Instructions to use beza4588/TenaOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use beza4588/TenaOS with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="beza4588/TenaOS", filename="gemma-4-E4B-it-BF16.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 beza4588/TenaOS with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf beza4588/TenaOS:BF16 # Run inference directly in the terminal: llama-cli -hf beza4588/TenaOS:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf beza4588/TenaOS:BF16 # Run inference directly in the terminal: llama-cli -hf beza4588/TenaOS:BF16
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 beza4588/TenaOS:BF16 # Run inference directly in the terminal: ./llama-cli -hf beza4588/TenaOS:BF16
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 beza4588/TenaOS:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf beza4588/TenaOS:BF16
Use Docker
docker model run hf.co/beza4588/TenaOS:BF16
- LM Studio
- Jan
- vLLM
How to use beza4588/TenaOS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "beza4588/TenaOS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "beza4588/TenaOS", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/beza4588/TenaOS:BF16
- Ollama
How to use beza4588/TenaOS with Ollama:
ollama run hf.co/beza4588/TenaOS:BF16
- Unsloth Studio new
How to use beza4588/TenaOS 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 beza4588/TenaOS 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 beza4588/TenaOS to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for beza4588/TenaOS to start chatting
- Pi new
How to use beza4588/TenaOS with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf beza4588/TenaOS:BF16
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": "beza4588/TenaOS:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use beza4588/TenaOS with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf beza4588/TenaOS:BF16
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 beza4588/TenaOS:BF16
Run Hermes
hermes
- Docker Model Runner
How to use beza4588/TenaOS with Docker Model Runner:
docker model run hf.co/beza4588/TenaOS:BF16
- Lemonade
How to use beza4588/TenaOS with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull beza4588/TenaOS:BF16
Run and chat with the model
lemonade run user.TenaOS-BF16
List all available models
lemonade list
| license: gemma | |
| tags: | |
| - tenaos | |
| - gemma | |
| - gguf | |
| - llama.cpp | |
| - clinical | |
| language: | |
| - en | |
| base_model: google/gemma-4-E4B-it | |
| pipeline_tag: text-generation | |
| # TenaOS — Gemma 4 E4B Instruct (BF16 GGUF) | |
| `llama.cpp`-ready BF16 conversion of | |
| [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it), | |
| plus the audio `mmproj` projector. Used by | |
| [TenaOS](https://github.com/brookyale0512/TenaOS) for on-device clinical | |
| inference (text + voice, multimodal in a single pass). | |
| ## Contents | |
| | File | Size | Purpose | | |
| | --- | --- | --- | | |
| | `gemma-4-E4B-it-BF16.gguf` | ~15 GB | Full-precision GGUF for generation | | |
| | `mmproj-gemma-4-E4B-it-bf16.gguf` | ~946 MB | Multimodal projector for audio input | | |
| We standardize on **BF16 full precision**. No quantization in the | |
| production path. | |
| ## Usage | |
| ```bash | |
| hf download beza4588/TenaOS --local-dir ./models | |
| # launch llama-server (CUDA build): | |
| llama-server \ | |
| -m ./models/gemma-4-E4B-it-BF16.gguf \ | |
| --mmproj ./models/mmproj-gemma-4-E4B-it-bf16.gguf \ | |
| --host 0.0.0.0 --port 8000 -ngl 99 --jinja --alias gemma-4 | |
| ``` | |
| In TenaOS the docker image bind-mounts this directory at `/models`; see | |
| [`scripts/fetch-models.sh`](https://github.com/brookyale0512/TenaOS/blob/main/scripts/fetch-models.sh). | |
| ## License | |
| Inherits the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). | |
| TenaOS packaging is Apache 2.0. | |