Komodo 7b Base

🚀 Model Card for Komodo-7B-Base

Komodo-7B-Base is a large language model developed through incremental pretraining and vocabulary expansion on Llama-2-7B-Base. It can handle English, Indonesian, and 11 regional languages of Indonesia.

Disclaimer: This is not an instruction-tuned model. Further fine-tuning is required for downstream tasks. For example, people often use the Alpaca dataset for further fine-tuning on the Llama-2-7B-Base model. Therefore, there is no prompt template for this model.

🚀 Quick Start

Since this is a gated model, you need to log in to your HF account before using it. Here's how you can do it. You can get the HF Token from your profile (Profile -> Settings -> Access Tokens).

import huggingface_hub
huggingface_hub.login("YOUR_HF_TOKEN")

Once logged in, you can start downloading and loading the model & tokenizer. We wrote a custom decoding function for Komodo-7B, so we need to pass trust_remote_code=True. The code also works without this parameter, but the decoding process may not work as expected.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda:0" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained("Yellow-AI-NLP/komodo-7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Yellow-AI-NLP/komodo-7b-base", trust_remote_code=True)
model = model.to(device)

Then, you can try using the model.

full_prompt = "Candi borobudur adalah"

tokens = tokenizer(full_prompt, return_tensors="pt").to(device)
output = model.generate(tokens["input_ids"], eos_token_id=tokenizer.eos_token_id)

print(tokenizer.decode(output[0], skip_special_tokens=True))
# Candi borobudur adalah candi yang terletak di Magelang, Jawa Tengah.

✨ Features

• Developed through incremental pretraining and vocabulary expansion on Llama-2-7B-Base.
• Can handle English, Indonesian, and 11 regional languages of Indonesia.

📚 Documentation

Model Details

Model Description

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Technical Details

Model Architecture and Objective

Komodo-7B is a decoder model using the Llama-2 architecture.

Tokenizer Details

Recognizing the importance of linguistic diversity, we focused on enhancing our language model's proficiency in both Indonesian and regional languages. To achieve this, we systematically expanded the tokenizer's vocabulary by identifying and incorporating approximately 2,000 frequently used words specific to Indonesian and 1,000 words for regional languages that were absent in the Llama-2 model.

The standard method for enhancing a vocabulary typically involves developing a new tokenizer and integrating it with the existing one. This technique has shown impressive results in projects like Chinese-LLaMA and Open-Hathi. The effectiveness of this strategy can be attributed to the significant linguistic distinctions between languages such as Chinese and Hindi when compared to English. In contrast, the Indonesian language employs the same Latin script as English, which presents a different set of challenges.

We tested the traditional method, as well as a new approach where we included the top n words (not tokens) from the Indonesian vocabulary. We discovered that with the new approach, we could achieve better fertility scores by adding around 3000 new vocabulary words. Adding more than 3000 words did not significantly improve the fertility score further, but it increased the size of the embedding matrix, leading to longer training times.

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Training Data

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Training Procedure

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Preprocessing

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Evaluation & Results

Please note that the benchmarking values below are based on our SFT Model, Komodo-7B-Instruct, while here we only release the base model, Komodo-7B-base.

More details can be found in our paper: https://arxiv.org/abs/2403.09362

Infrastructure

📄 License

The model is licensed under llama2.

📚 Citation

@misc{owen2024komodo,
      title={Komodo: A Linguistic Expedition into Indonesia's Regional Languages}, 
      author={Louis Owen and Vishesh Tripathi and Abhay Kumar and Biddwan Ahmed},
      year={2024},
      eprint={2403.09362},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Model Card Authors

• Louis Owen
• Vishesh Tripathi
• Abhay Kumar
• Biddwan Ahmed