This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the mental_train_zh and the mental_train_en datasets. It achieves the following results on the evaluation set:

Loss: 0.6514

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: 5e-06
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 64
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: 0.05
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.6924	0.3876	50	0.7080
0.6314	0.7752	100	0.6708
0.4803	1.1628	150	0.6634
0.467	1.5504	200	0.6565
0.4864	1.9380	250	0.6511

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Tonikroos/Qwen_2.5_7B_full_sft_Practice

Qwen_2.5_7B_full_sft_Practice

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tonikroos/
Qwen_2.5_7B_full_sft_Practice