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Training procedure

The following bitsandbytes quantization config was used during training:

  • 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

Model Description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/OPT%20Models/Grade%20School%20Math%20Instructions%20Fine-Tune%20OPT.ipynb.

Intended uses & limitations

This is intended to show the possibilities. It is mainly limited by the input data.

Training & Evaluation Dataset

Dataset Source: https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions

Hyperparameters Used

Hyperperameter Value
Model Checkpoint facebook/opt-2.7b
per_device_train_batch_size 4
gradient_accumulation_steps 4
fp16 True
warmup_steps 225
learning_rate 2e-4
Training Steps 450

Framework versions

Library Version
Python 3.10.1
Torch 2.0.1+cu118
Datasets 2.14.4
Transformer 4.31.0
PEFT 0.4.0


Perplexity = 6.35

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Dataset used to train DunnBC22/opt-2.7b-Fine-Tuned-Grade_School_Math_Instructions