This model is a fine-tuned version of Qwen/Qwen2.5-3B on the assimilation_strict_json_v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.0001

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: 3e-05
train_batch_size: 8
eval_batch_size: 2
seed: 12
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 16
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_steps: 0.05
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0005	1.32	50	0.0003
0.0001	2.64	100	0.0001
0.0001	3.0	114	0.0001

Framework versions

Transformers 5.6.0
Pytorch 2.7.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

17Lab/qwen3b-full-sft-s12

qwen3b_full_sft_8gpu_s12

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

17Lab/
qwen3b-full-sft-s12