This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the identity and the crafting_sft_success_new_mem datasets. It achieves the following results on the evaluation set:

Loss: 0.4032

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Use 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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.8427	0.3380	50	1.1575
0.5411	0.6760	100	0.5065
0.519	1.0203	150	0.4361
0.3662	1.3583	200	0.4007
0.3679	1.6962	250	0.3948
0.3176	2.0406	300	0.3846
0.2141	2.3785	350	0.4076
0.2089	2.7165	400	0.3996

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

izzcw/mini_llama_crafting_sft_success_new_mem

mini_llama_crafting_sft_success_new_mem

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

izzcw/
mini_llama_crafting_sft_success_new_mem