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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Atomic Chat

Verdenherre commited on Jun 29

Commit

fdcab72

verified ·

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@@ -58,8 +58,8 @@ To ensure file integrity during the XLPHY automated download process:
 | Tier | Q4_K_M | Q5_K_M | Q6_K | mmproj |
 | --- | --- | --- | --- |
-| Arc (1B) | `12bf0fff8815d5f73a3c9b586bd8fee8e7b248c935de70dec367679873d0f29d` | `X` | `X` | 'X' |
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 ## ⚖️ Attribution & Licensing

 | Tier | Q4_K_M | Q5_K_M | Q6_K | mmproj |
 | --- | --- | --- | --- |
+| Arc (1B) | `12bf0fff8815d5f73a3c9b586bd8fee8e7b248c935de70dec367679873d0f29d` | `X` | `X` | `X` |
+| Beam (E2B) | `cded614c9b24be92e5a868d2ba38fb24e15dfea34fc650193c475a6debc233a7` | `43b6d9cfc1108e172b9ff99759ce7c2052bbed5dd7c4b4675ca63a04b6ed8dfc` | `b4c977371027c423ba6e36c7ca6e31e11803853224046f62d94a24a827e4f041` | `3397f2e15859d268dc91782e92e70c485c7780448d57d622d145374390168f71` |
 ## ⚖️ Attribution & Licensing