diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert-finetune-checkpoint-to-gguf.py
index 4b9fa7c53..96d6633ed 100644
--- a/examples/finetune/convert-finetune-checkpoint-to-gguf.py
+++ b/examples/finetune/convert-finetune-checkpoint-to-gguf.py
@@ -131,7 +131,7 @@ class OptimizationContext:
     def load(self, data, offset):
         self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
         offset += 4
-        
+
         if self.version != 1:
             raise ValueError('Invalid version of optimization context in checkpoint file')
 
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 48b44366c..4230b1449 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -1468,7 +1468,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
 
 void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {
     // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-    
+
     std::string arch;
 
     std::vector<char> keybuf;
@@ -1525,7 +1525,7 @@ void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_gg
     read_tensor_by_name(lora->norm_b,           f_ggml_ctx, ggml_get_name(lora->norm_b));
     read_tensor_by_name(lora->output_a,         f_ggml_ctx, ggml_get_name(lora->output_a));
     read_tensor_by_name(lora->output_b,         f_ggml_ctx, ggml_get_name(lora->output_b));
-    
+
     for (uint32_t i = 0; i < lora->layers.size(); ++i) {
         auto & layer = lora->layers[i];
         read_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));
@@ -1565,7 +1565,7 @@ void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * mo
 
     gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx);
     gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd);
-    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff); 
+    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff);
     gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head);
     gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer);
     gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_rot);
diff --git a/ggml.c b/ggml.c
index 681891c8c..49aff6243 100644
--- a/ggml.c
+++ b/ggml.c
@@ -16497,7 +16497,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 if (src0->grad) {
                     src0->grad =
                         ggml_add_or_set(ctx, src0->grad,
-                            // last ggml_get_rows_back argument src0->grad is only 
+                            // last ggml_get_rows_back argument src0->grad is only
                             // necessary to setup correct output shape
                             ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad),
                         zero_table);