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---
license: mit
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MirrorAPI
This model is a fine-tuned version of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
### Training and evaluation data
The training data of MirrorAPI consists of:
- [`train_sft.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI/blob/main/train/train_sft.json)
- [`train_cot.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI/blob/main/train/train_cot.json)
- [`train_augment.json`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI/blob/main/train/train_augment.json)
The testing data are under [`test_sft`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI/tree/main/test/test_sft) and [`test_cot`](https://huggingface.co/datasets/stabletoolbench/MirrorAPI/tree/main/test/test_cot) for SFT and CoT modes, respectively.
### 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 |