add finetuning script

finetune/readme.md

@@ -1,69 +0,0 @@
-# Minicpm-V2 Finetuning
-
-<div align="center">
-
-[English](README.md) 
-
-</div>
-
-We offer the official scripts for easy finetuning of the pretrained minicpm-v2 model on downstream tasks. Our finetune scripts use DeepSpeed by default.
-
-### Data preparation
-
-To prepare your finetuning data, you should (1) formulate each sample as a dictionary consisting of an id, an image path list with an image (optional, not required for pure-text example), and a list of conversations, and (2) save data samples in JSON files.
-
-For the vision-language example with image, you are required to define placeholder(s) <ImageHere> to define the position to insert the image embeddings.
-
-<details>
-  <summary>
-    <b>vision-language example (vl_finetune_data.json) with 1 samples.</b>
-  </summary>
-
-```
-  [
-    {
-      "id": "0",
-      "image": 'path/to/image_0.jpg',
-      "conversations": [
-            {
-              'role': 'user', 
-              'content': '<image>\nHow many desserts are on the white plate?'
-            }, 
-            {
-                'role': 'assistant', 
-                'content': 'There are three desserts on the white plate.'
-            },   
-            {
-                'role': 'user', 
-                'content': 'What type of desserts are they?'
-            },
-            {
-                'role': 'assistant', 
-                'content': 'The desserts are cakes with bananas and pecans on top. They share similarities with donuts, but the presence of bananas and pecans differentiates them.'
-            }, 
-            {
-                'role': 'user', 
-                'content': 'What is the setting of the image?'}, 
-            {
-                'role': 'assistant', 
-                'content': 'The image is set on a table top with a plate containing the three desserts.'
-            },
-        ]
-    },
-  ]
-```
-
-</details>
-
-### Full-parameter finetuning
-
-Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. To launch your training, run the following script:
-
-```
-sh finetune_ds.sh
-```
-#### Customizing Hyperparameters
-To tailor the training process according to your specific requirements, you can adjust various hyperparameters. For comprehensive documentation on available hyperparameters and their functionalities, you can refer to the [official Transformers documentation](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments). Experimentation and fine-tuning of these parameters are essential for achieving optimal model performance tailored to your specific task and dataset.
-### LoRA finetuning
-
-**This part is still unfinished, and we will complete it as soon as possible.**