This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the identity and the data_mc_filtered datasets. It achieves the following results on the evaluation set:

Loss: 1.2652

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7445	0.7463	50	0.7196
0.576	1.4925	100	0.7831
0.3113	2.2388	150	0.8755
0.3723	2.9851	200	0.8511
0.2325	3.7313	250	0.8775
0.1831	4.4776	300	0.9325
0.107	5.2239	350	1.0493
0.0884	5.9701	400	0.9148
0.0442	6.7164	450	1.0387
0.0367	7.4627	500	1.1612
0.0111	8.2090	550	1.1844
0.016	8.9552	600	1.2519
0.0057	9.7015	650	1.2654

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

hlillemark/combined_sft_mc_filtered

combined_sft_mc_filtered

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hlillemark/
combined_sft_mc_filtered