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Model: augustoafleal/gpt2-ptbr-218m
Source: Original Platform

.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+tokenizer.model filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: pt
+license: mit
+tags:
+- gpt2
+- portuguese
+- causal-lm
+- pytorch
+- safetensors
+pipeline_tag: text-generation
+---
+
+# gpt2-ptbr-218m
+
+Portuguese GPT-2-like autoregressive language model trained from scratch.
+
+## Training pipeline
+
+The released model, `gpt2-ptbr-218m`, is the final checkpoint of a three-stage pipeline:
+
+1. **Pretraining** on Portuguese Wikipedia.
+2. **Supervised Fine-Tuning** on Alpaca PT-BR.
+3. **Supervised Fine-Tuning** on Canarim-Instruct-PTBR.
+
+The released checkpoint corresponds to the final instruction-tuned model.
+
+## Model description
+
+- **Architecture:** GPT-2 (decoder-only transformer)
+- **Parameters:** 218,040,320 trainable unique parameters (weight-tying between token embedding and output projection; zero biases added for Hugging Face compatibility)
+- **Layers:** 16
+- **Attention heads:** 16
+- **Embedding dimension:** 1024
+- **Vocabulary:** 16,000 tokens (SentencePiece BPE)
+- **Sequence length:** 256 tokens
+- **Activation:** GELU
+- **Dropout:** 0.1
+
+> **Note on parameter count:** During export, the weight-tying between the token embedding and the language model head is preserved, and zero-initialized bias tensors are added for compatibility with Hugging Face's `GPT2LMHeadModel`. These biases are not part of the original trained model and do not affect behavior.
+
+## Datasets
+
+- **Portuguese Wikipedia corpus** — used for autoregressive pretraining.
+- **Alpaca PT-BR** — Portuguese instruction-following dataset derived from Stanford Alpaca (Taori et al., 2023). Used for the first SFT stage.
+- **Canarim-Instruct-PTBR** — Portuguese instruction-following dataset by Maicon Domingues (Domingues, 2023). Used for the second SFT stage.
+
+## Tokenizer
+
+- **Type:** SentencePiece BPE
+- **Vocabulary:** 16,000 tokens
+- **Special tokens:** `<unk>` = 0, `<bos>` = 1, `<eos>` = 2, `<pad>` = 3
+- **Pre-tokenizer:** Metaspace (SentencePiece native)
+- **Model max length:** 256 tokens
+
+The tokenizer was trained from scratch on the Portuguese Wikipedia corpus.
+
+## Training details
+
+- **Type:** sft_response_only
+- **Best validation loss:** 2.1538288593292236
+- **Training steps:** 1050
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "augustoafleal/gpt2-ptbr-218m"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "A inteligência artificial é"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+output = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7,
+    top_k=40,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- The model may generate factual errors.
+- The model may repeat phrases.
+- The model may fail to follow instructions exactly.
+- The context length is limited to 256 tokens.
+- The model was trained on limited data compared to modern LLMs.
+- This model is not suitable for high-stakes use without human validation.
+
+## Dataset citations
+
+```
+Stanford Alpaca: Taori et al., 2023. https://github.com/tatsu-lab/stanford_alpaca
+Canarim-Instruct-PTBR: Domingues, 2023. https://huggingface.co/datasets/dominguesm/Canarim-Instruct-PTBR
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{gpt2ptbr218m,
+  title        = {gpt2-ptbr-218m: A Portuguese GPT-2-like Autoregressive Language Model},
+  author       = {Augusto Antônio Fontanive Leal},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/augustoafleal/gpt2-ptbr-218m}}
+}
+```

config.json

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+{
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "model_type": "gpt2",
+  "vocab_size": 16000,
+  "n_positions": 256,
+  "n_ctx": 256,
+  "n_embd": 1024,
+  "n_layer": 16,
+  "n_head": 16,
+  "n_inner": 4096,
+  "activation_function": "gelu",
+  "resid_pdrop": 0.1,
+  "embd_pdrop": 0.1,
+  "attn_pdrop": 0.1,
+  "layer_norm_epsilon": 1e-05,
+  "initializer_range": 0.02,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "use_cache": true,
+  "tie_word_embeddings": true
+}

generation_config.json

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+{
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "bos_token_id": 1,
+  "max_length": 256,
+  "do_sample": true,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0
+}

model.safetensors

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+size 938307344

special_tokens_map.json

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+{
+  "unk_token": "<unk>",
+  "bos_token": "<bos>",
+  "eos_token": "<eos>",
+  "pad_token": "<pad>"
+}

tokenizer.model

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+size 492472

tokenizer_config.json

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+{
+  "add_prefix_space": null,
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "extra_special_tokens": [
+    "<bos>",
+    "<eos>",
+    "<pad>"
+  ],
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "model_type": "gpt2"
+}