metadata

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