From 251a4c20bef527f767903e0e3fc472df6e7f52e7 Mon Sep 17 00:00:00 2001
From: YuzaChongyi <490083538@qq.com>
Date: Sun, 11 Aug 2024 12:11:01 +0800
Subject: [PATCH] Update readme.md

---
 finetune/readme.md | 70 ----------------------------------------------
 1 file changed, 70 deletions(-)

diff --git a/finetune/readme.md b/finetune/readme.md
index 855b583..700609f 100644
--- a/finetune/readme.md
+++ b/finetune/readme.md
@@ -50,75 +50,6 @@ For the vision-language example with image, you are required to provide **\<imag
 
 </details>
 
-### Full-parameter finetuning
-
-Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path and DATA path in the shell scripts.
-
-```shell
-MODEL="openbmb/MiniCPM-V-2_6" # or openbmb/MiniCPM-Llama3-V-2_5, openbmb/MiniCPM-V-2
-DATA="path/to/trainging_data" # json file
-EVAL_DATA="path/to/test_data" # json file
-```
-
-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.
-# MiniCPM-V Finetuning
-
-
-We offer the official scripts for easy finetuning of the pretrained **MiniCPM-Llama3-V 2.5** and **MiniCPM-V 2.0** on downstream tasks. Our finetune scripts use transformers Trainer and DeepSpeed by default.
-
-### Data preparation
-
-To prepare your finetuning data, you should formulate each sample as a dictionary consisting of an id, an image path list with an image, and a list of conversations. Then save data samples in JSON files.
-
-For the vision-language example with image, you are required to provide **\<image\>** to define the position to insert the image embeddings. If you don't provide \<image\>, the image will be placed at the front of the conversation.
-
-<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
 
@@ -137,7 +68,6 @@ To launch your training, run the following script:
 sh finetune_ds.sh
 ```
 
-Specially, Llama3 has a different chat_template for training and inference, we modified the chat_template for training, so please take care to restore the chat_template when inference on the training ckpt.
 
 ### LoRA finetuning