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+# MiniCPM Fine-tune
+
+This directory provides examples of fine-tuning the MiniCPM-2B model, including full model fine-tuning and PEFT. In terms of format, we offer examples for multi-turn dialogue fine-tuning and input-output format fine-tuning.
+
+If you have downloaded the model to your local system, the `OpenBMB/MiniCPM-2B` field mentioned in this document and in the code should be replaced with the corresponding address to load the model from your local system.
+
+Running the example requires `python>=3.10`. Besides the basic `torch` dependency, additional dependencies are needed to run the example code.
+
+
+
+**We have provided an [example notebook](lora_finetune.ipynb) to demonstrate how to process data and use the fine-tuning script with AdvertiseGen as an example.**
+
+```bash
+pip install -r requirements.txt
+```
+
+## Testing Hardware Standard
+
+We only provide examples for single-node multi-GPU/multi-node multi-GPU setups, so you will need at least one machine with multiple GPUs. In the **default configuration file** in this repository, we have documented the memory usage:
+
++ SFT full parameters fine-tuning: Evenly distributed across 4 GPUs, each GPU consumes `30245MiB` of memory.
++ LORA fine-tuning: One GPU, consuming `10619MiB`  of memory.。
+
+> Please note that these results are for reference only, and memory consumption may vary with different parameters. Please adjust according to your hardware situation.
+
+## Multi-Turn Dialogue Format
+
+The multi-turn dialogue fine-tuning example adopts the ChatGLM3 dialogue format convention, adding different `loss_mask` for different roles, thus calculating `loss` for multiple replies in one computation.
+
+For the data file, the example uses the following format
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "role": "system",
+        "content": "<system prompt text>"
+      },
+      {
+        "role": "user",
+        "content": "<user prompt text>"
+      },
+      {
+        "role": "assistant",
+        "content": "<assistant response text>"
+      },
+      // ... Muti Turn
+      {
+        "role": "user",
+        "content": "<user prompt text>"
+      },
+      {
+        "role": "assistant",
+        "content": "<assistant response text>"
+      }
+    ]
+  }
+  // ...
+]
+```
+
+## Dataset Format Example
+
+Here, taking the AdvertiseGen dataset as an example,
+you can download the AdvertiseGen dataset from [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing)
+or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b3f119a008264b1cabd1/?dl=1) . After extracting the AdvertiseGen directory, place it in the `data` directory and convert it into the following format dataset.
+
+
+> Please note, the fine-tuning code now includes a validation set, so for a complete set of fine-tuning datasets, it must contain training and validation datasets, while the test dataset is optional. Or, you can use the validation dataset in place of it.
+
+```
+{"conversations": [{"role": "user", "content": "类型#裙*裙长#半身裙"}, {"role": "assistant", "content": "这款百搭时尚的仙女半身裙，整体设计非常的飘逸随性，穿上之后每个女孩子都能瞬间变成小仙女啦。料子非常的轻盈，透气性也很好，穿到夏天也很舒适。"}]}
+```
+
+## Start Fine-tuning
+
+Execute **single-node multi-GPU/multi-node multi-GPU** runs with the following code.
+
+```bash
+cd finetune
+bash sft_finetune.sh
+```
+
+Execute **single-node single-GPU** runs with the following code.
+
+```angular2html
+cd finetune
+bash lora_finetune.sh
+```