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UWNSL/
Qwen2.5-32B-Instruct_Short_CoT_lora

Name: Qwen2.5-32B-Instruct_Short_CoT_lora
Availability: InStock

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Qwen/Qwen2.5-32B-Instruct

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MATH_training_response_Qwen2.5-32B-Instruct

This model is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct on the MATH_training_Qwen2.5-32B-Instruct dataset. It achieves the following results on the evaluation set:

Loss: 0.0312

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
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
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: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.0528	0.1500	200	0.0496
0.0179	0.3001	400	0.0363
0.0451	0.4501	600	0.0397
0.0267	0.6002	800	0.0343
0.0499	0.7502	1000	0.0334
0.0258	0.9002	1200	0.0329
0.0175	1.0503	1400	0.0320
0.0164	1.2003	1600	0.0316
0.0178	1.3503	1800	0.0303
0.0154	1.5004	2000	0.0321
0.0172	1.6504	2200	0.0317
0.0133	1.8005	2400	0.0311
0.019	1.9505	2600	0.0312

Framework versions

PEFT 0.12.0
Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

UWNSL/Qwen2.5-32B-Instruct_Short_CoT_lora

MATH_training_response_Qwen2.5-32B-Instruct

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

UWNSL/
Qwen2.5-32B-Instruct_Short_CoT_lora