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How to load the Brazilian legislative model and generate text with LLM Legislativo

Download the published safetensors model and tokenizer from Hugging Face and run its inference code.

  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Authorization notice. LLM Legislativo is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA researcher or developer who wants to test how a small model trained only on Brazilian legislative text writes, without training anything.

The problemReproducing a language model's output needs its weights, tokenizer, and inference code in one place.

The outcomeGenerated Portuguese legislative-style text from the released model, produced with the repo's own inference code.

What you need first

  • A Python environment (the README install section is cut off in the source, so exact setup steps are unconfirmed)
  • Internet access to Hugging Face Hub and GitHub
  • Enough disk space for the Parquet release if you download corpus files (the raw TAR archives total over 360 GiB)
  • Basic familiarity with PyTorch and Hugging Face datasets for the model and corpus steps

Steps

  1. Open the model page at https://huggingface.co/BaruqRodrigues/llm-legislativo and confirm it has model.safetensors, the tokenizer, and the inference code.
  2. Clone the repo: (inferred) git clone https://github.com/BaruqueRodrigues/llm-legislativo (inferred, check the README)
  3. Install dependencies with pip install -r requirements-cpu.lock. (inferred, check the README)
  4. Run the generation code in src/legislm/evaluation/ or the inference code shipped with the model, following its entry point (inferred, the exact command is not in the README).

Prompt for your agent

Using the repo https://github.com/BaruqueRodrigues/llm-legislativo and the Hugging Face model BaruqRodrigues/llm-legislativo, generate a short Portuguese text starting from the prompt <prompt>. Output the generated text and the exact command used. Verify that the tokenizer round-trips the prompt before generating.

Caveat. The model is trained on legislative text and its README gives no chat or instruction-tuning claims, so output quality should be judged by the reported metrics rather than assumed.

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