diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt
index 5fe604329..991896116 100644
--- a/awq-py/requirements.txt
+++ b/awq-py/requirements.txt
@@ -1,2 +1,2 @@
-torch>=2.0.0
+torch>=2.1.1
 transformers>=4.32.0
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 51724c0df..203eaf64b 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -59,7 +59,7 @@ class Model:
                 from safetensors import safe_open
                 ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
             else:
-                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", weights_only=True))
+                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
 
             with ctx as model_part:
                 for name in model_part.keys():
diff --git a/examples/finetune/README.md b/examples/finetune/README.md
index a2a2c1281..a884706c5 100644
--- a/examples/finetune/README.md
+++ b/examples/finetune/README.md
@@ -61,7 +61,7 @@ For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' L
   --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
 ```
 
-The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values to big will sometimes result in worse output. Play around to find good values.
+The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values.
 
 Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
 If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
index f0a071a69..fe5f81228 100644
--- a/examples/server/completion.js.hpp
+++ b/examples/server/completion.js.hpp
@@ -74,355 +74,376 @@ unsigned char completion_js[] = {
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 };
-unsigned int completion_js_len = 5099;
+unsigned int completion_js_len = 5346;
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index f22b77e7f..20551520e 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -383,2380 +383,2409 @@ unsigned char index_html[] = {
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 };
-unsigned int index_html_len = 33103;
+unsigned int index_html_len = 33456;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
index c9dc078b7..e09b3c8c5 100644
--- a/examples/server/index.js.hpp
+++ b/examples/server/index.js.hpp
@@ -2,1875 +2,1902 @@ unsigned char index_js[] = {
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+  0x5d, 0x3a, 0x6f, 0x3f, 0x6e, 0x3d, 0x3d, 0x3d, 0x6f, 0x3f, 0x6f, 0x3d,
+  0x22, 0x22, 0x3a, 0x69, 0x2b, 0x3d, 0x6e, 0x3a, 0x27, 0x22, 0x27, 0x3d,
+  0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x27, 0x22, 0x3d, 0x3d, 0x3d, 0x6e,
+  0x3f, 0x6f, 0x3d, 0x6e, 0x3a, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, 0x6e,
+  0x3f, 0x28, 0x75, 0x28, 0x29, 0x2c, 0x5f, 0x3d, 0x31, 0x29, 0x3a, 0x5f,
+  0x26, 0x26, 0x28, 0x22, 0x3d, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x3f, 0x28,
+  0x5f, 0x3d, 0x35, 0x2c, 0x65, 0x3d, 0x69, 0x2c, 0x69, 0x3d, 0x22, 0x22,
+  0x29, 0x3a, 0x22, 0x2f, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x28,
+  0x5f, 0x3c, 0x35, 0x7c, 0x7c, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, 0x74,
+  0x5b, 0x66, 0x5d, 0x5b, 0x73, 0x2b, 0x31, 0x5d, 0x29, 0x3f, 0x28, 0x75,
+  0x28, 0x29, 0x2c, 0x33, 0x3d, 0x3d, 0x3d, 0x5f, 0x26, 0x26, 0x28, 0x72,
+  0x3d, 0x72, 0x5b, 0x30, 0x5d, 0x29, 0x2c, 0x5f, 0x3d, 0x72, 0x2c, 0x28,
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+  0x28, 0x32, 0x2c, 0x30, 0x2c, 0x5f, 0x29, 0x2c, 0x5f, 0x3d, 0x30, 0x29,
+  0x3a, 0x22, 0x20, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c,
+  0x74, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c, 0x6e, 0x22,
+  0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c, 0x72, 0x22, 0x3d, 0x3d,
+  0x3d, 0x6e, 0x3f, 0x28, 0x75, 0x28, 0x29, 0x2c, 0x5f, 0x3d, 0x32, 0x29,
+  0x3a, 0x69, 0x2b, 0x3d, 0x6e, 0x29, 0x2c, 0x33, 0x3d, 0x3d, 0x3d, 0x5f,
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+  0x2c, 0x72, 0x7d, 0x28, 0x74, 0x29, 0x29, 0x2c, 0x6e, 0x29, 0x2c, 0x61,
+  0x72, 0x67, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x73, 0x2c, 0x5b, 0x5d, 0x29,
+  0x29, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3e, 0x31, 0x3f, 0x6e,
+  0x3a, 0x6e, 0x5b, 0x30, 0x5d, 0x7d, 0x76, 0x61, 0x72, 0x20, 0x5f, 0x6e,
+  0x3d, 0x65, 0x6e, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x4c, 0x29, 0x3b,
+  0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x7b, 0x49, 0x20, 0x61, 0x73, 0x20,
+  0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x2c, 0x6a, 0x20,
+  0x61, 0x73, 0x20, 0x46, 0x72, 0x61, 0x67, 0x6d, 0x65, 0x6e, 0x74, 0x2c,
+  0x68, 0x20, 0x61, 0x73, 0x20, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c,
+  0x5f, 0x20, 0x61, 0x73, 0x20, 0x62, 0x61, 0x74, 0x63, 0x68, 0x2c, 0x63,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x63, 0x6c, 0x6f, 0x6e, 0x65, 0x45, 0x6c,
+  0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x6d, 0x20, 0x61, 0x73, 0x20, 0x63,
+  0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x2c, 0x68, 0x74, 0x20, 0x61,
+  0x73, 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x43, 0x6f, 0x6e, 0x74,
+  0x65, 0x78, 0x74, 0x2c, 0x4c, 0x20, 0x61, 0x73, 0x20, 0x63, 0x72, 0x65,
+  0x61, 0x74, 0x65, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x52,
+  0x20, 0x61, 0x73, 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x52, 0x65,
+  0x66, 0x2c, 0x77, 0x20, 0x61, 0x73, 0x20, 0x65, 0x66, 0x66, 0x65, 0x63,
+  0x74, 0x2c, 0x4c, 0x20, 0x61, 0x73, 0x20, 0x68, 0x2c, 0x5f, 0x6e, 0x20,
+  0x61, 0x73, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x2c, 0x6c, 0x74, 0x20, 0x61,
+  0x73, 0x20, 0x68, 0x79, 0x64, 0x72, 0x61, 0x74, 0x65, 0x2c, 0x55, 0x20,
+  0x61, 0x73, 0x20, 0x69, 0x73, 0x56, 0x61, 0x6c, 0x69, 0x64, 0x45, 0x6c,
+  0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x43, 0x20, 0x61, 0x73, 0x20, 0x6f,
+  0x70, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x2c, 0x73, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x2c, 0x61, 0x20, 0x61, 0x73,
+  0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x58, 0x20, 0x61, 0x73,
+  0x20, 0x74, 0x6f, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x41, 0x72, 0x72, 0x61,
+  0x79, 0x2c, 0x75, 0x20, 0x61, 0x73, 0x20, 0x75, 0x6e, 0x74, 0x72, 0x61,
+  0x63, 0x6b, 0x65, 0x64, 0x2c, 0x54, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x43, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2c, 0x59,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x43, 0x6f, 0x6d, 0x70,
+  0x75, 0x74, 0x65, 0x64, 0x2c, 0x56, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x2c, 0x41, 0x74,
+  0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x44, 0x65, 0x62, 0x75, 0x67,
+  0x56, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x48, 0x74, 0x20, 0x61, 0x73, 0x20,
+  0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x46, 0x74,
+  0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x45, 0x72, 0x72, 0x6f, 0x72,
+  0x42, 0x6f, 0x75, 0x6e, 0x64, 0x61, 0x72, 0x79, 0x2c, 0x4d, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x49, 0x64, 0x2c, 0x24, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x49, 0x6d, 0x70, 0x65, 0x72, 0x61,
+  0x74, 0x69, 0x76, 0x65, 0x48, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x2c, 0x50,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4c, 0x61, 0x79, 0x6f,
+  0x75, 0x74, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x44, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4d, 0x65, 0x6d, 0x6f, 0x2c, 0x55,
+  0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x64, 0x75,
+  0x63, 0x65, 0x72, 0x2c, 0x4e, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73,
+  0x65, 0x52, 0x65, 0x66, 0x2c, 0x58, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x5a, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c,
+  0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x45, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
