metadata
license: mit
StableToolBench-MirrorAPI
This model is a fine-tuned version of Qwen2.5-7B-Instruct
Training and evaluation data
The training data of MirrorAPI consists of:
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.04
- lr_scheduler_warmup_steps: 100
- num_epochs: 5.0
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1