Instructions to use cortexso/athene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/athene with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/athene", filename="athene-v2-chat-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cortexso/athene with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/athene:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/athene:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/athene:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/athene: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 cortexso/athene:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/athene: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 cortexso/athene:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/athene:Q4_K_M
Use Docker
docker model run hf.co/cortexso/athene:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/athene with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/athene" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/athene", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/athene:Q4_K_M
- Ollama
How to use cortexso/athene with Ollama:
ollama run hf.co/cortexso/athene:Q4_K_M
- Unsloth Studio new
How to use cortexso/athene 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 cortexso/athene 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 cortexso/athene to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/athene to start chatting
- Pi new
How to use cortexso/athene with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf cortexso/athene:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "cortexso/athene:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use cortexso/athene with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf cortexso/athene:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default cortexso/athene:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use cortexso/athene with Docker Model Runner:
docker model run hf.co/cortexso/athene:Q4_K_M
- Lemonade
How to use cortexso/athene with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/athene:Q4_K_M
Run and chat with the model
lemonade run user.athene-Q4_K_M
List all available models
lemonade list
| license: other | |
| pipeline_tag: text-generation | |
| tags: | |
| - cortex.cpp | |
| ## Overview | |
| Athene-V2-Chat-72B is an open-weight LLM that competes on par with GPT-4o across various benchmarks. It is currently ranked as the best open model on Chatbot Arena, where it outperforms GPT-4o-0513 (the highest-ranked GPT-4o model on Arena) in hard and math categories. It also matches GPT-4o-0513 in coding, instruction following, longer queries, and multi-turn conversations. | |
| Trained through RLHF with Qwen-2.5-72B-Instruct as the base model, Athene-V2-Chat-72B excels in chat, math, and coding. Additionally, its sister model, Athene-V2-Agent-72B, surpasses GPT-4o in complex function calling and agentic applications, further extending its capabilities. | |
| ## Variants | |
| | No | Variant | Cortex CLI command | | |
| | --- | --- | --- | | |
| | 1 | [Athene-72b](https://huggingface.co/cortexso/athene/tree/72b) | `cortex run athene:72b` | | |
| ## Use it with Jan (UI) | |
| 1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart) | |
| 2. Use in Jan model Hub: | |
| ```bash | |
| cortexhub/athene | |
| ``` | |
| ## Use it with Cortex (CLI) | |
| 1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart) | |
| 2. Run the model with command: | |
| ```bash | |
| cortex run athene | |
| ``` | |
| ## Credits | |
| - **Author:** Nexusflow | |
| - **Converter:** [Homebrew](https://homebrew.ltd/) | |
| - **Original License:** [Licence](https://huggingface.co/Nexusflow/Athene-V2-Chat/blob/main/Nexusflow_Research_License_.pdf) | |
| - **Papers:** [Athene V2 Blog](https://nexusflow.ai/blogs/athene-v2) |