 };
-unsigned int index_js_len = 22472;
+unsigned int index_js_len = 22800;
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 07d779d20..b059c75f2 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -427,7 +427,7 @@
         }
 
         if (data.timings) {
-          llamaStats.value = data.timings;
+          llamaStats.value = data;
         }
       }
 
@@ -880,7 +880,7 @@
       }
       return html`
         <span>
-          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+          ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
         </span>
       `
     }
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 2d5554a88..418d3fb4c 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2017,6 +2017,10 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA.\n");
     printf("  --log-disable         disables logging to a file.\n");
     printf("\n");
+    printf("  --override-kv KEY=TYPE:VALUE\n");
+    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
+    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("\n");
 }
 
 static void server_params_parse(int argc, char **argv, server_params &sparams,
@@ -2380,6 +2384,49 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             log_set_target(stdout);
             LOG_INFO("logging to file is disabled.", {});
         }
+        else if (arg == "--override-kv")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            char * sep = strchr(argv[i], '=');
+            if (sep == nullptr || sep - argv[i] >= 128) {
+                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            struct llama_model_kv_override kvo;
+            std::strncpy(kvo.key, argv[i], sep - argv[i]);
+            kvo.key[sep - argv[i]] = 0;
+            sep++;
+            if (strncmp(sep, "int:", 4) == 0) {
+                sep += 4;
+                kvo.tag = LLAMA_KV_OVERRIDE_INT;
+                kvo.int_value = std::atol(sep);
+            } else if (strncmp(sep, "float:", 6) == 0) {
+                sep += 6;
+                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
+                kvo.float_value = std::atof(sep);
+            } else if (strncmp(sep, "bool:", 5) == 0) {
+                sep += 5;
+                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
+                if (std::strcmp(sep, "true") == 0) {
+                    kvo.bool_value = true;
+                } else if (std::strcmp(sep, "false") == 0) {
+                    kvo.bool_value = false;
+                } else {
+                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
+                    invalid_param = true;
+                    break;
+                }
+            } else {
+                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            params.kv_overrides.push_back(kvo);
+        }
         else
         {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@@ -2387,6 +2434,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             exit(1);
         }
     }
+    if (!params.kv_overrides.empty()) {
+        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.back().key[0] = 0;
+    }
 
     if (invalid_param)
     {
diff --git a/ggml-metal.m b/ggml-metal.m
index 2022d21cb..686f14c59 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -257,13 +257,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
+        NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"];
         if (libPath != nil) {
+            // pre-compiled library found
             NSURL * libURL = [NSURL fileURLWithPath:libPath];
             GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
             ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
         } else {
-            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+            GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__);
 
             NSString * sourcePath;
             NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
@@ -291,6 +292,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
             options = [MTLCompileOptions new];
             options.preprocessorMacros = @{ @"QK_K" : @(64) };
 #endif
+            // try to disable fast-math
+            // NOTE: this seems to have no effect whatsoever
+            //       instead, in order to disable fast-math, we have to build ggml.metallib from the command line
+            //       using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            //       and go through the "pre-compiled library found" path above
+            //[options setFastMathEnabled:false];
+
             ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
         }
 
@@ -1230,7 +1238,7 @@ void ggml_metal_graph_compute(
                                 // not sure how to avoid this
                                 // TODO: make a simpler cpy_bytes kernel
 
-                                const int nth = MIN(1024, ne00);
+                                const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00);
 
                                 [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32];
                                 [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
@@ -1285,7 +1293,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26];
                             [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
 
-                            const int nth = MIN(1024, ne0);
+                            const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00);
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
@@ -1649,6 +1657,10 @@ void ggml_metal_graph_compute(
                                         }
                                 };
 
+                                if (ggml_is_quantized(src0t)) {
+                                    GGML_ASSERT(ne00 >= nth0*nth1);
+                                }
+
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
@@ -1707,6 +1719,9 @@ void ggml_metal_graph_compute(
                             // TODO: make this more general
                             GGML_ASSERT(n_as <= 8);
 
+                            // max size of the src1ids array in the kernel stack
+                            GGML_ASSERT(ne11 <= 512);
+
                             struct ggml_tensor * src2 = gf->nodes[i]->src[2];
 
                             const int64_t  ne20 = src2 ? src2->ne[0] : 0;
@@ -1724,9 +1739,6 @@ void ggml_metal_graph_compute(
                             GGML_ASSERT(!ggml_is_transposed(src2));
                             GGML_ASSERT(!ggml_is_transposed(src1));
 
-                            GGML_ASSERT(ne20 % 32 == 0);
-                            // !!!!!!!!! TODO: this assert is probably required but not sure!
-                            //GGML_ASSERT(ne20 >= 64);
                             GGML_ASSERT(src1t == GGML_TYPE_F32);
 
                             const uint r2 = ne12/ne22;
@@ -1734,22 +1746,22 @@ void ggml_metal_graph_compute(
 
                             // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                             // to the matrix-vector kernel
-                            int ne11_mm_min = 1;
+                            int ne11_mm_min = n_as;
 
                             const int idx = ((int32_t *) dst->op_params)[0];
 
                             // batch size
                             GGML_ASSERT(ne01 == ne11);
 
-                            const int64_t _ne1 = 1; // kernel_mul_mm_impl needs a reference in constant memory
-
                             // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                             // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
                             // !!!
