Instructions to use Prosodia/Prosodia_1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Prosodia/Prosodia_1-gguf 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 Prosodia/Prosodia_1-gguf:F16 # Run inference directly in the terminal: llama cli -hf Prosodia/Prosodia_1-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Prosodia/Prosodia_1-gguf:F16 # Run inference directly in the terminal: llama cli -hf Prosodia/Prosodia_1-gguf:F16
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 Prosodia/Prosodia_1-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf Prosodia/Prosodia_1-gguf:F16
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 Prosodia/Prosodia_1-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Prosodia/Prosodia_1-gguf:F16
Use Docker
docker model run hf.co/Prosodia/Prosodia_1-gguf:F16
- LM Studio
- Jan
- Ollama
How to use Prosodia/Prosodia_1-gguf with Ollama:
ollama run hf.co/Prosodia/Prosodia_1-gguf:F16
- Unsloth Desktop
- Pi
How to use Prosodia/Prosodia_1-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Prosodia/Prosodia_1-gguf:F16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Prosodia/Prosodia_1-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Prosodia/Prosodia_1-gguf with Docker Model Runner:
docker model run hf.co/Prosodia/Prosodia_1-gguf:F16
- Lemonade
How to use Prosodia/Prosodia_1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Prosodia/Prosodia_1-gguf:F16
Run and chat with the model
lemonade run user.Prosodia_1-gguf-F16
List all available models
lemonade list
- Hermes Agent
How to use Prosodia/Prosodia_1-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Prosodia/Prosodia_1-gguf:F16
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 Prosodia/Prosodia_1-gguf:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Prosodia/Prosodia_1-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Prosodia/Prosodia_1-gguf:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Prosodia/Prosodia_1-gguf:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Introduction
Prosodia is an organization dedicated to developing and transparently distributing open-source Portuguese language models. The Prosodia 1 is a 0.6b billion parameter Small Language Model (SLM) trained on publicly available data and released for community use. Built on the Qwen3 architecture, it represents our commitment to accessible AI development. It was the first of its kind and exists only as a proof we can do it with one single MI300X gpu in a record time of 7 days.
Training
This model was developed through a focused one-week effort with substantially limited computational resources compared to industry leaders. Its primary purpose is to demonstrate that through intelligent design, transparency, and community collaboration, it is possible to create high-quality Brazilian and Portuguese language models without massive infrastructure.
The training regimen utilized approximately 20 billion tokens for the base model and just under 1 billion tokens for instruction tuning and subsequent refinements. While these volumes are far from ideal, they serve as a rapid proof-of-concept that establishes a foundation for future, more comprehensive development.
Inference
The model is fully compatible with standard LLM deployment platforms and can be used and distributed across frameworks such as HuggingFace, vLLM, GGUF, and similar tools.
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