Instructions to use guicybercode/Tucano-630m-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 guicybercode/Tucano-630m-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 guicybercode/Tucano-630m-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf guicybercode/Tucano-630m-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf guicybercode/Tucano-630m-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf guicybercode/Tucano-630m-GGUF: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 guicybercode/Tucano-630m-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf guicybercode/Tucano-630m-GGUF: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 guicybercode/Tucano-630m-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf guicybercode/Tucano-630m-GGUF:Q4_K_M
Use Docker
docker model run hf.co/guicybercode/Tucano-630m-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use guicybercode/Tucano-630m-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "guicybercode/Tucano-630m-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guicybercode/Tucano-630m-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/guicybercode/Tucano-630m-GGUF:Q4_K_M
- Ollama
How to use guicybercode/Tucano-630m-GGUF with Ollama:
ollama run hf.co/guicybercode/Tucano-630m-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use guicybercode/Tucano-630m-GGUF with Docker Model Runner:
docker model run hf.co/guicybercode/Tucano-630m-GGUF:Q4_K_M
- Lemonade
How to use guicybercode/Tucano-630m-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull guicybercode/Tucano-630m-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Tucano-630m-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Tucano-630m GGUF
I wanted to run TucanoBR/Tucano-630m, a Portuguese language model, on an ordinary CPU without Python or a GPU. This repo has the model converted to GGUF (Q4_K_M, Q8_0 and F16) so it runs in llama.cpp and Ollama.
llama-cli -hf guicybercode/Tucano-630m-GGUF:Q4_K_M -p "A capital do Brasil é" -n 48
The line above uses llama.cpp's -hf download (tested 2026-10-22 with a llama.cpp build from source).
All credit for the model goes to the original authors, TucanoBR (Nicholas Kluge Corrêa, Aniket Sen, Sophia Falk, Shiza Fatimah; paper: https://arxiv.org/abs/2411.07854). I only converted the format. License: Apache-2.0, same as the original.
Versão quantizada do modelo TucanoBR/Tucano-630m para uso local; todo o crédito é dos autores originais.
Files
| File | Type | Size |
|---|---|---|
| Tucano-630m-Q4_K_M.gguf | Q4_K_M | 387 MB (386980864 bytes) |
| Tucano-630m-Q8_0.gguf | Q8_0 | 671 MB (670583808 bytes) |
| Tucano-630m-F16.gguf | F16 | 1.26 GB (1261390848 bytes) |
How these were made
llama.cpp (master, built on Linux x86_64 CPU):
python convert_hf_to_gguf.py Tucano-630m --outtype f16 --outfile Tucano-630m/Tucano-630m-F16.gguf
llama-quantize Tucano-630m-F16.gguf Tucano-630m-Q4_K_M.gguf Q4_K_M
llama-quantize Tucano-630m-F16.gguf Tucano-630m-Q8_0.gguf Q8_0
Usage
llama-completion -m Tucano-630m-Q4_K_M.gguf -p "A capital do Brasil é" -n 48 --temp 0
Smoke test
Prompt: A capital do Brasil é, 48 tokens, temperature 0, raw completion (no chat template). This only checks that the files load and produce Portuguese text; it is not an evaluation.
Q4_K_M:
A capital do Brasil é a cidade mais populosa da América Latina, sendo definida como sede de 12 das 15 maiores cidades dos Estados Unidos (AL), além de ser o centro da maior região do mundo: a América do Sul. Com uma população estimada em 2015 de 460,
Q8_0:
A capital do Brasil é a cidade mais populosa da América Latina, sendo a sexta maior cidade das Américas e a quinta cidade mais populosa da União Europeia. Depois de Paris, é a única cidade europeia a ter um status administrativo oficial de prefeito (que poderia ser revogado se
No benchmarks were run on these quantized files. See the original model card for the authors' evaluations and intended use.
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