                             // TODO: for now, always use mat-vec kernels until we figure out how to improve the
                             //       indirect matrix multiplication
                             // !!!
-                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && _ne1 > ne11_mm_min) {
+                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
+                                ne20 % 32 == 0 && ne20 >= 64 &&
+                                ne11 > ne11_mm_min) {
                                 switch (src2->type) {
                                     case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32];  break;
                                     case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32];  break;
@@ -1779,14 +1791,15 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:11];
                                 [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:12];
                                 [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:13];
-                                [encoder setBytes:&_ne1    length:sizeof(_ne1) atIndex:14];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:14];
                                 [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:15];
                                 [encoder setBytes:&r2      length:sizeof(r2)   atIndex:16];
                                 [encoder setBytes:&r3      length:sizeof(r3)   atIndex:17];
                                 [encoder setBytes:&idx     length:sizeof(idx)  atIndex:18];
                                 // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < n_as; ++j) {
-                                    struct ggml_tensor * src_cur = dst->src[2 + j];
+                                for (int j = 0; j < 8; ++j) {
+                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
+                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
 
                                     size_t offs_src_cur = 0;
                                     id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
@@ -1796,8 +1809,7 @@ void ggml_metal_graph_compute(
 
                                 [encoder setThreadgroupMemoryLength:8192 atIndex:0];
 
-                                // TODO: processing one row at a time (ne11 -> 1) is not efficient
-                                [encoder dispatchThreadgroups:MTLSizeMake( (_ne1 + 31)/32, (ne21 + 63)/64, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                             } else {
                                 int nth0 = 32;
                                 int nth1 = 1;
@@ -1880,11 +1892,17 @@ void ggml_metal_graph_compute(
                                         } break;
                                     default:
                                         {
-                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
+                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
                                             GGML_ASSERT(false && "not implemented");
                                         }
                                 };
 
+                                if (ggml_is_quantized(src2t)) {
+                                    GGML_ASSERT(ne20 >= nth0*nth1);
+                                }
+
+                                const int64_t _ne1 = 1; // kernels needs a reference in constant memory
+
                                 [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                                 [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                                 [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
@@ -1909,8 +1927,9 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&r3   length:sizeof(r3)   atIndex:21];
                                 [encoder setBytes:&idx  length:sizeof(idx)  atIndex:22];
                                 // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < n_as; ++j) {
-                                    struct ggml_tensor * src_cur = dst->src[2 + j];
+                                for (int j = 0; j < 8; ++j) {
+                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
+                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
 
                                     size_t offs_src_cur = 0;
                                     id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
@@ -2229,7 +2248,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
                             [encoder setBytes:&sf   length:sizeof(sf)   atIndex:18];
 
-                            const int nth = MIN(1024, ne0);
+                            const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0);
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index d5b54e112..9aa7b502a 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -59,26 +59,26 @@ kernel void kernel_add(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         constant  int64_t & offs,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
@@ -109,26 +109,26 @@ kernel void kernel_mul(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
         uint3   ntg[[threads_per_threadgroup]]) {
@@ -158,26 +158,26 @@ kernel void kernel_div(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]],
         uint3   ntg[[threads_per_threadgroup]]) {
@@ -205,7 +205,7 @@ kernel void kernel_add_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb [[buffer(28)]],
+        constant   uint64_t & nb [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] + src1[tpig % nb];
 }
@@ -214,7 +214,7 @@ kernel void kernel_mul_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb  [[buffer(28)]],
+        constant   uint64_t & nb  [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * src1[tpig % nb];
 }
@@ -223,7 +223,7 @@ kernel void kernel_div_row(
         device const float4 * src0,
         device const float4 * src1,
         device       float4 * dst,
-        constant    int64_t & nb  [[buffer(28)]],
+        constant   uint64_t & nb  [[buffer(28)]],
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] / src1[tpig % nb];
 }
@@ -307,26 +307,26 @@ kernel void kernel_sum_rows(
         constant  int64_t & ne01,
         constant  int64_t & ne02,
         constant  int64_t & ne03,
-        constant  int64_t & nb00,
-        constant  int64_t & nb01,
-        constant  int64_t & nb02,
-        constant  int64_t & nb03,
+        constant uint64_t & nb00,
+        constant uint64_t & nb01,
+        constant uint64_t & nb02,
+        constant uint64_t & nb03,
         constant  int64_t & ne10,
         constant  int64_t & ne11,
         constant  int64_t & ne12,
         constant  int64_t & ne13,
-        constant  int64_t & nb10,
-        constant  int64_t & nb11,
-        constant  int64_t & nb12,
-        constant  int64_t & nb13,
+        constant uint64_t & nb10,
+        constant uint64_t & nb11,
+        constant uint64_t & nb12,
+        constant uint64_t & nb13,
         constant  int64_t & ne0,
         constant  int64_t & ne1,
         constant  int64_t & ne2,
         constant  int64_t & ne3,
-        constant  int64_t & nb0,
-        constant  int64_t & nb1,
-        constant  int64_t & nb2,
-        constant  int64_t & nb3,
+        constant uint64_t & nb0,
+        constant uint64_t & nb1,
+        constant uint64_t & nb2,
+        constant uint64_t & nb3,
         uint3 tpig[[thread_position_in_grid]]) {
     int64_t i3 = tpig.z;
     int64_t i2 = tpig.y;
@@ -846,7 +846,7 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre
 #define N_SIMDGROUP 2  // number of SIMD groups in a thread group
 //Note: This is a template, but strictly speaking it only applies to
 //      quantizations where the block size is 32. It also does not
-//      giard against the number of rows not being divisible by
+//      guard against the number of rows not being divisible by
 //      N_DST, so this is another explicit assumption of the implementation.
