diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 649e354092f28b..b6ba8b81d81e48 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2705,7 +2705,7 @@ class StarCoder2Model(Model):
 
 @Model.register("Rwkv6ForCausalLM")
 class RwkvModel(Model):
-    model_arch = gguf.MODEL_ARCH.RWKV
+    model_arch = gguf.MODEL_ARCH.RWKV6
 
     def set_vocab(self):
         assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index f1fbad201eb93f..5298ae19b5830e 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -210,7 +210,7 @@ class MODEL_ARCH(IntEnum):
     GEMMA        = auto()
     GEMMA2       = auto()
     STARCODER2   = auto()
-    RWKV         = auto()
+    RWKV6        = auto()
     MAMBA        = auto()
     XVERSE       = auto()
     COMMAND_R    = auto()
@@ -362,7 +362,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.GEMMA:          "gemma",
     MODEL_ARCH.GEMMA2:         "gemma2",
     MODEL_ARCH.STARCODER2:     "starcoder2",
-    MODEL_ARCH.RWKV:           "rwkv",
+    MODEL_ARCH.RWKV6:          "rwkv6",
     MODEL_ARCH.MAMBA:          "mamba",
     MODEL_ARCH.XVERSE:         "xverse",
     MODEL_ARCH.COMMAND_R:      "command-r",
@@ -903,7 +903,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
-    MODEL_ARCH.RWKV: [
+    MODEL_ARCH.RWKV6: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.TOKEN_EMBD_NORM,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama.cpp b/src/llama.cpp
index 587f88ca89b445..f941c5abc87848 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -210,7 +210,7 @@ enum llm_arch {
     LLM_ARCH_T5,
     LLM_ARCH_T5ENCODER,
     LLM_ARCH_JAIS,
-    LLM_ARCH_RWKV,
+    LLM_ARCH_RWKV6,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -256,7 +256,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_T5,              "t5"           },
     { LLM_ARCH_T5ENCODER,       "t5encoder"    },
     { LLM_ARCH_JAIS,            "jais"         },
-    { LLM_ARCH_RWKV,            "rwkv"         },
+    { LLM_ARCH_RWKV6,           "rwkv6"        },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
@@ -1328,7 +1328,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
         },
     },
     {
-        LLM_ARCH_RWKV,
+        LLM_ARCH_RWKV6,
         {
             { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
             { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
@@ -3052,7 +3052,7 @@ static bool llama_kv_cache_init(
     cache.has_shift = false;
 
     // TODO: find a nicer way to add other recurrent model architectures
-    cache.recurrent = model.arch == LLM_ARCH_MAMBA || model.arch == LLM_ARCH_RWKV;
+    cache.recurrent = model.arch == LLM_ARCH_MAMBA || model.arch == LLM_ARCH_RWKV6;
     cache.v_trans   = !cache.recurrent && !cparams.flash_attn;
 
     cache.head = 0;
@@ -5353,7 +5353,7 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_RWKV:
+        case LLM_ARCH_RWKV6:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
@@ -7705,7 +7705,7 @@ static bool llm_load_tensors(
                         layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
                     }
                 } break;
-            case LLM_ARCH_RWKV:
+            case LLM_ARCH_RWKV6:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
@@ -14139,7 +14139,7 @@ struct llm_build_context {
         return gf;
     }
 
-    ggml_cgraph * build_rwkv() {
+    ggml_cgraph * build_rwkv6() {
         ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
 
         // Token shift state dimensions should be 2 * n_emb
@@ -14528,9 +14528,9 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_jais();
             } break;
-        case LLM_ARCH_RWKV:
+        case LLM_ARCH_RWKV6:
             {
-                result = llm.build_rwkv();
+                result = llm.build_rwkv6();
             } break;
         default:
             GGML_ABORT("fatal error");
@@ -17255,7 +17255,7 @@ struct llama_context * llama_new_context_with_model(
     ggml_type type_v = params.type_v;
 
     // Mamba and RWKV only need a constant number of KV cache cells per sequence
-    if (model->arch == LLM_ARCH_MAMBA || model->arch == LLM_ARCH_RWKV) {
+    if (model->arch == LLM_ARCH_MAMBA || model->arch == LLM_ARCH_RWKV6) {
         // Mamba and RWKV need at least as many KV cells as there are sequences kept at any time
         kv_size = std::max((uint32_t) 1, params.n_seq_max);
         // it's probably best to keep as much precision as possible for the states
@@ -17565,7 +17565,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_T5:
         case LLM_ARCH_T5ENCODER:
         case LLM_ARCH_JAIS:
-        case LLM_ARCH_RWKV:
+        case LLM_ARCH_RWKV6:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values