Model Description
Pre-training on cleaned version of Principles
- removing numeric references to footnotes
- removing numeric counts, i.e. 1) ... 2) ... 3) ...
- correcting gramma, i.e. full stops must be followed by a space
- finetuning OPT-30B model on the dataset above
- Dataset location: Jellywibble/dalio-principles-cleaned-v3
Metrics
- Checkpoint 8 served
- Hellaswag Perplexity: 30.65
- 2.289 eval loss
wandb link: https://wandb.ai/jellywibble/huggingface/runs/2jqc504o?workspace=user-jellywibble
Model Parameters
Trained on 4xA40, effective batchsize = 8
- base_model_name facebook/opt-30b
- dataset_name Jellywibble/dalio-principles-cleaned-v3
- block_size 1024
- gradient_accumulation_steps 2
- per_device_train_batch_size 1
- seed 2
- num_train_epochs 1
- learning_rate 3e-6
Notes
- It is important for the effective batch size to be at least 8
- Learning rate higher than 3e-6 will result in massive overfitting, i.e. much worse Hellaswag metrics
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