Instructions to use beza4588/TenaOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

TenaOS

File size: 1,389 Bytes

b758bc0

---
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.