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Punica

Punica: Serving multiple LoRA finetuned LLMs at the cost of one

Paper: https://arxiv.org/abs/2310.18547

See https://github.com/punica-ai/punica/tree/master/examples/finetune

Model

  • Base Model: Llama-2-7b-hf
  • LoRA target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
  • LoRA rank: 16
  • Training epochs: 4

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 4.0

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.2.0.dev20230911+cu121
  • Datasets 2.14.4
  • Tokenizers 0.14.1
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