Update README.md

README.md

@@ -160,6 +160,53 @@ chat_prompt = tokenizer.apply_chat_template(
 )
 ```
 ---
+
+## 🪄 Using Specific Revisions (Training Checkpoints)
+
+KORMo provides multiple model revisions corresponding to different training stages and checkpoints.  
+You can load a specific revision with the `revision` parameter in `from_pretrained`.
+
+### 📍 Stage 1 Model (sft-stage1)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "KORMo-Team/KORMo-10B-sft"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    revision="sft-stage1",  # Load Stage 1 checkpoint
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+### 🚀 Main Model (Final Checkpoint: sft-stage2-ckpt2)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "KORMo-Team/KORMo-10B-sft"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    revision="sft-stage2-ckpt2",  # Load Final Main Checkpoint
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+> 💡 **Tip**:  
+> - Use `sft-stage1` for ablation studies or comparison experiments.  
+> - Use `sft-stage2-ckpt2` as the **main production model**.  
+
+---
+
+
 ## Contact
 - KyungTae Lim, Professor at KAIST. `ktlim@kaist.ac.kr`