Question about whether Yi-VL-6B can be fine-tuned using its own dataset

[a2382625920]

Reminder

• I have searched the Github Discussion and issues and have not found anything similar to this.

Motivation

The low footprint of Yi-VL's video memory and the high speed of its inference allows room for more utility. If the Yi-VL series of multimodal macromodels can be fine-tuned using its own dataset, it many projects will be a great leap forward!

Solution

No response

Alternatives

No response

Anything Else?

No response

Are you willing to submit a PR?

• I'm willing to submit a PR!

[Jintao-Huang]

hello~

yi-vl-6b is an excellent performing model, and the ms-swift LLM training framework has incorporated sft for yi-vl. It provides example scripts and supports custom datasets. You can check it out here~
https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_vl_6b_chat

The script utilizes the COCO dataset for fine-tuning. After training, the generated samples are as follows:

"""
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A large airplane is on display in a museum. 
###

[LABELS]People walking in a museum with a airplane hanging from the celing.
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000492132.jpg']
--------------------------------------------------------------------
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A bowl of fruit and cake next to a cup of coffee. 
###

[LABELS]a bowl of fruit and pastry on a table
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000558642.jpg']
"""

[minlik]

I added the finetuning scripts. See #368

[a2382625920]

hello~

yi-vl-6b is an excellent performing model, and the ms-swift LLM training framework has incorporated sft for yi-vl. It provides example scripts and supports custom datasets. You can check it out here~ https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_vl_6b_chat

The script utilizes the COCO dataset for fine-tuning. After training, the generated samples are as follows:

"""
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A large airplane is on display in a museum. 
###

[LABELS]People walking in a museum with a airplane hanging from the celing.
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000492132.jpg']
--------------------------------------------------------------------
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A bowl of fruit and cake next to a cup of coffee. 
###

[LABELS]a bowl of fruit and pastry on a table
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000558642.jpg']
"""

Thanks, I'm swift trying to register the dataset and train with the already downloaded model!

[a2382625920]

I added the finetuning scripts. See #368

Thank you very much for your work, I will try to use it!

[a2382625920]

I added the finetuning scripts. See #368

Hi, I tried the method you provided and it comes up with the following warning, which may have an effect on the final fine-tuned result:

WARNING: tokenization mismatch: 208 vs. 210. (ignored)
WARNING: tokenization mismatch: 220 vs. 222. (ignored)
WARNING: tokenization mismatch: 174 vs. 176. (ignored)
WARNING: tokenization mismatch: 219 vs. 221. (ignored)
WARNING: tokenization mismatch: 197 vs. 199. (ignored)
WARNING: tokenization mismatch: 216 vs. 218. (ignored)
WARNING: tokenization mismatch: 196 vs. 198. (ignored)
WARNING: tokenization mismatch: 222 vs. 224. (ignored)
WARNING: tokenization mismatch: 180 vs. 182. (ignored)
WARNING: tokenization mismatch: 219 vs. 221. (ignored)
WARNING: tokenization mismatch: 233 vs. 235. (ignored)
WARNING: tokenization mismatch: 177 vs. 179. (ignored)
WARNING: tokenization mismatch: 195 vs. 197. (ignored)
WARNING: tokenization mismatch: 227 vs. 229. (ignored)
WARNING: tokenization mismatch: 226 vs. 228. (ignored)
WARNING: tokenization mismatch: 221 vs. 223. (ignored)
WARNING: tokenization mismatch: 178 vs. 180. (ignored)
WARNING: tokenization mismatch: 237 vs. 239. (ignored)
WARNING: tokenization mismatch: 178 vs. 180. (ignored)
WARNING: tokenization mismatch: 227 vs. 229. (ignored)
WARNING: tokenization mismatch: 175 vs. 177. (ignored)
WARNING: tokenization mismatch: 222 vs. 224. (ignored)
WARNING: tokenization mismatch: 215 vs. 217. (ignored)
WARNING: tokenization mismatch: 217 vs. 219. (ignored)
WARNING: tokenization mismatch: 220 vs. 222. (ignored)
WARNING: tokenization mismatch: 215 vs. 217. (ignored)
WARNING: tokenization mismatch: 227 vs. 229. (ignored)
WARNING: tokenization mismatch: 178 vs. 180. (ignored)
WARNING: tokenization mismatch: 235 vs. 237. (ignored)
WARNING: tokenization mismatch: 177 vs. 179. (ignored)
WARNING: tokenization mismatch: 221 vs. 223. (ignored)
WARNING: tokenization mismatch: 197 vs. 199. (ignored)
WARNING: tokenization mismatch: 220 vs. 222. (ignored)
WARNING: tokenization mismatch: 176 vs. 178. (ignored)
WARNING: tokenization mismatch: 228 vs. 230. (ignored)
WARNING: tokenization mismatch: 221 vs. 223. (ignored)
WARNING: tokenization mismatch: 175 vs. 177. (ignored)
WARNING: tokenization mismatch: 220 vs. 222. (ignored)
WARNING: tokenization mismatch: 177 vs. 179. (ignored)
WARNING: tokenization mismatch: 176 vs. 178. (ignored)
WARNING: tokenization mismatch: 176 vs. 178. (ignored)
WARNING: tokenization mismatch: 229 vs. 231. (ignored)
WARNING: tokenization mismatch: 221 vs. 223. (ignored)
WARNING: tokenization mismatch: 177 vs. 179. (ignored)
WARNING: tokenization mismatch: 220 vs. 222. (ignored)
WARNING: tokenization mismatch: 227 vs. 229. (ignored)
WARNING: tokenization mismatch: 178 vs. 180. (ignored)
WARNING: tokenization mismatch: 233 vs. 235. (ignored)
WARNING: tokenization mismatch: 176 vs. 178. (ignored)
WARNING: tokenization mismatch: 175 vs. 177. (ignored)
WARNING: tokenization mismatch: 230 vs. 232. (ignored)

