Text Generation
GGUF
English
Gemma 3
quantized
vision
edge-ai
local-first
xlphy
codexcon
1b
4b
12b
27b
imatrix
conversational
Instructions to use CodexCon-OS/Amethyst-Core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
| license: | |
| - gemma | |
| - apache-2.0 | |
| language: | |
| - en | |
| base_model: | |
| - google/gemma-3-1b-it | |
| - google/gemma-4-e2b-it | |
| tags: | |
| - Gemma 3 | |
| - gguf | |
| - quantized | |
| - vision | |
| - text-generation | |
| - edge-ai | |
| - local-first | |
| - xlphy | |
| - codexcon | |
| - 1b | |
| - 4b | |
| - 12b | |
| - 27b | |
| # ๐ฎ XLPHY Amethyst (Gemma Series) for Project: XLPHY AI | |
| XLPHY Amethyst is a suite of high-efficiency, local-first AI models optimized specifically for the Project: XLPHY AI ecosystem. These models are repackaged and quantized to provide a premium, low-latency, and multimodal experience for autonomous agents and sovereign AI applications. | |
| > **Developer Note:** These are optimized derivatives of the Google Gemma 3 series, rebranded and tuned for seamless integration within the Project: XLPHY AI autonomous agent architecture. | |
| ## ๐ง Model Selection | |
| The Amethyst series is built for Project: XLPHY AI and is divided into four "Gemstone Tiers." Each tier is available in `Q4_K_M`, `Q5_K_M`, and `Q6_K` quantization levels. | |
| | File Name (Template) | Tier Identity | Base Engine | Primary Purpose | License | Available Quants | | |
| | --- | --- | --- | --- | --- | --- | | |
| | `amethyst-arc-1b-[quant].gguf` | arc | Gemma 3 1B IT | Ultra-fast local execution and IoT. | Gemma | `Q4_K_M` | | |
| | `amethyst-beam-e2b-[quant].gguf` | Core | Gemma 4 E2B IT | Main driver with Vision support. | Apache 2.0 | `Q4_K_M`, `Q6_K`, `Q8_0` | | |
| ## ๐ฆ Quantization Guide | |
| - **Q4_K_M (Low):** Fastest and most memory-efficient. Ideal for mobile and entry-level hardware. | |
| - **Q5_K_M (Medium):** The "sweet spot" for Amethyst, with minimal quality differences from the original model. | |
| - **Q6_K (High):** Near-lossless performance for users who prioritize maximum accuracy. | |
| ## ๐ ๏ธ Implementation & Runtime | |
| Designed for the Project: XLPHY AI "Offline-First" philosophy. Best executed via: | |
| - **XLPHY Desktop App** (Native Integration) | |
| - **`llama.cpp` / `llama-cli`** | |
| - Any GGUF-compatible inference engine supporting Gemma 3 and Gemma 4 | |
| ## ๐ Checksums (SHA256) | |
| To ensure file integrity during the XLPHY automated download process: | |
| | Tier | Q4_K_M | Q6_K | Q8_0 | mmproj | | |
| | --- | --- | --- | --- | --- | | |
| | Arc (1B) | `12bf0fff8815d5f73a3c9b586bd8fee8e7b248c935de70dec367679873d0f29d` | `X` | `X` | `X` | | |
| | Beam (E2B) | `6c950d754366dd8b372fd17a40497ba5f130a46d833b4c5bccc9f6bb6382ce1e` | `5a352f63b59e1b37bac5071e3aed65e8954883a1625bf6676bf9aa2381295beb` | `6febf73c7c6b44c550ba857ceca3788262ce2272603ca16b1f2609556205dea3` | `b23366bec60eda708aa54816c8f72dfdfedd5c5ccbe4e7db500aa7724bd14b20` | | |
| ## โ๏ธ Attribution & Licensing | |
| These files are redistributed/repackaged quantized derivatives of the Google Gemma family. | |
| - **Original Architecture:** Developed by Google DeepMind | |
| - **Optimization:** Repackaged by CodexCon Digital Solutions for Project: XLPHY AI | |
| - **Amethyst Arc (Gemma 3):** Gemma Terms of Use | |
| - **Amethyst Beam (Gemma 4):** Apache License 2.0 | |
| ## โ ๏ธ Limitations & Safety | |
| - **Hallucinations:** Like all LLMs, these models may produce incorrect information. | |
| - **Human-in-the-loop:** Always validate technical outputs, especially for vision-based tasks or critical code. | |
| - **Non-Critical Use:** Not intended for medical, legal, or other high-stakes safety-critical applications. | |
| --- | |
| Developed by **CodexCon** | Lead Founder: **Cid Cruz** | |