garras

Available Files

For models under 1B parameters, Q8 is the recommended quantization. Q4 is too lossy at this scale and noticeably degrades output quality.

Model Details

Architecture

Garras is a standard GPT-2 decoder-only transformer with pre-norm layer normalization and weight tying between the token embedding and the language model head.

Tokenizer

A custom bilingual BPE tokenizer trained on samples from all four data domains, ensuring proper subword coverage for both English and Portuguese.

Special tokens:

Training Data

Garras was pretrained on a curated mix of four prose/web streams. No QA pairs or instruction data were used during pretraining — those are reserved for fine-tuning.

Training Procedure

Fine-Tuning Roadmap

Garras is designed as a base model. The following fine-tuning stages are planned:

Intended Use

Direct Use

• Bilingual text generation (English and Brazilian Portuguese)
• Foundation for fine-tuning on domain-specific tasks
• Research and experimentation with small-scale LLMs
• Educational purposes — understanding transformer training from scratch

Downstream Use

• Chat assistants (after instruction fine-tuning)
• Agricultural advisory tools
• Bilingual content generation
• Domain-specific QA systems

Out-of-Scope Use

• This is a 125M parameter base model — it will not match the quality of larger instruction-tuned models
• Not suitable for production use without fine-tuning
• Should not be used for medical, legal, or safety-critical applications
• May generate biased, incorrect, or nonsensical text

Bias, Risks, and Limitations

• Scale limitations: At 125M parameters, Garras has limited world knowledge and reasoning capability compared to larger models.
• Training data bias: The model inherits biases present in web-crawled data (FineWeb, mC4, etc.) and public-domain books.
• Language balance: Despite the 30% Portuguese allocation, English performance will generally be stronger due to the larger English data ecosystem.
• Repetition: Small models are prone to repetitive generation. Use the recommended sampling parameters (temperature 0.85, repeat_penalty 1.35, top_k 40) to mitigate this.
• Hallucination: The model will confidently generate plausible-sounding but factually incorrect text.

Environmental Impact

• Hardware: 1× NVIDIA A100 40GB
• Cloud Provider: Google Colab
• Training time: ~12-24 hours (estimated for 2.2B tokens)
• Carbon footprint: Minimal for this model scale

Technical Notes

GGUF Conversion

The GGUF files were produced using llama.cpp's convert_hf_to_gguf.py. The custom BPE tokenizer required a patch to get_vocab_base_pre() to register the tokenizer hash — it maps to "gpt2" pre-tokenizer type since both use ByteLevel encoding.

Ollama Sampling Parameters

The Modelfiles include tuned sampling parameters for small model inference:

Citation

@misc{garras2025,
  author       = {hipnologo},
  title        = {Garras: A Bilingual English/Portuguese LLM Trained from Scratch},
  year         = {2025},
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/hipnologo/garras}
}

Author

hipnologo — GitHub

Built with ❤️ and 🐾 """

api = HfApi() api.upload_file( path_or_fileobj=readme_content.encode("utf-8"), path_in_repo="README.md", repo_id=HF_REPO_NAME, ) print(f"✅ Model card updated → https://huggingface.co/{HF_REPO_NAME}")