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bsana1
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Model Details
Model Provider
bsana1
Model Tree
pierreguillou/gpt2-small-portuguese
Input Modalities
Output Modalities
Supported Functionality
This is a completion model, not an instruction-following one — it continues whatever text it's given, in cordel style. It is not chat-tuned; there is no chat template. (Instruction-following is what the separate v2 model, a LoRA adapter on a different, instruct-tuned base, is for.)
Text completion in the style of cordel poetry: give it an opening line or a theme and let it continue. Works best primed with a stanza or two of real cordel first (see the demo's few-shot prompt, or the usage example below).
A small hobby/learning project, not a production model. Fine-tuned on a single ~150 KB corpus (17 public-domain folhetos) — not evaluated for factual accuracy, safety, or anything outside creative Portuguese poetry completion. It does not follow instructions; asking it a question will just get you more cordel-flavored text, not an answer.
Inherits the base model's general limitations (GPorTuguese-2, 124M params, trained mostly on Wikipedia prose — cordel is a big style shift from that). The fine-tuning corpus is small and draws on 19th/20th century Northeastern Brazilian folk material, so outputs can reflect the period's language, social attitudes, and references. Not evaluated for bias.
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "bsana1/arrodeio-gpt2-cordel"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)model.eval()# Priming with a real stanza first helps it lock into cordel meter/rhyme.prompt = ("Eram doze cavaleiros\nhomens muito valorosos,\ndestemidos, animosos,\n""entre todos os guerreiros.\n\na vida no sertão\n")ids = tok(prompt, return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=110, do_sample=True, temperature=0.8,top_k=40, repetition_penalty=1.3, pad_token_id=tok.eos_token_id)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
data/corpus.txt (~157k chars, ~150 KB): 17 public-domain cordel folhetos
scraped from pt.wikisource.org (scripts/fetch_cordel.py), restricted to
long-dead authors (life + 70 years) — e.g. Leandro Gomes de Barros
(d. 1918), Silvino Pirauá de Lima (d. 1913) — plus a set of traditional
ditados populares (folk sayings).
Continued pretraining (plain next-token prediction), not instruction tuning — only the top transformer blocks are unfrozen, so the model keeps its grip on Portuguese and mainly picks up cordel style/rhythm rather than overfitting to the small corpus.
Corpus tokenized with the base model's own GPT-2 BPE tokenizer, chunked into fixed-length windows for training.
Trained on a CPU (Apple Silicon, 8 GB) in about 25 minutes. Final model size: ~480 MB (full GPT-2 small checkpoint, not an adapter).
No formal benchmark — evaluated qualitatively against the other stages of the same project (from-scratch model, Tucano+LoRA). See samples/comparison.md in the GitHub repo for the same prompts run through every stage side by side.
Training took ~25 minutes on a single laptop CPU — negligible compute.
GPT-2 small (124M params, 12 layers, 12 heads, 768 hidden size), causal language modeling objective (next-token prediction).
Apple Silicon laptop CPU (8 GB RAM), local training.
PyTorch, 🤗 Transformers.
Questions/issues: open one on github.com/bsana1/arrodeio-llm.