Instructions to use guicybercode/Tucano-630m-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use guicybercode/Tucano-630m-mlx-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("guicybercode/Tucano-630m-mlx-4bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use guicybercode/Tucano-630m-mlx-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "guicybercode/Tucano-630m-mlx-4bit" --prompt "Once upon a time"
- Atomic Chat
Tucano-630m MLX 4-bit
Quantized versions of TucanoBR/Tucano-630m, a Portuguese language model by TucanoBR (Nicholas Kluge Corrêa, Aniket Sen, Sophia Falk, Shiza Fatimah; paper: https://arxiv.org/abs/2411.07854). All credit for the model goes to the original authors; this repository only contains a format conversion. 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 | Quantization | Size |
|---|---|---|
| model.safetensors | 4-bit (group size 64), non-quantized tensors in float16 | 355 MB (354639240 bytes) |
How this was made
mlx-lm 0.32.2 with mlx 0.32.4 (CPU backend, Linux x86_64):
mlx_lm.convert --hf-path TucanoBR/Tucano-630m --mlx-path Tucano-630m-mlx-4bit -q --q-bits 4 --dtype float16
--dtype float16 was used because, on the MLX CPU backend on Linux, a bfloat16 conversion produced only <unk> tokens in testing, while float16 produced normal text. Not tested on Apple Silicon.
Usage
pip install mlx-lm
mlx_lm.generate --model guicybercode/Tucano-630m-mlx-4bit --prompt "A capital do Brasil é" --max-tokens 32 --temp 0
Smoke test
Prompt: A capital do Brasil é, 32 tokens, temperature 0, run on the MLX CPU backend on Linux. Only a load-and-generate check, not an evaluation.
também a cidade mais populosa da América Latina. A cidade do Rio de Janeiro é um importante centro financeiro, cultural e sede de diversos órgãos governamentais. A cidade
No benchmarks were run on this quantized model. See the original model card for the authors' evaluations and intended use.
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TucanoBR/Tucano-630m