Instructions to use RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("GPT2-Large") model = PeftModel.from_pretrained(base_model, "RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_Method2test/resolve/main/tokenizer_config.json
261 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |