Update README

README.md

@@ -20,7 +20,7 @@
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 <p align="center">
   MiniCPM-V 4.0 <a href="https://huggingface.co/openbmb/MiniCPM-V-4">🤗</a>  <a href="https://minicpm-v.openbmb.cn/"> 🤖</a> | MiniCPM-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a>  <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> 🤖</a> | MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="http://120.92.209.146:8887/">🤖</a> | <a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook">🍳 Cookbook</a> | 
   📄 Technical Blog [<a href="https://openbmb.notion.site/MiniCPM-o-2-6-A-GPT-4o-Level-MLLM-for-Vision-Speech-and-Multimodal-Live-Streaming-on-Your-Phone-185ede1b7a558042b5d5e45e6b237da9">English</a>/<a href="https://openbmb.notion.site/MiniCPM-o-2-6-GPT-4o-188ede1b7a558084b3aedd669cb80730">中文</a>] 
@@ -3122,7 +3122,7 @@ pip install vllm
 
 ### Simple Fine-tuning <!-- omit in toc -->
 
-We support simple fine-tuning with Hugging Face for MiniCPM-o 2.6, MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0.
+We support simple fine-tuning with Hugging Face for MiniCPM-V 4.0, MiniCPM-o 2.6, MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0.
 
 [Reference Document](./finetune/readme.md)
 
@@ -3139,7 +3139,7 @@ Best Practices: [MiniCPM-o 2.6](https://github.com/PKU-Alignment/align-anything/
 We support fine-tuning MiniCPM-o 2.6 and MiniCPM-V 2.6 with the LLaMA-Factory framework. LLaMA-Factory provides a solution for flexibly customizing the fine-tuning (Lora/Full/Qlora) of 200+ LLMs without the need for coding through the built-in web UI LLaMABoard. It supports various training methods like sft/ppo/dpo/kto and advanced algorithms like Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA.
 
 
-Best Practices: [MiniCPM-o 2.6 | MiniCPM-V 2.6](./docs/llamafactory_train_and_infer.md). 
+Best Practices: [MiniCPM-V 4.0 | MiniCPM-o 2.6 | MiniCPM-V 2.6](./docs/llamafactory_train_and_infer.md). 
 
 
 ### With the SWIFT Framework <!-- omit in toc -->

README_zh.md

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    <a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook" target="_blank">&nbsp;🍳 使用指南</a >&nbsp;
  </span>
 
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+  <br>
   <p align="center">
   MiniCPM-V 4.0 <a href="https://huggingface.co/openbmb/MiniCPM-V-4">🤗</a>  <a href="https://minicpm-v.openbmb.cn/"> 🤖</a> | MiniCPM-o 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-o-2_6">🤗</a> <a href="https://minicpm-omni-webdemo-us.modelbest.cn/"> 🤖</a> | MiniCPM-V 2.6 <a href="https://huggingface.co/openbmb/MiniCPM-V-2_6">🤗</a> <a href="http://120.92.209.146:8887/">🤖</a> | 
   📄 技术报告 [<a href="https://openbmb.notion.site/MiniCPM-o-2-6-GPT-4o-188ede1b7a558084b3aedd669cb80730">中文</a>/<a href="https://openbmb.notion.site/MiniCPM-o-2-6-A-GPT-4o-Level-MLLM-for-Vision-Speech-and-Multimodal-Live-Streaming-on-Your-Phone-185ede1b7a558042b5d5e45e6b237da9">English</a>]
@@ -2987,7 +2987,7 @@ pip install vllm
 
 ### 简易微调 <!-- omit in toc -->
 
-我们支持使用 Huggingface Transformers 库简易地微调 MiniCPM-o 2.6、MiniCPM-V 2.6、MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0 模型。
+我们支持使用 Huggingface Transformers 库简易地微调 MiniCPM-V 4.0、MiniCPM-o 2.6、MiniCPM-V 2.6、MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0 模型。
 
 [参考文档](./finetune/readme.md)
 
@@ -3003,7 +3003,7 @@ pip install vllm
 
 我们支持使用 LLaMA-Factory 微调 MiniCPM-o 2.6 和 MiniCPM-V 2.6。LLaMA-Factory 提供了一种灵活定制 200 多个大型语言模型（LLM）微调（Lora/Full/Qlora）解决方案，无需编写代码，通过内置的 Web 用户界面 LLaMABoard 即可实现训练/推理/评估。它支持多种训练方法，如 sft/ppo/dpo/kto，并且还支持如 Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA 等高级算法。
 
-最佳实践: [MiniCPM-o 2.6 | MiniCPM-V 2.6](./docs/llamafactory_train_and_infer.md). 
+最佳实践: [MiniCPM-V 4.0 | MiniCPM-o 2.6 | MiniCPM-V 2.6](./docs/llamafactory_train_and_infer.md). 
 
 
 ### 使用 SWIFT 框架 <!-- omit in toc -->

docs/llamafactory_train_and_infer.md

@@ -13,6 +13,7 @@
 - [Inference](#Inference)
 
 ## Support Models
+* [openbmb/MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4)
 * [openbmb/MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6)
 * [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6)
 

finetune/readme.md

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-# MiniCPM-V Finetuning
+# MiniCPM-V & o Finetuning
 
 
-We offer the official scripts for easy finetuning of the pretrained **MiniCPM-o-2_6**, **MiniCPM-V-2_6**, **MiniCPM-Llama3-V 2.5** and **MiniCPM-V 2.0** on downstream tasks. Our finetune scripts use transformers Trainer and DeepSpeed by default.
+We offer the official scripts for easy finetuning of the pretrained **MiniCPM-V 4.0**, **MiniCPM-o 2.6**, **MiniCPM-V 2.6**, **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
 
@@ -96,11 +96,10 @@ If the total token count exceeds `max_length`, truncation will be applied. For m
 Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path, DATA path and LLM_TYPE in the shell scripts.
 
 ```shell
-MODEL="MiniCPM-o-2_6" # or "openbmb/MiniCPM-V-2_6", openbmb/MiniCPM-Llama3-V-2_5, openbmb/MiniCPM-V-2
+MODEL="MiniCPM-o-2_6" # or "openbmb/MiniCPM-V-2_6", "openbmb/MiniCPM-Llama3-V-2_5", "openbmb/MiniCPM-V-2"
-DATA="path/to/trainging_data" # json file
+DATA="path/to/training_data.json"
-EVAL_DATA="path/to/test_data" # json file
+EVAL_DATA="path/to/test_data.json"
-LLM_TYPE="qwen" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm, if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE="llama3",
+LLM_TYPE="qwen" # llama for MiniCPM-V-4, minicpm for MiniCPM-V-2, llama3 for MiniCPM-Llama3-V-2_5, qwen for MiniCPM-o-2_6/MiniCPM-V-2_6
-# if use openbmb/MiniCPM-o-2_6 or openbmb/MiniCPM-V-2_6, please set LLM_TYPE=qwen
 ```
 
 To launch your training, run the following script: