//TODO - Update with the config schema
Note: This workflow is specifically designed for cloud-based training. For on-device training, refer to the On-Device Training Workflow section.

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On-Device Training with train_engine

This workflow is optimized for training on your own infrastucture. 
from simplifine_alpha import train_engine
import wandb
import os

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Disable WandB Logging (Optional)

WandB logging can be passed as the value of the arguement report_to in the training arguments objects passed to the trainer. It can be fully deactivated as follows:
WandB logging is enabled by default.
# Disabling WandB logging, change if you'd like to have it enabled.
wandb.init(mode='disabled')

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Dataset Configuration

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Using a Hugging Face Dataset

You can provide the name of a Hugging Face (HF) dataset if you’d like to use a pre-existing dataset. Ensure that you adjust the keys, response_template, and template to match your dataset’s structure. 
# You can provide an HF dataset name.
# Be sure to change the keys, response template, and template accordingly.
template = '''### TITLE: {title}\n ### ABSTRACT: {abstract}\n ###EXPLANATION: {explanation}'''
response_template='\n ###EXPLANATION:'
keys = ['title', 'abstract', 'explanation']
dataset_name='REPLACE_WITH_HF_DATASET_NAME'

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Using a Local Dataset

If you don’t want to use a Hugging Face dataset, set from_hf to False and provide a local dataset. The example below shows how to manually create a dataset: 
from_hf = True
if True:  # Change this if you want to use a local dataset instead of a Hugging Face dataset.
  from_hf = False
  data = {
      'title':['title 1', 'title 2', 'title 3']*200,
      'abstract':['abstract 1', 'abstract 2', 'abstract 3']*200,
      'explanation':['explanation 1', 'explanation 2', 'explanation 3']*200
  }

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Model Selection

Choose a model for training. 
# You can change the model. Bigger models might throw OOM errors.
model_name = 'EleutherAI/pythia-160m'
Large models may cause OOM errors, especially in environments with limited GPU memory. Consider using smaller models or utilizing Simplifine’s other GPU offerings.

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Initiating Cloud-Based Training

Finally, use the train_engine.hf_sft function to start training in the cloud. The parameters allow you to control the training process, including whether to use mixed-precision training (fp16), distributed data parallel (ddp), and more 

train_engine.hf_sft(model_name,
                    from_hf=from_hf
                    dataset_name=dataset_name,
                    keys = keys,
                    data = data,
                    template = template,
                    response_template=response_template, 
                    zero=False,
                    ddp=False,
                    gradient_accumulation_steps=4,
                    fp16=True,
                    max_seq_length=2048)
For efficient cloud training, consider adjusting gradient_accumulation_steps and using mixed-precision (fp16=True) to reduce memory usage.