

How to use CodexCon-OS/Amethyst-Core with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodexCon-OS/Amethyst-Core"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodexCon-OS/Amethyst-Core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CodexCon-OS/Amethyst-Core:Q4_K_M

Ollama
How to use CodexCon-OS/Amethyst-Core with Ollama:
```
ollama run hf.co/CodexCon-OS/Amethyst-Core:Q4_K_M
```

Unsloth Studio

How to use CodexCon-OS/Amethyst-Core 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 CodexCon-OS/Amethyst-Core 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 CodexCon-OS/Amethyst-Core to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CodexCon-OS/Amethyst-Core to start chatting

Atomic Chat new
Docker Model Runner
How to use CodexCon-OS/Amethyst-Core with Docker Model Runner:
```
docker model run hf.co/CodexCon-OS/Amethyst-Core:Q4_K_M
```

Lemonade

How to use CodexCon-OS/Amethyst-Core with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CodexCon-OS/Amethyst-Core:Q4_K_M

Run and chat with the model

lemonade run user.Amethyst-Core-Q4_K_M

List all available models

lemonade list

Amethyst-Core / LICENSE

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	XLPHY Amethyst Core License Notice

	This repository distributes quantized GGUF model artifacts derived from the
	Google Gemma family for Project: XLPHY AI. These models are released under
	dual licensing depending on the base model.

	Model Artifact Terms

	### Gemma 3 Models (Amethyst Arc 1B)

	Amethyst Arc models derived from Gemma 3 are subject to the Gemma Terms of Use.
	You may use, copy, modify, distribute, and deploy these artifacts only as
	permitted by those terms.

	Official Terms: https://ai.google.dev/gemma/terms
	Upstream Reference: https://huggingface.co/google/gemma-3-1b-it

	### Gemma 4 Models (Amethyst Beam E2B)

	Amethyst Beam models derived from Gemma 4 are released under the Apache License 2.0.
	You may use, copy, modify, distribute, and deploy these artifacts in accordance
	with the terms of the Apache License 2.0.

	Official Terms: https://www.apache.org/licenses/LICENSE-2.0
	Upstream Reference: https://huggingface.co/google/gemma-4-e2b-it

	No Additional Rights

	Except where explicitly stated above, this repository does not grant rights
	beyond the respective licenses for each model artifact.

	Attribution

	Google, Gemma, and related marks are trademarks of Google LLC.
	No endorsement by Google DeepMind is implied.