DifferentiableEvolutionaryRL/DERL-Meta-Optimizer-Init-Qwen2.5-0.5B-Instruct

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sft

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the sftwithformat dataset.

Model description

The model is the base Meta-Optimizer for DERL used for all tasks.

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 1e-05
• —trainbatchsize: 128
• —evalbatchsize: 8
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 4096
• —totalevalbatch_size: 64
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 12.0

Training results

Framework versions

• —Transformers 4.52.4
• —Pytorch 2.6.0+cu124
• —Datasets 3.3.2
• —Tokenizers 0.21.1