Comment
lm_eval/averages/micro_avg_bpb
lm_eval/averages/micro_avg_bpb
lm_eval/averages/micro_avg_bpb
lm_eval/averages/micro_avg_bpb
Run set
18970
Name
28 visualized
State
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_attn_implementation_autoset
_name_or_path
accelerator_config.even_batches
accelerator_config.non_blocking
accelerator_config.split_batches
accelerator_config.use_seedable_sampler
adafactor
adam_beta1
adam_beta2
adam_epsilon
add_cross_attention
allow_nondivisible_batch_size
allow_partial_checkpoint
architectures
attention_bias
attention_dropout
attention_probs_dropout_prob
auto_find_batch_size
auto_map.AutoConfig
auto_map.AutoModel
auto_map.AutoModelForMaskedLM
auto_map.AutoModelForMultipleChoice
auto_map.AutoModelForQuestionAnswering
auto_map.AutoModelForSequenceClassification
auto_map.AutoModelForTokenClassification
average_tokens_across_devices
batch_axis
batch_eval_metrics
bf16
bf16_full_eval
bos_token_id
chat_train_urls
checkpoint_path
checkpointer.append_run_id_to_base_path
checkpointer.base_path
checkpointer.delete_old_temp_checkpoints
checkpointer.keep
checkpointer.save_interval
chunk_size_feed_forward
classifier_activation
classifier_bias
classifier_dropout
classifier_pooling
cli_configs.batch_size
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https://wandb.ai/marin-community/marin/reports/654-Scaling-Laws-with-soft-metrics--VmlldzoxMDk2ODkxNw