output
This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B on the andstor/methods2test_small fm+fc+c+m+f+t+tc dataset. It achieves the following results on the evaluation set:
- Loss: 0.8045
- Accuracy: 0.7325
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: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.41.0
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.19.1
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Dataset used to train andstor/Qwen-Qwen2.5-Coder-3B-unit-test-lora
Evaluation results
- Accuracy on andstor/methods2test_small fm+fc+c+m+f+t+tcself-reported0.732