Quick Start Guide

This hub contains weights that are trained with LLaMA2-Accessory. To get started, follow the steps below:

  1. Clone the LLaMA2-Accessory repository from GitHub:

    git clone https://github.com/Alpha-VLLM/LLaMA2-Accessory.git

  2. Download the weights from this hub.

  3. The following instructions will guide you through running the models with each checkpoint.

Please note that the checkpoints provided here are trained using the quantization-assisted method. This involves quantizing and freezing the base model while retaining a small portion of trainable parameters. This approach significantly reduces VRAM usage.

For the checkpoints located in the finetune/mm/ directory, use the following commands:

# Run the 13B multi-modal single run checkpoint
torchrun --nproc-per-node=1 demos/single_turn_mm.py \
--llama_config <path-to-params.json> configs/model/finetune/sg/llamaPeft_normBiasLora.json \
--tokenizer_path <path-to-tokenizer.model> \
--pretrained_path <stage1-of-lamaQformerv2_13b> <this-repo>/finetune/mm/alpacaLlava_llamaQformerv2Peft_QF_13B/epoch2 \
--quant \
--llama_type llama_qformerv2_peft

# Explanation of flags:
# --llama_config : Path to the corresponding params.json
# --tokenizer_path : Path to the corresponding tokenizer.model
# --pretrained_path : Combination of <base weights> and <peft weights>
# --quant : Apply quantization method
# --llama_type : Choose from [llama, llama_adapter, llama_peft, llama_qformerv2, llama_qformerv2_peft]

For the checkpoints located in the finetune/sg/ directory, use the following commands:

# 70B single turn platypus
torchrun --nproc-per-node=1 --master-port 29500 demos/single_turn.py \
--llama_config <path-to-Llama-2-70b/params.json> \
--tokenizer_path <path-to-tokenizer.model> \
--pretrained_path <path-to-Llama-2-70b> <path-to-platypus_normBias_QF_70B/epoch3> \
--quant --llama_type llama_peft

Make sure to replace placeholders like <path-to-params.json>, <path-to-tokenizer.model>, and <stage1-of-lamaQformerv2_13b> with the actual paths.

Follow these steps to successfully run the checkpoints using the provided commands and flags. For more details, refer to the documentation in the LLaMA2-Accessory repository.