Impact of Tokenization on LLaMa Russian Adaptation
Paper • 2312.02598 • Published • 7
How to use rccmsu/ruadapt_saiga2_7b_v0.1 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("rccmsu/ruadapt_llama2_7b_v0.1")
model = PeftModel.from_pretrained(base_model, "rccmsu/ruadapt_saiga2_7b_v0.1")Use in the same way as IlyaGusev/saiga2_7b_lora.
WARNING! Load tokenizer as AutoTokenizer.from_pretrained(model_path, use_fast=True)
Up to 60% faster generation and 35% training (on identical russian text sequences!) with HF because of different tokenizer.
Colab: https://colab.research.google.com/drive/109ZhEB6STy-0jO-Z_4ttkWr1jg_FCTRW?usp=sharing
Paper: Tikhomirov M., Chernyshev D. Impact of Tokenization on LLaMa Russian Adaptation //arXiv preprint arXiv:2312.02598. – 2023.
Instruction version (Saiga datasets) of Russian adaptation of LLaMa-2-7B by replacing the tokenizer. Paper: Tikhomirov M.M., Chernyshev D.I., Impact of Tokenization on LLaMa Russian Adaptation (will be soon)