 template<typename block_q_type, int nr, int nsg, int nw>
 void mul_vec_q_n_f32_impl(
@@ -920,14 +920,21 @@ kernel void kernel_mul_mv_q4_0_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -939,14 +946,21 @@ kernel void kernel_mul_mv_q4_1_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -958,14 +972,21 @@ kernel void kernel_mul_mv_q5_0_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -977,14 +998,21 @@ kernel void kernel_mul_mv_q5_1_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -1071,12 +1099,19 @@ kernel void kernel_mul_mv_q8_0_f32(
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
         constant   int64_t & ne10,
+        constant   int64_t & ne11,
         constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -1182,8 +1217,8 @@ kernel void kernel_mul_mv_f32_f32(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1209,8 +1244,8 @@ kernel void kernel_mul_mv_f16_f16(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
 
@@ -1346,8 +1381,8 @@ kernel void kernel_mul_mv_f16_f32_1row(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1452,8 +1487,8 @@ kernel void kernel_mul_mv_f16_f32(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]]) {
     kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
@@ -1478,8 +1513,8 @@ kernel void kernel_mul_mv_f16_f32_l4(
         constant  uint64_t & nb12,
         constant   int64_t & ne0,
         constant   int64_t & ne1,
-        constant   uint    & r2   [[buffer(17)]],
-        constant   uint    & r3   [[buffer(18)]],
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]]) {
 
@@ -1543,7 +1578,8 @@ kernel void kernel_alibi_f32(
     const int64_t i3 = n / (ne2*ne1*ne0);
     const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
     const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+  //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
     const int64_t k = i3*ne3 + i2;
 
     float m_k;
@@ -2410,22 +2446,6 @@ typedef struct {
 } block_q6_K;
 // 210 bytes / block
 
-static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
-    uchar4 r;
-    if (j < 4) {
-        r[0] = q[j+0] & 63;
-        r[2] = q[j+1] & 63;
-        r[1] = q[j+4] & 63;
-        r[3] = q[j+5] & 63;
-    } else {
-        r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4);
-        r[1] = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
-        r[3] = (q[j+5] >>  4) | ((q[j+1] >> 6) << 4);
-    }
-    return r;
-}
-
 //====================================== dot products =========================
 
 void kernel_mul_mv_q2_K_f32_impl(
@@ -2584,14 +2604,21 @@ kernel void kernel_mul_mv_q2_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -2841,14 +2868,21 @@ kernel void kernel_mul_mv_q3_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -2984,8 +3018,8 @@ void kernel_mul_mv_q4_K_f32_impl(
         constant   uint    & r2,
         constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
-        uint tiisg[[thread_index_in_simdgroup]],
-        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
 
     const int ix = tiisg/4;  // 0...7
     const int it = tiisg%4;  // 0...3
@@ -2994,7 +3028,7 @@ void kernel_mul_mv_q4_K_f32_impl(
     const int r0 = tgpig.x;
     const int r1 = tgpig.y;
     const int im = tgpig.z;
-    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int first_row = r0 * N_DST;
     const int ib_row = first_row * nb;
 
     const uint i12 = im%ne12;
@@ -3060,7 +3094,7 @@ void kernel_mul_mv_q4_K_f32_impl(
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
-            dst[r1*ne0+ im*ne0*ne1 + first_row + row] = all_sum;
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
         }
     }
 }
@@ -3072,14 +3106,21 @@ kernel void kernel_mul_mv_q4_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint tiisg[[thread_index_in_simdgroup]],
         uint sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3271,14 +3312,21 @@ kernel void kernel_mul_mv_q5_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3398,14 +3446,21 @@ kernel void kernel_mul_mv_q6_K_f32(
         device const float * src1,
         device       float * dst,
         constant   int64_t & ne00,
-        constant   int64_t & ne01[[buffer(4)]],
-        constant   int64_t & ne02[[buffer(5)]],
-        constant   int64_t & ne10[[buffer(9)]],
-        constant   int64_t & ne12[[buffer(11)]],
-        constant   int64_t & ne0 [[buffer(15)]],
-        constant   int64_t & ne1 [[buffer(16)]],
-        constant   uint    & r2  [[buffer(17)]],
-        constant   uint    & r3  [[buffer(18)]],
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]]) {
@@ -3523,7 +3578,7 @@ void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg
     device const int8_t * qs = ((device const int8_t *)xb->qs);
     const half d = xb->d;
 
-    for (int i=0;i<16;i++) {
+    for (int i = 0; i < 16; i++) {
         reg[i/4][i%4] = (qs[i + 16*il] * d);
     }
 }
@@ -3792,12 +3847,12 @@ void kernel_mul_mm_impl(device const  uchar * src0,
                         device        float * dst,
                         constant    int64_t & ne00,
                         constant    int64_t & ne02,
-                        constant    int64_t & nb01,
-                        constant    int64_t & nb02,
+                        constant   uint64_t & nb01,
+                        constant   uint64_t & nb02,
                         constant    int64_t & ne12,
-                        constant    int64_t & nb10,
-                        constant    int64_t & nb11,
-                        constant    int64_t & nb12,
+                        constant   uint64_t & nb10,
+                        constant   uint64_t & nb11,
+                        constant   uint64_t & nb12,
                         constant    int64_t & ne0,
                         constant    int64_t & ne1,
                         constant       uint & r2,
@@ -3918,18 +3973,143 @@ void kernel_mul_mm_impl(device const  uchar * src0,
     }
 }
 
+// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids
+template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
+void kernel_mul_mm_id_impl(
+        device const  uchar * src0,
+        device const  uchar * src1,
+        thread        short * src1ids,
+        device        float * dst,
+        constant    int64_t & ne00,
+        constant    int64_t & ne02,
+        constant   uint64_t & nb01,
+        constant   uint64_t & nb02,
+        constant    int64_t & ne12,
+        constant   uint64_t & nb10,
+        constant   uint64_t & nb11,
+        constant   uint64_t & nb12,
+        constant    int64_t & ne0,
+                    int64_t   ne1,
+        constant       uint & r2,
+        constant       uint & r3,
+        threadgroup   uchar * shared_memory,
+        uint3                 tgpig[[threadgroup_position_in_grid]],
+        uint                  tiitg[[thread_index_in_threadgroup]],
+        uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    threadgroup half  * sa = (threadgroup half  *)(shared_memory);
+    threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
+
+    const uint r0 = tgpig.y;
+    const uint r1 = tgpig.x;
+    const uint im = tgpig.z;
+
+    if (r1 * BLOCK_SIZE_N >= ne1) return;
+
+    // if this block is of 64x32 shape or smaller
+    short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M;
+    short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N;
+
+    // a thread shouldn't load data outside of the matrix
+    short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
+    short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
+
+    simdgroup_half8x8  ma[4];
+    simdgroup_float8x8 mb[2];
+    simdgroup_float8x8 c_res[8];
+    for (int i = 0; i < 8; i++){
+        c_res[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
+    }
+
+    short il = (tiitg % THREAD_PER_ROW);
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    uint   offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02);
+    ushort offset1 = il/nl;
+
+    device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1;
+    device const float   * y = (device const float   *)(src1
+        + nb12 * im
+        + nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col]
+        + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
+
+    for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
+        // load data and store to threadgroup memory
+        half4x4 temp_a;
+        dequantize_func(x, il, temp_a);
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        for (int i = 0; i < 16; i++) {
+            *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \
+            +                     (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \
