diff --git a/common/common.cpp b/common/common.cpp
index 8503da88a..e2faeee0d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -724,7 +724,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         return std::make_tuple(nullptr, nullptr);
     }
 
-    for (int i = 0; i < params.lora_adapter.size(); ++i) {
+    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
         const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
         float lora_scale = std::get<1>(params.lora_adapter[i]);
         int err = llama_model_apply_lora_from_file(model,
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 9da63d8c9..b092bd612 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -406,18 +406,13 @@ void init_model(struct llama_model * input, struct my_llama_model * model, uint3
     hparams.n_layer = llama_model_n_layer(input);
     hparams.n_rot   = llama_model_n_rot(input);
 
-    const uint32_t n_embd  = hparams.n_embd;
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_vocab = hparams.n_vocab;
-    const uint32_t n_ff    = hparams.n_ff;
-
     model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
     model->norm           = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
     model->output         = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));
 
-    model->layers.resize(n_layer);
+    model->layers.resize(hparams.n_layer);
 
-    for (uint32_t i = 0; i < n_layer; ++i) {
+    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
         auto & layer = model->layers[i];
 
         layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));
@@ -654,7 +649,7 @@ struct ggml_tensor * llama_build_lora_finetune_graphs(
     const float rope_freq_base  = lora->hparams.rope_freq_base;
     const float rope_freq_scale = lora->hparams.rope_freq_scale;
 
-    GGML_ASSERT(n_layer == lora->layers.size());
+    GGML_ASSERT((size_t) n_layer == lora->layers.size());
 
     auto set_name = [](struct ggml_tensor * t, const char * n) {
         ggml_set_name(t, n);
@@ -828,15 +823,12 @@ struct ggml_tensor * llama_build_lora_finetune_graphs(
 
         // allocating checkpoints in one block to reduce memory fragmentation
         // note: they will be freed in reverse order
-        for (int i = 0; i < checkpoints.size(); ++i) {
+        for (unsigned int i = 0; i < checkpoints.size(); ++i) {
             if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
                 ggml_allocr_alloc(alloc, checkpoints[i]);
             }
         }
 
-        int n_leafs_after = gb->n_leafs;
-        int n_nodes_after = gb->n_nodes;
-
         ggml_allocr_alloc_graph(alloc, gb);
 
         // remove the additional nodes and leafs
diff --git a/ggml.c b/ggml.c
index 77abf7c8f..8ade33957 100644
--- a/ggml.c
+++ b/ggml.c
@@ -4851,7 +4851,6 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
 }
 
 void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) {
-    const int64_t ne3 = tensor->ne[3];
     const int64_t ne2 = tensor->ne[2];
     const int64_t ne1 = tensor->ne[1];
     const int64_t ne0 = tensor->ne[0];
@@ -16214,16 +16213,16 @@ struct hash_map {
     void * vals[GGML_GRAPH_HASHTABLE_SIZE];
 };
 
-struct hash_map * new_hash_map() {
+static struct hash_map * new_hash_map(void) {
     struct hash_map * result = malloc(sizeof(struct hash_map));
     for (int i=0; i<GGML_GRAPH_HASHTABLE_SIZE; ++i) {
         result->keys[i] = NULL;
         result->vals[i] = NULL;
     }
     return result;
-};
+}
 
-void free_hash_map(struct hash_map * map) {
+static void free_hash_map(struct hash_map * map) {
     free(map);
 }
 
@@ -19176,7 +19175,6 @@ static enum ggml_opt_result linesearch_backtracking(
         float * step,
         const float * xp,
         struct ggml_tensor * f,
-        struct ggml_cgraph * gf,
         struct ggml_cgraph * gb,
         struct ggml_cplan  * cplan,
         const int np,
@@ -19421,7 +19419,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         ggml_vec_cpy_f32(nx, xp, x);
         ggml_vec_cpy_f32(nx, gp, g);
 
-        ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gf, gb, &cplan, np, ps, callback, callback_data);
+        ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, callback, callback_data);
 
         if (ls < 0) {
             // linesearch failed - go back to the previous point and return