hf (pretrained=pszemraj/jamba-H1024_L12-v0.13-KIx2,trust_remote_code=True,dtype=float), gen_kwargs: (None), limit: 0.9999, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	Metric	Value		Stderr
winogrande	1	none	acc	0.5067	±	0.0141
piqa	1	none	acc	0.5912	±	0.0138
		none	acc_norm	0.5951	±	0.0138
openbookqa	1	none	acc	0.1800	±	0.0172
		none	acc_norm	0.2920	±	0.0204
lambada_openai	1	none	perplexity	103.1241	±	8.5843
		none	acc	0.2502	±	0.0122
boolq	2	none	acc	0.6196	±	0.0136
arc_easy	1	none	acc	0.3836	±	0.0137
		none	acc_norm	0.3694	±	0.0136

example outputs

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 80085
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Input Tokens Seen
3.2013	0.4241	200	3.0653	0.4479	419430400
3.1976	0.8481	400	3.0434	0.4506	838860800
3.1485	1.2722	600	3.0375	0.4513	1258291200
3.1871	1.6963	800	3.0366	0.4514	1677721600

Framework versions

Transformers 4.40.1
Pytorch 2.2.0+cu121
Datasets 2.19.0
Tokenizers 0.19.1

pszemraj/jamba-900M-v0.13-KIx2

jamba-900M-v0.13-KIx2

Quick eval

example outputs

Training procedure

Training hyperparameters

Training results

Framework versions

pszemraj/
jamba-900M-v0.13-KIx2