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https://github.com/ssbuild/llava_finetuning


https://github.com/ssbuild/llava_finetuning

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README

        

## update information
- [deep_training](https://github.com/ssbuild/deep_training)

```text
2024-06-01 initial
```

## install
- pip install -U -r requirements.txt
- 如果无法安装, 可以切换官方源 pip install -i https://pypi.org/simple -U -r requirements.txt

## weigtht select one is suitable for you
支持且不限于以下权重
- [Yi-VL-6B](https://huggingface.co/01-ai/Yi-VL-6B)
- [Yi-VL-34B](https://huggingface.co/01-ai/Yi-VL-34B)

## data sample
open_data https://github.com/ssbuild/open_data

单条数据示例
```text
p prefix optional
text must
img must

```

```json
{"id": 1, "p": "", "text": "图中是一只拉布拉多犬", "img": "../assets/demo.jpeg"}
```

## infer
# infer_finetuning.py 推理微调模型
# infer_lora_finetuning.py 推理微调模型
# infer_ptuning.py 推理p-tuning-v2微调模型
python infer_finetuning.py

## training
```text
# 制作数据
cd scripts
bash train_full.sh -m dataset
or
bash train_lora.sh -m dataset
or
bash train_ptv2.sh -m dataset

注: num_process_worker 为多进程制作数据 , 如果数据量较大 , 适当调大至cpu数量
dataHelper.make_dataset_with_args(data_args.train_file,mixed_data=False, shuffle=True,mode='train',num_process_worker=0)

# 全参数训练
bash train_full.sh

# lora adalora ia3
bash train_lora.sh

# ptv2
bash train_ptv2.sh
```

## 训练参数
[训练参数](args.MD)

## 友情链接

- [pytorch-task-example](https://github.com/ssbuild/pytorch-task-example)
- [tf-task-example](https://github.com/ssbuild/tf-task-example)
- [chatmoss_finetuning](https://github.com/ssbuild/chatmoss_finetuning)
- [chatglm_finetuning](https://github.com/ssbuild/chatglm_finetuning)
- [t5_finetuning](https://github.com/ssbuild/t5_finetuning)
- [llm_finetuning](https://github.com/ssbuild/llm_finetuning)
- [llm_rlhf](https://github.com/ssbuild/llm_rlhf)
- [chatglm_rlhf](https://github.com/ssbuild/chatglm_rlhf)
- [t5_rlhf](https://github.com/ssbuild/t5_rlhf)
- [rwkv_finetuning](https://github.com/ssbuild/rwkv_finetuning)
- [baichuan_finetuning](https://github.com/ssbuild/baichuan_finetuning)

##
纯粹而干净的代码

## Star History

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