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README.md

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----
-language:
-- en
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+# Quick Start Guide
+
+This hub contains weights that are trained with [LLaMA2-Accessory](https://github.com/Alpha-VLLM/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:
+
+```bash
+# Run the 13B multi-modal single run checkpoint
+torchrun --nproc-per-node=1 demos/single_turn_mm.py \
+--llama_config <path-to-params.json> \
+--tokenizer_path <path-to-tokenizer.model> \
+--pretrained_path <stage1-of-lamaQformerv2_13b> <this-repo>/finetune/mm/alpacaLlava_llamaQformerv2Peft_QF_13B/ \
+--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]
+```
+
+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.