pythia-31m-goodwiki-deduped-2048-scratch/README.md

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

tags	metrics	inference	widget	pipeline_tag	license	datasets	language
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	text-generation	apache-2.0	euirim/goodwiki	en

pythia-31m-goodwiki-deduped-2048-scratch

Train from scratch based on config of EleutherAI/pythia-31m for 3 epochs.

It achieves the following results on the evaluation set:

• Loss: 4.5181
• Accuracy: 0.2680

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

***** eval metrics *****                                              
  epoch                   =        3.0                   
  eval_accuracy           =     0.2694                                  eval_loss               =     4.4986                                
  eval_runtime            = 0:00:14.62                                
  eval_samples            =        500                                  eval_samples_per_second =     34.187                                  eval_steps_per_second   =     17.093                              
  perplexity              =    89.8934

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
6.8347	0.16	100	6.7683	0.1380
6.0732	0.32	200	6.0489	0.1712
5.6949	0.48	300	5.6941	0.1935
5.4723	0.64	400	5.4411	0.2066
5.2672	0.8	500	5.2621	0.2162
5.165	0.96	600	5.1339	0.2241
5.0693	1.12	700	5.0290	0.2304
4.9234	1.28	800	4.9430	0.2369
4.886	1.44	900	4.8702	0.2413
4.8422	1.6	1000	4.8086	0.2458
4.7688	1.76	1100	4.7593	0.2488
4.734	1.93	1200	4.7118	0.2527
4.6877	2.09	1300	4.6721	0.2556
4.6135	2.25	1400	4.6350	0.2583
4.6117	2.41	1500	4.6013	0.2606
4.5424	2.57	1600	4.5707	0.2635
4.5535	2.73	1700	4.5447	0.2658
4.4823	2.89	1800	4.5181	0.2680

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.	24.85
ARC (25-shot)	23.12
HellaSwag (10-shot)	25.66
MMLU (5-shot)	23.11
TruthfulQA (0-shot)	51.32
Winogrande (5-shot)	49.88
GSM8K (5-shot)	0.0
DROP (3-shot)	0.86