
## 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](https://huggingface.co/meta-llama/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

{"language": ["en"], "license": "apache-2.0", "tags": ["punica", "llama-factory", "lora", "generated_from_trainer"], "datasets": ["gsm8k"], "base_model": "meta-llama/Llama-2-7b-hf", "pipeline_tag": "text2text-generation"}
punica llama-factory lora generated_from_trainer text2text-generation en dataset:gsm8k arxiv:2310.18547 base_model:meta-llama/Llama-2-7b-hf license:apache-2.0 region:us