+            +                     (tiitg / THREAD_PER_ROW) % 8  + (i & 7) * 8) = temp_a[i/4][i%4];
+        }
+
+        *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y);
+
+        il = (il + 2 < nl) ? il + 2 : il % 2;
+        x  = (il < 2) ? x + (2+nl-1)/nl : x;
+        y += BLOCK_SIZE_K;
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        // load matrices from threadgroup memory and conduct outer products
+        threadgroup half  * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
+        threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
+
+        for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) {
+            for (int i = 0; i < 4; i++) {
+                simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i);
+            }
+            simdgroup_barrier(mem_flags::mem_none);
+            for (int i = 0; i < 2; i++) {
+                simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i);
+            }
+
+            lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE;
+            lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE;
+
+            for (int i = 0; i < 8; i++){
+                simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]);
+            }
+        }
+    }
+
+    {
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+        threadgroup float * temp_str = ((threadgroup float *)shared_memory) \
+                                      + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
+        for (int i = 0; i < 8; i++) {
+            simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0;
+        if (sgitg == 0) {
+            for (int i = 0; i < n_rows; i++) {
+                for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
+                    *(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
+                }
+            }
+        }
+    }
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm(device const  uchar * src0,
                           device const  uchar * src1,
                           device        float * dst,
                           constant    int64_t & ne00,
                           constant    int64_t & ne02,
-                          constant    int64_t & nb01,
-                          constant    int64_t & nb02,
+                          constant   uint64_t & nb01,
+                          constant   uint64_t & nb02,
                           constant    int64_t & ne12,
-                          constant    int64_t & nb10,
-                          constant    int64_t & nb11,
-                          constant    int64_t & nb12,
+                          constant   uint64_t & nb10,
+                          constant   uint64_t & nb11,
+                          constant   uint64_t & nb12,
                           constant    int64_t & ne0,
                           constant    int64_t & ne1,
                           constant       uint & r2,
@@ -3964,20 +4144,20 @@ template<typename block_q, short nl, void (*dequantize_func)(device const block_
 kernel void kernel_mul_mm_id(
         device const   uchar * ids,
         device const   uchar * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
-        constant     int64_t & nb01,
-        constant     int64_t & nb02,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
         constant     int64_t & ne12,
         constant     int64_t & ne13,
-        constant     int64_t & nb10,
-        constant     int64_t & nb11,
-        constant     int64_t & nb12,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -3993,18 +4173,28 @@ kernel void kernel_mul_mm_id(
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const uchar * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
+    device const uchar * src0s[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
 
-    const int64_t bid = tgpig.z/(ne12*ne13);
+    // expert id
+    const int32_t id = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
-    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    // row indices of src1 for expert id
+    int64_t _ne1 = 0;
+    short src1ids[512];
 
-    kernel_mul_mm_impl<block_q, nl, dequantize_func>(
-        src0[id],
-        src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+    for (int64_t i1 = 0; i1 < ne1; i1++) {
+        if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
+            src1ids[_ne1++] = i1;
+        }
+    }
+
+    kernel_mul_mm_id_impl<block_q, nl, dequantize_func>(
+        src0s[id],
+        src1,
+        src1ids,
+        dst,
         ne00,
         ne02,
         nb01,
@@ -4014,7 +4204,7 @@ kernel void kernel_mul_mm_id(
         nb11,
         nb12,
         ne0,
-        ne1,
+        _ne1,
         r2,
         r3,
         shared_memory,
@@ -4070,12 +4260,12 @@ typedef void (mat_mm_t)(
         device        float * dst,
         constant    int64_t & ne00,
         constant    int64_t & ne02,
-        constant    int64_t & nb01,
-        constant    int64_t & nb02,
+        constant   uint64_t & nb01,
+        constant   uint64_t & nb02,
         constant    int64_t & ne12,
-        constant    int64_t & nb10,
-        constant    int64_t & nb11,
-        constant    int64_t & nb12,
+        constant   uint64_t & nb10,
+        constant   uint64_t & nb11,
+        constant   uint64_t & nb12,
         constant    int64_t & ne0,
         constant    int64_t & ne1,
         constant       uint & r2,
@@ -4103,20 +4293,20 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<b
 typedef void (mat_mm_id_t)(
         device const   uchar * ids,
         device const   uchar * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
-        constant     int64_t & nb01,
-        constant     int64_t & nb02,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
         constant     int64_t & ne12,
         constant     int64_t & ne13,
-        constant     int64_t & nb10,
-        constant     int64_t & nb11,
-        constant     int64_t & nb12,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4152,8 +4342,8 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu
 kernel void kernel_mul_mv_id_f32_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4169,7 +4359,7 @@ kernel void kernel_mul_mv_id_f32_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4196,7 +4386,7 @@ kernel void kernel_mul_mv_id_f32_f32(
     kernel_mul_mv_f32_f32_impl(
         src0[id],
         src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+        dst  + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4221,8 +4411,8 @@ kernel void kernel_mul_mv_id_f32_f32(
 kernel void kernel_mul_mv_id_f16_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4238,7 +4428,7 @@ kernel void kernel_mul_mv_id_f16_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4265,7 +4455,7 @@ kernel void kernel_mul_mv_id_f16_f32(
     kernel_mul_mv_f16_f32_impl(
         src0[id],
         src1 + bid*nb11,
-        (device float *) (dst + bid*nb1),
+        dst  + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4290,8 +4480,8 @@ kernel void kernel_mul_mv_id_f16_f32(
 kernel void kernel_mul_mv_id_q8_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4307,7 +4497,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4334,7 +4524,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
     kernel_mul_mv_q8_0_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4353,8 +4543,8 @@ kernel void kernel_mul_mv_id_q8_0_f32(
 kernel void kernel_mul_mv_id_q4_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4370,7 +4560,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4397,7 +4587,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
     mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4416,8 +4606,8 @@ kernel void kernel_mul_mv_id_q4_0_f32(
 kernel void kernel_mul_mv_id_q4_1_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4433,7 +4623,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4460,7 +4650,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
     mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4479,8 +4669,8 @@ kernel void kernel_mul_mv_id_q4_1_f32(
 kernel void kernel_mul_mv_id_q5_0_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4496,7 +4686,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4523,7 +4713,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
     mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4542,8 +4732,8 @@ kernel void kernel_mul_mv_id_q5_0_f32(
 kernel void kernel_mul_mv_id_q5_1_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4559,7 +4749,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4586,7 +4776,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
     mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4605,8 +4795,8 @@ kernel void kernel_mul_mv_id_q5_1_f32(
 kernel void kernel_mul_mv_id_q2_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4622,7 +4812,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4649,7 +4839,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
     kernel_mul_mv_q2_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4668,8 +4858,8 @@ kernel void kernel_mul_mv_id_q2_K_f32(
 kernel void kernel_mul_mv_id_q3_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4685,7 +4875,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4712,7 +4902,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
     kernel_mul_mv_q3_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4731,8 +4921,8 @@ kernel void kernel_mul_mv_id_q3_K_f32(
 kernel void kernel_mul_mv_id_q4_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4748,7 +4938,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4775,7 +4965,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
     kernel_mul_mv_q4_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4794,8 +4984,8 @@ kernel void kernel_mul_mv_id_q4_K_f32(
 kernel void kernel_mul_mv_id_q5_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4811,7 +5001,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4838,7 +5028,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
     kernel_mul_mv_q5_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
@@ -4857,8 +5047,8 @@ kernel void kernel_mul_mv_id_q5_K_f32(
 kernel void kernel_mul_mv_id_q6_K_f32(
         device const    char * ids,
         device const    char * src1,
-        device         uchar * dst,
-        constant     int64_t & nbi1,
+        device         float * dst,
+        constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
         constant     int64_t & ne02,
@@ -4874,7 +5064,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
         constant    uint64_t & nb12,
         constant     int64_t & ne0,
         constant     int64_t & ne1,
-        constant     int64_t & nb1,
+        constant    uint64_t & nb1,
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
@@ -4901,7 +5091,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
     kernel_mul_mv_q6_K_f32_impl(
         src0[id],
         (device const float *) (src1 + bid*nb11),
-        (device       float *) ( dst + bid*nb1),
+        dst + bid*ne0,
         ne00,
         ne01,
         ne02,
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index ae62cc575..f0a1c51f8 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -46,6 +46,8 @@ class Keys:
         HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
         MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
         CLAMP_KQV         = "{arch}.attention.clamp_kqv"
+        KEY_LENGTH        = "{arch}.attention.key_length"
+        VALUE_LENGTH      = "{arch}.attention.value_length"
         LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
         LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
 
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 73e021607..d93aaa877 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -333,6 +333,12 @@ class GGUFWriter:
     def add_head_count_kv(self, count: int) -> None:
         self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
 
+    def add_key_length(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
+
+    def add_value_length(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
+
     def add_max_alibi_bias(self, bias: float) -> None:
         self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
 
diff --git a/koboldcpp.py b/koboldcpp.py
index 75c48c2c4..8655716ce 100755
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -425,7 +425,7 @@ maxhordelen = 256
 modelbusy = threading.Lock()
 requestsinqueue = 0
 defaultport = 5001
-KcppVersion = "1.54"
+KcppVersion = "1.55"
 showdebug = True
 showsamplerwarning = True
 showmaxctxwarning = True
diff --git a/llama.cpp b/llama.cpp
index 0b1b09210..a57437dd5 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -246,6 +246,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
     LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
     LLM_KV_ATTENTION_CLAMP_KQV,
+    LLM_KV_ATTENTION_KEY_LENGTH,
+    LLM_KV_ATTENTION_VALUE_LENGTH,
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
 
@@ -298,6 +300,8 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
     { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
     { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
+    { LLM_KV_ATTENTION_KEY_LENGTH,          "%s.attention.key_length"             },
+    { LLM_KV_ATTENTION_VALUE_LENGTH,        "%s.attention.value_length"           },
     { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
 
@@ -1289,6 +1293,8 @@ struct llama_hparams {
     uint32_t n_head_kv;
     uint32_t n_layer;
     uint32_t n_rot;
+    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
+    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
     uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
@@ -1315,6 +1321,8 @@ struct llama_hparams {
         if (this->n_head_kv     != other.n_head_kv)     return true;
         if (this->n_layer       != other.n_layer)       return true;
         if (this->n_rot         != other.n_rot)         return true;
+        if (this->n_embd_head_k != other.n_embd_head_k) return true;
+        if (this->n_embd_head_v != other.n_embd_head_v) return true;
         if (this->n_ff          != other.n_ff)          return true;
         if (this->n_expert      != other.n_expert)      return true;
         if (this->n_expert_used != other.n_expert_used) return true;
@@ -1336,12 +1344,12 @@ struct llama_hparams {
         return n_head/n_head_kv;
     }
 
-    uint32_t n_embd_head() const {
-        return n_embd/n_head;
+    uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
+        return n_embd_head_k * n_head_kv;
     }
 
-    uint32_t n_embd_gqa() const {
-        return n_embd/n_gqa();
+    uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
+        return n_embd_head_v * n_head_kv;
     }
 };
 
@@ -1655,8 +1663,9 @@ static bool llama_kv_cache_init(
                           uint32_t   n_ctx,
                                int   n_gpu_layers,
                               bool   offload) {
-    const uint32_t n_embd  = hparams.n_embd_gqa();
-    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+    const uint32_t n_layer      = hparams.n_layer;
 
     cache.has_shift = false;
 
@@ -1687,8 +1696,8 @@ static bool llama_kv_cache_init(
     const int i_gpu_start = (int) n_layer - n_gpu_layers;
 
     for (int i = 0; i < (int) n_layer; i++) {
-        ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd*n_ctx);
-        ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd*n_ctx);
+        ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx);
+        ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx);
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
         cache.k_l.push_back(k);
@@ -2683,6 +2692,12 @@ static void llm_load_hparams(
         // gpt-j n_rot = rotary_dim
     }
 
+    hparams.n_embd_head_k = hparams.n_embd / hparams.n_head;
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+    hparams.n_embd_head_v = hparams.n_embd / hparams.n_head;
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+
     // arch-specific KVs
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
@@ -3112,8 +3127,12 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
     LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
     LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
-    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
+    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
+    LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
+    LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
     LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
+    LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %u\n",     __func__, hparams.n_embd_k_gqa());
+    LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %u\n",     __func__, hparams.n_embd_v_gqa());
     LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
     LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
     LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
@@ -3202,10 +3221,11 @@ static bool llm_load_tensors(
 
     // create tensors for the weights
     {
-        const int64_t n_embd     = hparams.n_embd;
-        const int64_t n_embd_gqa = hparams.n_embd_gqa();
-        const int64_t n_layer    = hparams.n_layer;
-        const int64_t n_vocab    = hparams.n_vocab;
+        const int64_t n_embd       = hparams.n_embd;
