diff --git a/mikazuki/app/api.py b/mikazuki/app/api.py
index 0049469..d719d27 100644
--- a/mikazuki/app/api.py
+++ b/mikazuki/app/api.py
@@ -148,7 +148,7 @@ async def create_toml_file(request: Request):
                 f.write(sample_prompts_arg)
 
             config["sample_prompts"] = sample_prompts_file
-            log.info(f"Wrote promopts to file {sample_prompts_file}")
+            log.info(f"Wrote prompts to file {sample_prompts_file}")
     except ValueError as e:
         return APIResponseFail(message=str(e))
 
diff --git a/mikazuki/schema/shared.ts b/mikazuki/schema/shared.ts
index 8fa93a8..d18e215 100644
--- a/mikazuki/schema/shared.ts
+++ b/mikazuki/schema/shared.ts
@@ -91,15 +91,23 @@
             Schema.object({}),
         ]),
 
-        SAVE_SETTINGS: Schema.object({
-            output_name: Schema.string().default("aki").description("模型保存名称"),
-            output_dir: Schema.string().role('filepicker', { type: "folder" }).default("./output").description("模型保存文件夹"),
-            save_model_as: Schema.union(["safetensors", "pt", "ckpt"]).default("safetensors").description("模型保存格式"),
-            save_precision: Schema.union(["fp16", "float", "bf16"]).default("fp16").description("模型保存精度"),
-            save_every_n_epochs: Schema.number().default(2).description("每 N epoch（轮）自动保存一次模型"),
-            save_state: Schema.boolean().description("保存训练状态 配合 `resume` 参数可以继续从某个状态训练"),
-        }).description("保存设置"),
-
+        SAVE_SETTINGS: Schema.intersect([
+            Schema.object({
+                output_name: Schema.string().default("aki").description("模型保存名称"),
+                output_dir: Schema.string().role('filepicker', { type: "folder" }).default("./output").description("模型保存文件夹"),
+                save_model_as: Schema.union(["safetensors", "pt", "ckpt"]).default("safetensors").description("模型保存格式"),
+                save_precision: Schema.union(["fp16", "float", "bf16"]).default("fp16").description("模型保存精度"),
+                save_every_n_epochs: Schema.number().default(2).description("每 N epoch（轮）自动保存一次模型"),
+                save_state: Schema.boolean().default(false).description("保存训练状态 配合 `resume` 参数可以继续从某个状态训练"),
+            }),
+            Schema.union([
+                Schema.object({
+                    save_state: Schema.const(true).required(),
+                    save_last_n_epochs_state: Schema.number().min(1).description("仅保存最后 n epoch 的训练状态"),
+                }),
+                Schema.object({})
+            ])
+        ]).description("保存设置"),
         LR_OPTIMIZER: Schema.intersect([
             Schema.object({
                 learning_rate: Schema.string().default("1e-4").description("总学习率, 在分开设置 U-Net 与文本编码器学习率后这个值失效。"),