This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the gsm8k_multi_n500_train dataset. It achieves the following results on the evaluation set:

Loss: 0.4412
Accuracy: 0.8740
Mcq Accuracy: 0.4111

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Mcq Accuracy
0.3906	0.7111	200	0.4350	0.8738	0.3911
0.3497	1.4196	400	0.4327	0.8747	0.3956
0.3014	2.128	600	0.4379	0.8750	0.4156
0.2891	2.8391	800	0.4415	0.8738	0.3956

Framework versions

PEFT 0.18.1
Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

xummer/llama3-1-8b-gsm8k-P1-multi-n500-seed42-lora-all

gsm8k_P1_multi_n500_seed42

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

xummer/
llama3-1-8b-gsm8k-P1-multi-n500-seed42-lora-all