update README.md file

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hanluzhi0429@gmail.com
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**Read this in other languages: [English](README.md), [中文](README_zh.md).**
# OpenVLA Lightweight Version
OpenVLA的轻量化版本,基于llava-next框架
特点:
1.主体模型仅0.5B,使用qwen2-0.5B
2.不使用RLDS格式数据集,使用mllm格式进行微调
3.不占用LLM模型固有tokens,使用新增的271个tokens表示动作
Lightweight version of OpenVLA, based on the llava-next framework
Features:
1.The main model is only 0.5B, using qwen2-0.5B.
2.Does not use RLDS format datasets, instead fine-tuned using mllm format.
3.Does not occupy inherent tokens of the LLM model, instead using an additional 271 tokens to represent actions.
# How to use
## Installation
@@ -29,9 +35,24 @@ conda activate vla-qwen
pip install --upgrade pip
pip install -e ".[train]"
```
### 4. Download the weights from huggingface
```bash
cd openVLA-Qwen/models
git clone git clone https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-ov
```
### 5. Change the file path in scripts/train/finetune_ov_vla.sh
```bash
PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov"
--data_path scripts/train/vla.yaml
--image_folder /images \
```
### 6. Change the data paht in scripts/train/vla.yaml
```bash
json_path: /root/data/vla_llava.json
```
## Acknowledgements
I would like to express my gratitude to the following repositories:
This project makes use of the following open-source projects, and I would like to express my gratitude to their creators:
- [OpenVLA](https://github.com/openvla/openvla) - For providing the core structure and functionality that inspired OpenVLA-Qwen.
- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - For his contribution to data preprocessing techniques.
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**其他语言版本: [English](README.md), [中文](README_zh.md).**
# OpenVLA Lightweight Version
OpenVLA的轻量化版本,基于llava-next框架
特点:
1.主体模型仅0.5B,使用qwen2-0.5B
2.不使用RLDS格式数据集,使用mllm格式进行微调
3.不占用LLM模型固有tokens,使用新增的271个tokens表示动作
## Installation
Follow the steps below to install the OpenVLA-Qwen lightweight version.
### 1. Clone the repository
Clone the repository to your local machine:
```bash
git clone https://github.com/IP127000/openVLA-Qwen.git
cd openVLA-Qwen
```
### 2. Create a new conda environment and activate it:
```bash
conda create -n vla-qwen python=3.10 -y
conda activate vla-qwen
```
### 3. Install the required dependencies
```bash
pip install --upgrade pip
pip install -e ".[train]"
```
## Acknowledgements
I would like to express my gratitude to the following repositories:
- [OpenVLA](https://github.com/openvla/openvla) - For providing the core structure and functionality that inspired OpenVLA-Qwen.
- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - For his contribution to data preprocessing techniques.
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**Read this in other languages: [English](README.md), [中文](README_zh.md).**
# OpenVLA Lightweight Version
基于llava-next框架的OpenVLA轻量版。
特性:
主模型只有0.5B,使用qwen2-0.5B。
不使用RLDS格式的数据集,而是采用mllm格式进行微调。
不占用LLM模型的固有token,而是使用额外的271个token来表示动作。
# 如何使用
## Installation
按照以下步骤安装OpenVLA-Qwen轻量版。
### 1. Clone the repository
Clone the repository to your local machine:
```bash
git clone https://github.com/IP127000/openVLA-Qwen.git
cd openVLA-Qwen
```
### 2. Create a new conda environment and activate it:
```bash
conda create -n vla-qwen python=3.10 -y
conda activate vla-qwen
```
### 3. Install the required dependencies
```bash
pip install --upgrade pip
pip install -e ".[train]"
```
### 4. Download the weights from huggingface
```bash
cd openVLA-Qwen/models
git clone git clone https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-ov
```
### 5. Change the file path in scripts/train/finetune_ov_vla.sh
```bash
PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov"
--data_path scripts/train/vla.yaml
--image_folder /images \
```
### 6. Change the data paht in scripts/train/vla.yaml
```bash
json_path: /root/data/vla_llava.json
```
## 致谢
本项目使用了以下开源项目,在此向其开发者表示感谢:
- [OpenVLA](https://github.com/openvla/openvla) - 感谢提供核心结构和功能,启发了OpenVLA-Qwen。
- [LLaVA_OpenVLA](https://github.com/Darren-greenhand/LLaVA-Next) - 感谢他对数据处理技术的贡献。
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MIXTURE="qwen"
PROMPT_VERSION="qwen_vla"
RUN_NAME="openvla-${MIXTURE}"
PREV_STAGE_CHECKPOINT="models"
PREV_STAGE_CHECKPOINT="models/llava-onevision-qwen2-0.5b-ov"
echo "PREV_STAGE_CHECKPOINT: ${PREV_STAGE_CHECKPOINT}"
echo "MID_RUN_NAME: ${RUN_NAME}"
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datasets:
- json_path: /root/code/openvla-llava/LLaVA-Next/vla_llava.json
- json_path: /root/data/vla_llava.json
sampling_strategy: "all"