| Tips: | |
| To convert the model, you need to clone the original repository using git clone https://github.com/persimmon-ai-labs/adept-inference, then get the checkpoints: | |
| git clone https://github.com/persimmon-ai-labs/adept-inference | |
| wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar | |
| tar -xvf 8b_base_model_release.tar | |
| python src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py --input_dir /path/to/downloaded/persimmon/weights/ --output_dir /output/path \ | |
| --pt_model_path /path/to/8b_chat_model_release/iter_0001251/mp_rank_00/model_optim_rng.pt | |
| --ada_lib_path /path/to/adept-inference | |
| For the chat model: | |
| wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar | |
| tar -xvf 8b_base_model_release.tar | |
| Thereafter, models can be loaded via: | |
| from transformers import PersimmonForCausalLM, PersimmonTokenizer | |
| model = PersimmonForCausalLM.from_pretrained("/output/path") | |
| tokenizer = PersimmonTokenizer.from_pretrained("/output/path") | |
| Perismmon uses a sentencepiece based tokenizer, with a Unigram model. |