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 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
Inference Providers
NEW
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Model tree for abcdabcd987/viggo-llama2-7b-lora-16
Base model
meta-llama/Llama-2-7b-hf