Instructions to use RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData 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_RevisedData") - Notebooks
- Google Colab
- Kaggle
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Download README.md from RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData: direct link, hf CLI and curl.
- Browser
- Download file 469 Bytes
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https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/resolve/main/README.md
- Command line
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hf download hf://RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/README.md
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curl -L -o README.md https://huggingface.co/RajuEEE/RewardModelForQuestionAnswering_GPT2_RevisedData/resolve/main/README.md
469 Bytes
| library_name: peft | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - quant_method: bitsandbytes | |
| - load_in_8bit: True | |
| - load_in_4bit: False | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: fp4 | |
| - bnb_4bit_use_double_quant: False | |
| - bnb_4bit_compute_dtype: float32 | |
| ### Framework versions | |
| - PEFT 0.6.0.dev0 | |