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 / NOTICE

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	XLPHY Amethyst Core Model Distribution Notice

	This distribution package contains rebranded and quantized GGUF model files:

	Gemma 3 Models (Amethyst Arc):
	- amethyst-arc-1b-Q4_K_M.gguf
	- amethyst-arc-1b-Q5_K_M.gguf
	- amethyst-arc-1b-Q6_K.gguf

	Gemma 4 Models (Amethyst Beam):
	- amethyst-beam-e2b-Q4_K_M.gguf
	- amethyst-beam-e2b-Q5_K_M.gguf
	- amethyst-beam-e2b-Q6_K.gguf

	These files are derivative/packaged artifacts based on upstream Gemma models:

	- google/gemma-3-1b-it (Licensed under Gemma Terms of Use)
	- google/gemma-4-e2b-it (Licensed under Apache License 2.0)

	Upstream model provider: Google DeepMind.

	Dual Licensing

	This package distributes models under dual licensing:
	- Gemma 3 derivatives are redistributed under Gemma Terms of Use
	- Gemma 4 derivatives are redistributed under Apache License 2.0

	See the LICENSE file in this directory for detailed license information:
	https://ai.google.dev/gemma/terms (Gemma Terms)
	https://www.apache.org/licenses/LICENSE-2.0 (Apache License 2.0)

	Attribution and notices are provided here for transparency and compliance.
	This NOTICE file does not modify applicable license terms.

	No endorsement by the upstream provider is implied.