diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp
index d1731bba6..c719077c9 100644
--- a/examples/cvector-generator/cvector-generator.cpp
+++ b/examples/cvector-generator/cvector-generator.cpp
@@ -422,8 +422,7 @@ int main(int argc, char ** argv) {
     int n_layers = llama_n_layer(model);
     int n_embd = llama_n_embd(model);
     // get model hint param (a.k.a model arch name)
-    char model_hint[128];
-    llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
+    const char* model_hint = llama_model_meta_val_str(model, "general.architecture");
 
     // init train_context
     train_context ctx_train(n_embd, n_layers);
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b8e003be9..edbeec0e7 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -663,14 +663,13 @@ struct server_context {
     bool validate_model_chat_template() const {
         std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
         std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        if (res >= 0) {
-            llama_chat_message chat[] = {{"user", "test"}};
-            std::string tmpl = std::string(model_template.data(), model_template.size());
-            int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0);
-            return chat_res > 0;
+        const char* tmpl = llama_model_meta_val_str(model, template_key.c_str());
+        if (tmpl == NULL) {
+            return false;
         }
-        return false;
+        llama_chat_message chat[] = {{"user", "test"}};
+        int32_t chat_res = llama_chat_apply_template(model, tmpl, chat, 1, true, nullptr, 0);
+        return chat_res > 0;
     }
 
     void init() {
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index c47ed3e47..6a8cd64d1 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -336,15 +336,8 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
 
 static std::string llama_get_chat_template(const struct llama_model * model) {
     std::string template_key = "tokenizer.chat_template";
-    // call with NULL buffer to get the total size of the string
-    int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0);
-    if (res < 0) {
-        return "";
-    } else {
-        std::vector<char> model_template(res, 0);
-        llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        return std::string(model_template.data(), model_template.size());
-    }
+    const char* tmpl = llama_model_meta_val_str(model, template_key.c_str());
+    return tmpl ? tmpl : "";
 }
 
 //
diff --git a/include/llama.h b/include/llama.h
index 90791d5f5..885fff9ef 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -449,12 +449,11 @@ extern "C" {
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
 
     // Functions to access the model's GGUF metadata scalar values
-    // - The functions return the length of the string on success, or -1 on failure
-    // - The output string is always null-terminated and cleared on failure
+    // - The functions return a const char* of the value on success, and NULL on failure.
     // - GGUF array values are not supported by these functions
 
     // Get metadata value as a string by key name
-    LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
+    LLAMA_API const char* llama_model_meta_val_str(const struct llama_model * model, const char * key);
 
     // Get the number of metadata key/value pairs
     LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
@@ -463,7 +462,7 @@ extern "C" {
     LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
 
     // Get metadata value as a string by index
-    LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
+    LLAMA_API const char* llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i);
 
     // Get a string describing the model type
     LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
diff --git a/src/llama.cpp b/src/llama.cpp
index c51b36e66..47ee8a993 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2887,7 +2887,7 @@ struct llama_model {
     std::vector<llama_layer> layers;
 
     // gguf metadata
-    std::unordered_map<std::string, std::string> gguf_kv;
+    std::unordered_map<std::string, char*> gguf_kv;
 
     llama_split_mode split_mode;
     int main_gpu;
@@ -5389,7 +5389,7 @@ static void llm_load_hparams(
         }
         const char * name = gguf_get_key(ctx, i);
         const std::string value = gguf_kv_to_str(ctx, i);
-        model.gguf_kv.emplace(name, value);
+        model.gguf_kv.emplace(name, strdup(value.c_str()));
     }
 
     // get general kv
@@ -20097,15 +20097,12 @@ float llama_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
 
-int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
+const char* llama_model_meta_val_str(const struct llama_model * model, const char * key) {
     const auto & it = model->gguf_kv.find(key);
     if (it == model->gguf_kv.end()) {
-        if (buf_size > 0) {
-            buf[0] = '\0';
-        }
-        return -1;
+        return NULL;
     }
-    return snprintf(buf, buf_size, "%s", it->second.c_str());
+    return it->second;
 }
 
 int32_t llama_model_meta_count(const struct llama_model * model) {
@@ -20124,16 +20121,13 @@ int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, c
     return snprintf(buf, buf_size, "%s", it->first.c_str());
 }
 
-int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
+const char* llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i) {
     if (i < 0 || i >= (int)model->gguf_kv.size()) {
-        if (buf_size > 0) {
-            buf[0] = '\0';
-        }
-        return -1;
+        return NULL;
     }
     auto it = model->gguf_kv.begin();
     std::advance(it, i);
-    return snprintf(buf, buf_size, "%s", it->second.c_str());
+    return it->second;
 }
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
@@ -22116,14 +22110,13 @@ int32_t llama_chat_apply_template(
     if (tmpl == nullptr) {
         GGML_ASSERT(model != nullptr);
         // load template from model
-        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
         std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        if (res < 0) {
+        const char* tmpl = llama_model_meta_val_str(model, template_key.c_str());
+        if (tmpl == NULL) {
             // worst case: there is no information about template, we will use chatml by default
             curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
         } else {
-            curr_tmpl = std::string(model_template.data(), model_template.size());
+            curr_tmpl = tmpl;
         }
     }