diff --git a/common/train.cpp b/common/train.cpp
index 1eec3e3fb..3724d75c2 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -497,15 +497,11 @@ static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.
 static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s";
 static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y";
 
-static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
-static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA   = "finetune_lora";
-static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
 static const char * LLM_KV_TRAINING_FILE_VERSION         = "training.file_version";
 static const char * LLM_KV_TRAINING_ITERATION_COUNT      = "training.iteration_count";
 static const char * LLM_KV_TRAINING_SAMPLE_COUNT         = "training.sample_count";
 static const char * LLM_KV_TRAINING_TOKEN_COUNT          = "training.token_count";
 static const char * LLM_KV_TRAINING_EPOCH_COUNT          = "training.epoch_count";
-static const char * LLM_KV_TRAINING_SAMPLES_HASH         = "training.samples_hash";
 static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH = "training.shuffle.samples_hash";
 static const char * LLM_KV_TRAINING_SHUFFLE_RNG_STATE    = "training.shuffle.rng_state";
 static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT = "training.shuffle.sample_count";
@@ -661,10 +657,6 @@ bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_g
     GGUF_GET_KEY(fctx, file_version,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION);
     GGML_ASSERT(file_version <= 1);
 
-    std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
-    GGUF_GET_KEY(fctx, train_type,           gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
-    GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-
     if (file_version == 0) {
 
         GGUF_GET_KEY(fctx, train->train_its,     gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT);
@@ -690,7 +682,6 @@ bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_g
 
 void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train) {
     gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION,    1);
-    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE,            LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
     gguf_set_val_u64(fctx, LLM_KV_TRAINING_ITERATION_COUNT, train->train_its);
     gguf_set_val_u64(fctx, LLM_KV_TRAINING_SAMPLE_COUNT,    train->train_samples);
     gguf_set_val_u64(fctx, LLM_KV_TRAINING_TOKEN_COUNT,     train->train_tokens);
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 548075493..ae3582a54 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -151,6 +151,10 @@ struct my_llama_lora {
 };
 
 // gguf constants
+static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
+static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA   = "finetune_lora";
+static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
+
 static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD  = "training.lora.rank.token_embd";
 static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm";
 static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT      = "training.lora.rank.output";
@@ -994,11 +998,16 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod
 }
 
 static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
+    std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
+    GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
+    GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
+
     load_train_state_gguf(fctx, f_ggml_ctx, train);
+    load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
 }
 
 static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
+    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
     save_llama_lora_gguf(fctx, model, lora);
     save_train_state_gguf(fctx, train);
 }
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 88174e064..5c37776f3 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -68,6 +68,9 @@ struct my_llama_model {
 };
 
 // gguf constants (sync with gguf.py)
+static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
+static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA   = "finetune_lora";
+static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
 
 static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
 static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
@@ -654,12 +657,17 @@ static void save_llama_model_file(const char * filename, const char * fn_vocab_m
 
 static void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct train_state * train) {
     load_llama_model_gguf(fctx, f_ggml_ctx, model);
-    if (!load_train_state_gguf(fctx, f_ggml_ctx, train)) {
+    if (load_train_state_gguf(fctx, f_ggml_ctx, train)) {
+        std::string train_type = LLM_KV_TRAINING_TYPE_TRAIN_MODEL;
+        GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
+        GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
+    } else {
         printf("%s: loaded llama model as checkpoint\n", __func__);
     }
 }
 
 static void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
+    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
     save_llama_model_gguf(fctx, fn_vocab_model, model);
     save_train_state_gguf(fctx, train);
 }