[a2382625920]

hello~

yi-vl-6b is an excellent performing model, and the ms-swift LLM training framework has incorporated sft for yi-vl. It provides example scripts and supports custom datasets. You can check it out here~ https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_vl_6b_chat

The script utilizes the COCO dataset for fine-tuning. After training, the generated samples are as follows:

"""
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A large airplane is on display in a museum. 
###

[LABELS]People walking in a museum with a airplane hanging from the celing.
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000492132.jpg']
--------------------------------------------------------------------
[PROMPT]This is a chat between an inquisitive human and an AI assistant. Assume the role of the AI assistant. Read all the images carefully, and respond to the human's questions with informative, helpful, detailed and polite answers. 这是一个好奇的人类和一个人工智能助手之间的对话。假设你扮演这个AI助手的角色。仔细阅读所有的图像，并对人类的问题做出信息丰富、有帮助、详细的和礼貌的回答。

### Human: [-200 * 1]
please describe the image.
### Assistant:
[OUTPUT]A bowl of fruit and cake next to a cup of coffee. 
###

[LABELS]a bowl of fruit and pastry on a table
[IMAGES]['https://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/coco/2014/val2014/COCO_val2014_000000558642.jpg']
"""

I don't know how I can register my own dataset. And this doesn't allow me to enter my own local model path, after I enter the local model path, swift still uses the network to download the model. How can I solve this?

[minlik]

WARNING: tokenization mismatch

I didn't encounter the same issue. Could you please share your training scripts?

After reviewing the code, I noticed that the WARNING might be caused by the commented code here. Could you please checke your local code?

[minlik]

@a2382625920 I thought of another possibility. The training code is modified from LLaVA. If you have installed llava locally, you can uninstall it and try again.

[a2382625920]

我想到了另一种可能性。训练代码是从 LLaVA 修改而来的。如果您已在本地安装了 llava，则可以将其卸载并重试。

I did use llava's virtual environment to run the code, and after I uninstalled it and installed Yi's installer environment, the following error was reported:

Traceback (most recent call last):
File "/root/siton-glusterfs-eaxtsxdfs/hzt/projects/Yi/VL/llava/train/train_mem.py", line 6, in
from llava.train import llama_flash_attn_monkey_patch
ModuleNotFoundError: No module named 'llava'

Do you have to use the llava environment?

[a2382625920]

WARNING: tokenization mismatch

I didn't encounter the same issue. Could you please share your training scripts?

After reviewing the code, I noticed that the WARNING might be caused by the commented code here. Could you please checke your local code?

#!/bin/bash

deepspeed --include localhost:0 --master_port 1234 llava/train/train_mem.py
--deepspeed ./scripts/zero2.json
--lora_enable True
--model_name_or_path /root/siton-glusterfs-eaxtsxdfs/hzt/model/Yi-VL-6B
--data_path /root/siton-glusterfs-eaxtsxdfs/hzt/data/LLaVa_data/filter_cap_1226.json
--image_folder /root/siton-glusterfs-eaxtsxdfs/xts/data/s_mix/image
--vision_tower /root/siton-glusterfs-eaxtsxdfs/hzt/model/Yi-VL-6B/vit/clip-vit-H-14-laion2B-s32B-b79K-yi-vl-6B-448
--output_dir ./checkpoint/Yi-VL-6B
--mm_vision_select_layer -2
--mm_use_im_start_end False
--mm_use_im_patch_token False
--bf16 True
--num_train_epochs 10
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--gradient_accumulation_steps 8
--evaluation_strategy "no"
--save_strategy "steps"
--save_steps 200
--save_total_limit 3
--learning_rate 2e-5
--weight_decay 0.
--warmup_ratio 0.03
--lr_scheduler_type "cosine"
--logging_steps 1
--tf32 True
--model_max_length 2048
--gradient_checkpointing True
--lazy_preprocess True
--dataloader_num_workers 4
--report_to wandb

[minlik]

我想到了另一种可能性。训练代码是从 LLaVA 修改而来的。如果您已在本地安装了 llava，则可以将其卸载并重试。

I did use llava's virtual environment to run the code, and after I uninstalled it and installed Yi's installer environment, the following error was reported:

Traceback (most recent call last): File "/root/siton-glusterfs-eaxtsxdfs/hzt/projects/Yi/VL/llava/train/train_mem.py", line 6, in from llava.train import llama_flash_attn_monkey_patch ModuleNotFoundError: No module named 'llava'

Do you have to use the llava environment?

Could you please run the command export PYTHONPATH=$PWD:$PYTHONPATH under the VL folder and then try again? Thank you.

[lucasjinreal]

Can u also share pretraining script? Which tuning projector and vision encoder with stage 1 and stage 2?
This not same as llava.