pythia-31m-simplewiki-scratch-bf16/README.md

tags, metrics, inference, widget, license, datasets, pipeline_tag

tags	metrics	inference	widget	license	datasets	pipeline_tag
generated_from_trainer	accuracy	parameters max_new_tokens do_sample repetition_penalty no_repeat_ngram_size guidance_scale eta_cutoff 64 true 1.1 5 1.01 0.001	text example_title My name is El Microondas the Wise and El Microondas text example_title A meme is meme text example_title Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had Coreference resolution text example_title On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book Logic puzzles text example_title The two men running to become New York City's next mayor will face off in their first debate Wednesday night Reading comprehension	apache-2.0	pszemraj/simple_wikipedia_LM	text-generation

pythia-31m-simplewiki-scratch-bf16

Trained from random initialized config based on EleutherAI/pythia-31m, 3 epochs bf16 It achieves the following results on the evaluation set:

• Loss: 4.1763
• Accuracy: 0.3676

Model description

tuned with bf16 (previous was fp32)

Intended uses & limitations

More information needed

Training and evaluation data

***** eval metrics *****                                              
  epoch                   =       2.99                   
  eval_accuracy           =     0.3723                                  eval_loss               =     4.1155                                
  eval_runtime            = 0:00:14.44                                
  eval_samples            =        500                                  eval_samples_per_second =     34.602                                  eval_steps_per_second   =     17.301                              
  perplexity              =    61.2811

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 2
• eval_batch_size: 2
• seed: 80085
• gradient_accumulation_steps: 64
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
• lr_scheduler_type: inverse_sqrt
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.8617	0.45	100	5.5276	0.2451
5.2782	0.9	200	4.9596	0.2965
4.9996	1.35	300	4.6412	0.3310
4.6292	1.8	400	4.4344	0.3485
4.5339	2.25	500	4.2875	0.3600
4.5214	2.7	600	4.1763	0.3676

Framework versions

• Transformers 4.33.1
• Pytorch 2.2.0.dev20230907+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.63
ARC (25-shot)	22.78
HellaSwag (10-shot)	25.61
MMLU (5-shot)	23.12
TruthfulQA (0-shot)	49.65
Winogrande (5-shot)	50.51
GSM8K (5-shot)	0.0
DROP (3-shot)	0.72