This model is a continual pretrained version of unsloth/gemma-2-27b-bnb-4bit on Amharic text data. The tokenizer was expanded from the original 256k vocabulary to better support Amharic script, and the model underwent continual pretraining with ~2B tokens of Amharic corpus data.

Note: This is an early research version open-sourced solely for research purposes on low-resource language LLMs. For production-ready models with multimodality features, please visit platform.addisassistant.com.

Quick start

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "b1n1yam/gemma-2-27b-amharic-cpt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "አዲስ አበባ የኢትዮጵያ"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training procedure

This model underwent continual pretraining (CPT) on Amharic text data:

Tokenizer expansion: Extended the original Gemma 2 tokenizer to better cover Amharic script
Training data: ~2B tokens of Amharic corpus, including transcribed audio sources
Training approach: Continual pretraining to adapt the model to Amharic language

Framework versions

TRL: 0.24.0
Transformers: 4.57.2
PyTorch: 2.6.0.dev20241112+cu121
Datasets: 3.6.0
Tokenizers: 0.22.1

Citation

If you use this model, please cite:

bibtex

@misc{daniel2024addisai,
  title = {Addis AI: Continual Pretrained Gemma 2 27B for Amharic},
  author = {Daniel, Biniyam},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/b1n1yam/gemma-2-27b-amharic-cpt}}
}