+        const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const int64_t n_layer      = hparams.n_layer;
+        const int64_t n_vocab      = hparams.n_vocab;
 
         const auto tn = LLM_TN(model.arch);
         switch (model.arch) {
@@ -3231,7 +3251,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3299,7 +3322,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3347,7 +3373,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3397,7 +3426,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3449,7 +3481,11 @@ static bool llm_load_tensors(
                         model.output         = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
+
                     const int i_gpu_start = n_layer - n_gpu_layers;
                     model.layers.resize(n_layer);
                     for (uint32_t i = 0; i < n_layer; ++i) {
@@ -3498,7 +3534,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3549,7 +3588,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3596,7 +3638,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3694,7 +3739,10 @@ static bool llm_load_tensors(
                         model.output_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab},         backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3743,7 +3791,10 @@ static bool llm_load_tensors(
                         model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -3790,7 +3841,10 @@ static bool llm_load_tensors(
                         model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff = hparams.n_ff;
+                    const uint32_t n_ff      = hparams.n_ff;
+                    const int64_t n_embd_gqa = n_embd_v_gqa;
+                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
+                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
 
                     const int i_gpu_start = n_layer - n_gpu_layers;
 
@@ -4029,8 +4083,8 @@ static struct ggml_tensor * llm_build_inp_embd(
     return inpL;
 }
 
-// Persimmon: n_rot = n_embd_head/2
-// Other:     n_rot = n_embd_head
+// Persimmon: n_rot = n_embd_head_k/2
+// Other:     n_rot = n_embd_head_k
 static void llm_build_k_shift(
       struct ggml_context * ctx,
       const llama_hparams & hparams,
@@ -4043,17 +4097,17 @@ static void llm_build_k_shift(
                   float     freq_base,
                   float     freq_scale,
        const llm_build_cb & cb) {
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
-    const float   ext_factor  = cparams.yarn_ext_factor;
-    const float   attn_factor = cparams.yarn_attn_factor;
-    const float   beta_fast   = cparams.yarn_beta_fast;
-    const float   beta_slow   = cparams.yarn_beta_slow;
+    const int64_t n_layer       = hparams.n_layer;
+    const int64_t n_head_kv     = hparams.n_head_kv;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+    const int32_t n_orig_ctx    = cparams.n_yarn_orig_ctx;
+    const float   ext_factor    = cparams.yarn_ext_factor;
+    const float   attn_factor   = cparams.yarn_attn_factor;
+    const float   beta_fast     = cparams.yarn_beta_fast;
+    const float   beta_slow     = cparams.yarn_beta_slow;
 
-    GGML_ASSERT(n_embd_head % n_rot == 0);
+    GGML_ASSERT(n_embd_head_k % n_rot == 0);
 
     struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
     cb(K_shift, "K_shift", -1);
@@ -4071,9 +4125,9 @@ static void llm_build_k_shift(
             // we rotate only the first n_rot dimensions
             ggml_rope_custom_inplace(ctx,
                     ggml_view_3d(ctx, kv.k_l[il],
-                        n_embd_head, n_head_kv, n_ctx,
-                        ggml_row_size(kv.k_l[il]->type, n_embd_head),
-                        ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
+                        n_embd_head_k, n_head_kv, n_ctx,
+                        ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
+                        ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
                         0),
                     K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow);
@@ -4094,18 +4148,19 @@ static void llm_build_kv_store(
                     int32_t   kv_head,
          const llm_build_cb & cb,
                     int64_t   il) {
-    const int64_t n_embd_gqa = hparams.n_embd_gqa();
+    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
     // compute the transposed [n_tokens, n_embd] V matrix
-    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens));
+    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
     //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
     cb(v_cur_t, "v_cur_t", il);
 
-    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_gqa,
-            (ggml_row_size(kv.k_l[il]->type, n_embd_gqa))*kv_head);
+    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
+            (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
     cb(k_cache_view, "k_cache_view", il);
 
-    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_gqa,
+    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
             (  n_ctx)*ggml_element_size(kv.v_l[il]),
             (kv_head)*ggml_element_size(kv.v_l[il]));
     cb(v_cache_view, "v_cache_view", il);
@@ -4255,20 +4310,20 @@ static struct ggml_tensor * llm_build_kqv(
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+    const int64_t n_head        = hparams.n_head;
+    const int64_t n_head_kv     = hparams.n_head_kv;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+    const int64_t n_embd_head_v = hparams.n_embd_head_v;
 
     struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
     cb(q, "q", il);
 
     struct ggml_tensor * k =
         ggml_view_3d(ctx, kv.k_l[il],
-                n_embd_head, n_kv, n_head_kv,
-                ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
-                ggml_row_size(kv.k_l[il]->type, n_embd_head),
+                n_embd_head_k, n_kv, n_head_kv,
+                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
+                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
                 0);
     cb(k, "k", il);
 
@@ -4307,9 +4362,9 @@ static struct ggml_tensor * llm_build_kqv(
     // split cached v into n_head heads
     struct ggml_tensor * v =
         ggml_view_3d(ctx, kv.v_l[il],
-                n_kv, n_embd_head, n_head_kv,
+                n_kv, n_embd_head_v, n_head_kv,
                 ggml_element_size(kv.v_l[il])*n_ctx,
-                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head,
+                ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
                 0);
     cb(v, "v", il);
 
@@ -4319,7 +4374,7 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
     cb(kqv_merged, "kqv_merged", il);
 
-    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
+    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
     cb(cur, "kqv_merged_cont", il);
 
     cur = ggml_mul_mat(ctx, wo, cur);
@@ -4346,8 +4401,10 @@ struct llm_build_context {
     const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
     const int64_t n_head;
     const int64_t n_head_kv;
-    const int64_t n_embd_head;
-    const int64_t n_embd_gqa;
+    const int64_t n_embd_head_k;
+    const int64_t n_embd_k_gqa;
+    const int64_t n_embd_head_v;
+    const int64_t n_embd_v_gqa;
     const int64_t n_expert;
     const int64_t n_expert_used;
 
@@ -4389,8 +4446,10 @@ struct llm_build_context {
         n_ctx            (cparams.n_ctx),
         n_head           (hparams.n_head),
         n_head_kv        (hparams.n_head_kv),
-        n_embd_head      (hparams.n_embd_head()),
-        n_embd_gqa       (hparams.n_embd_gqa()),
+        n_embd_head_k    (hparams.n_embd_head_k),
+        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
+        n_embd_head_v    (hparams.n_embd_head_v),
+        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
         n_expert         (hparams.n_expert),
         n_expert_used    (hparams.n_expert_used),
         freq_base        (cparams.rope_freq_base),
@@ -4433,6 +4492,8 @@ struct llm_build_context {
     struct ggml_cgraph * build_llama() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
         GGML_ASSERT(n_embd_head == hparams.n_rot);
 
         struct ggml_tensor * cur;
@@ -4617,6 +4678,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_baichuan() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -4734,6 +4798,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_falcon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -4853,6 +4922,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_starcoder() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
@@ -4949,7 +5023,12 @@ struct llm_build_context {
     struct ggml_cgraph * build_persimmon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
