kou199024/Inverse-Qwen2.5-7B-BlackBox

Inverse-Qwen-2.5-BlackBox-7B-LoRA-Adapter

This model is a fine-tuned version of Qwen/Qwen2.5-7B on the inv_qwen_inf-ins_660k dataset for paper Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts.

Model description

LoRA Adapter for Inverse-Qwen-2.5-7B. Please use with the originl Qwen2.5-7B base model.

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 32
• total_train_batch_size: 1024
• total_eval_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3.0

Framework versions

• PEFT 0.12.0
• Transformers 4.46.1
• Pytorch 2.4.0+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3

---

license: apache-2.0