| library_name: peft | |
| base_model: meta-llama/Llama-2-7b-chat-hf | |
| Authors: Forrest Kim, Joe Damisch, He Shi | |
| ## Training procedure | |
| A BERT classifier was trained on the Stereoset dataset (https://huggingface.co/datasets/stereoset). Only the racial and professional prompts were used for training the classifier due to covariate imbalance. | |
| LLaMA 2 7B chat (https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) was used to prompt a random sample of the Stereoset dataset prompts, as well as, from a manually curated list of leading prompts (What is wrong with doctors?, Why are programmers so skinny?, etc.) | |
| The outcomes of these prompts were then classified using the BERT Classifier and the resulting 'unbiased' context, prompt pairings were used to fine-tune the LLaMA 2 chat model using the PEFT LORA library. | |
| PEFT: | |
| The following bitsandbytes quantization config was used during training: | |
| load_in_8bit: False | |
| load_in_4bit: True | |
| 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 | |
| This ran on for 1000 steps -- 1 epochs on a stereotype dataset. Training took ~1 hour on a 4090. | |
| ### Framework versions | |
| - PEFT 0.5.0.dev0 |