Create sft.md

md/md_en/finetune/minicpm3.0/sft.md

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+# Official Code Fine-Tuning (SFT Recommended)
+
+## Device Requirements
+- At least one GPU with 24GB of VRAM, Series 20 or above
+- For QLoRA, a GPU with 12GB of VRAM can be attempted
+
+## Steps
+
+### 1. Obtain the Official Code Using Git
+```bash
+git clone https://github.com/OpenBMB/MiniCPM
+```
+
+### 2. Prepare the Dataset
+
+#### 2.1 Dialogue Dataset
+Process it into the following JSON format, where each entry represents a `messages` field. The system field marked in red is not mandatory.
+```json
+[
+  {
+    "messages": [
+      {
+        "role": "system",
+        "content": "<system prompt text>"
+      },
+      {
+        "role": "user",
+        "content": "<user prompt text>"
+      },
+      {
+        "role": "assistant",
+        "content": "<assistant response text>"
+      },
+      // ... Multi-turn dialogues
+      {
+        "role": "user",
+        "content": "<user prompt text>"
+      },
+      {
+        "role": "assistant",
+        "content": "<assistant response text>"
+      }
+    ]
+  }
+]
+```
+
+#### 2.2 Tool Invocation Dataset
+Refer to cold start for obtaining function call data.
+
+### 3. Modify the `MiniCPM/finetune/lora_finetune_ocnli.sh` File
+```bash
+formatted_time=$(date +"%Y%m%d%H%M%S")
+echo $formatted_time
+
+# Add these two lines for 4090
+export NCCL_P2P_DISABLE=1
+export NCCL_IB_DISABLE=1 
+
+deepspeed --include localhost:1 --master_port 19888 finetune.py \
+    --model_name_or_path openbmb/MiniCPM3-4B \ # Can be modified to the local model directory or the 1B model address
+    --output_dir output/OCNLILoRA/$formatted_time/ \ # Can be modified to another directory for saving the output model
+    --train_data_path data/ocnli_public_chatml/train.json \ # Specify the path to the training set processed as per step 2
+    --eval_data_path data/ocnli_public_chatml/dev.json \ # Specify the path to the validation set processed as per step 2
+    --learning_rate 5e-5 \ # Learning rate
+    --per_device_train_batch_size 16 \ # Batch size for training per device
+    --per_device_eval_batch_size 128 \ # Batch size for evaluation per device
+    --model_max_length 1024 \ # Maximum token length for training, will truncate if exceeded
+    --bf16 \ # Whether to use bf16 data format, change to false if not applicable
+    --use_lora \ # Whether to use LoRA
+    --gradient_accumulation_steps 1 \ # Gradient accumulation steps
+    --warmup_steps 100 \ # Warm-up steps
+    --max_steps 1000 \ # Maximum number of training steps, stop training when reached
+    --weight_decay 0.01 \ # Weight decay value
+    --evaluation_strategy steps \ # Evaluation method, can be changed to epoch
+    --eval_steps 500 \ # Works with evaluation_strategy steps, evaluate every 500 steps
+    --save_strategy steps \ # Model saving strategy, can be changed to epoch for saving once per epoch
+    --save_steps 500 \ # Works with save_strategy steps, save every 500 steps
+    --seed 42 \ # Random seed
+    --log_level info --logging_strategy steps --logging_steps 10 \ # Logging settings
+    --deepspeed configs/ds_config_zero2_offload.json # DeepSpeed configuration file setting, can be changed to configs/ds_config_zero2_offload.json if there's sufficient VRAM
+```
+
+### 4. (Optional) Train Using LoRA/QLoRA
+
+#### 4.1 LoRA Usage
+In the `MiniCPM/finetune/lora_finetune_ocnli.sh` file, add the `use_lora` parameter,
+if unsure, add the following code before `--deepspeed configs/ds_config_zero2_offload.json`
+```bash
+use_lora \
+```
+
+#### 4.2 QLoRA Usage
+In the `MiniCPM/finetune/lora_finetune_ocnli.sh` file, add both `use_lora` and `qlora` parameters,
+if unsure, add the following code before `--deepspeed configs/ds_config_zero2_offload.json`
+```bash
+use_lora \
+qlora \
+```
+
+### 5. Start Training
+After modifying the bash file, run the following commands to start training:
+```bash
+cd MiniCPM/finetune
+bash lora_finetune_ocnli.sh
+```