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metadata
license: mit
datasets:
  - Replete-AI/code_bagel
language:
  - en
tags:
  - code

Base_model

microsoft/Phi-3-medium-128k-instruct(https://huggingface.co/microsoft/Phi-3-medium-128k-instruct)

Datasets

Replete-AI/code_bagel(https://huggingface.co/datasets/Replete-AI/code_bagel)

Train Loss

image/png

Train State

Trainable params: 27852800 || all params: 13988090880 || trainable%: 0.1991 Total Training Duration:69h18m17s { "epoch": 0.9999679800589659, "total_flos": 1.446273483573748e+20, "train_loss": 0.44412665014957775, "train_runtime": 249497.725, "train_samples_per_second": 13.018, "train_steps_per_second": 0.102 }

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 1200
  • num_epochs: 1.0

I personally fine-tuned the largest dataset, which took the most time.