-        const int64_t n_rot = n_embd_head / 2;
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
+        const int64_t n_rot = n_embd_head_k / 2;
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
@@ -5158,6 +5237,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_refact() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5246,6 +5330,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_bloom() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5337,6 +5426,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_mpt() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5432,6 +5526,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_stablelm() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5542,6 +5639,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_qwen() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5653,6 +5753,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_phi2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * attn_norm_output;
         struct ggml_tensor * ffn_output;
@@ -5765,6 +5870,9 @@ struct llm_build_context {
     struct ggml_cgraph * build_plamo() {
         struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
@@ -5869,6 +5977,11 @@ struct llm_build_context {
     struct ggml_cgraph * build_gpt2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_gqa  == n_embd);
+
         struct ggml_tensor * cur;
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
@@ -8176,7 +8289,7 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c
     }
 }
 
-void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
     const int64_t t_start_sample_us = ggml_time_us();
 
     k = std::max(k, (int) min_keep);
@@ -8539,7 +8652,7 @@ void llama_sample_classifier_free_guidance(
     }
 }
 
-llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
     GGML_ASSERT(ctx);
 
     auto N = float(llama_n_vocab(llama_get_model(ctx)));
@@ -9750,7 +9863,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
     return result;
 }
 
-int llama_max_devices(void) {
+int32_t llama_max_devices(void) {
     return LLAMA_MAX_DEVICES;
 }
 
@@ -9892,8 +10005,8 @@ struct llama_context * llama_new_context_with_model(
     const ggml_type type_k = params.type_k;
     const ggml_type type_v = params.type_v;
 
-    GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_k) == 0);
-    GGML_ASSERT(hparams.n_embd_head() % ggml_blck_size(type_v) == 0);
+    GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
+    GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
 
     // reserve memory for context buffers
     if (!hparams.vocab_only) {
@@ -10061,15 +10174,15 @@ enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
     return model->vocab.type;
 }
 
-int llama_n_vocab(const struct llama_model * model) {
+int32_t llama_n_vocab(const struct llama_model * model) {
     return model->vocab.id_to_token.size();
 }
 
-int llama_n_ctx_train(const struct llama_model * model) {
+int32_t llama_n_ctx_train(const struct llama_model * model) {
     return model->hparams.n_ctx_train;
 }
 
-int llama_n_embd(const struct llama_model * model) {
+int32_t llama_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
@@ -10077,7 +10190,7 @@ float llama_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
 
-int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
+int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
     const auto & it = model->gguf_kv.find(key);
     if (it == model->gguf_kv.end()) {
         if (buf_size > 0) {
@@ -10088,11 +10201,11 @@ int llama_model_meta_val_str(const struct llama_model * model, const char * key,
     return snprintf(buf, buf_size, "%s", it->second.c_str());
 }
 
-int llama_model_meta_count(const struct llama_model * model) {
+int32_t llama_model_meta_count(const struct llama_model * model) {
     return (int)model->gguf_kv.size();
 }
 
-int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
     if (i < 0 || i >= (int)model->gguf_kv.size()) {
         if (buf_size > 0) {
             buf[0] = '\0';
@@ -10104,7 +10217,7 @@ int llama_model_meta_key_by_index(const struct llama_model * model, int i, char
     return snprintf(buf, buf_size, "%s", it->first.c_str());
 }
 
-int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
     if (i < 0 || i >= (int)model->gguf_kv.size()) {
         if (buf_size > 0) {
             buf[0] = '\0';
@@ -10116,9 +10229,10 @@ int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, c
     return snprintf(buf, buf_size, "%s", it->second.c_str());
 }
 
-int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
-    return snprintf(buf, buf_size, "%s %s %s",
+int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
+    return snprintf(buf, buf_size, "%s %s%s %s",
             llama_model_arch_name(model->arch).c_str(),
+            model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "",
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }
@@ -10143,7 +10257,7 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
     return ggml_get_tensor(model->ctx, name);
 }
 
-int llama_model_quantize(
+uint32_t llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
         const llama_model_quantize_params * params) {
@@ -10156,7 +10270,7 @@ int llama_model_quantize(
     }
 }
 
-int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
     } catch (const std::exception & err) {
@@ -10165,7 +10279,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
     }
 }
 
-int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
+int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
     } catch (const std::exception & err) {
@@ -10263,7 +10377,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
     }
 }
 
-int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
+int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     int result = 0;
 
     for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
@@ -10273,7 +10387,7 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     return result;
 }
 
-int llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
+int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
     return ctx->kv_self.used;
 }
 
@@ -10437,9 +10551,10 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const auto   n_layer = hparams.n_layer;
-        const auto   n_embd  = hparams.n_embd_gqa();
-        const auto   n_ctx   = cparams.n_ctx;
+        const auto   n_layer      = hparams.n_layer;
+        const auto   n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const auto   n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const auto   n_ctx        = cparams.n_ctx;
 
         const size_t   kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf);
         const uint32_t kv_head     = kv_self.head;
@@ -10461,15 +10576,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
             std::vector<struct ggml_tensor *> vout2d(n_layer);
 
             for (int il = 0; il < (int) n_layer; ++il) {
-                kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head);
-                vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd);
+                kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
+                vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
 
                 ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                        n_embd, kv_head,
-                        elt_size*n_embd, 0);
+                        n_embd_k_gqa, kv_head,
+                        elt_size*n_embd_k_gqa, 0);
 
                 ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                        kv_head, n_embd,
+                        kv_head, n_embd_v_gqa,
                         elt_size*n_ctx, 0);
 
                 ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il]));
@@ -10576,9 +10691,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const int    n_layer = hparams.n_layer;
-        const int    n_embd  = hparams.n_embd_gqa();
-        const int    n_ctx   = cparams.n_ctx;
+        const int    n_layer      = hparams.n_layer;
+        const int    n_embd_k_gqa = hparams.n_embd_k_gqa();
+        const int    n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const int    n_ctx        = cparams.n_ctx;
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -10602,15 +10718,15 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
             std::vector<struct ggml_tensor *> vin2d(n_layer);
 
             for (int il = 0; il < n_layer; ++il) {
-                kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd, kv_head);
-                vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd);
+                kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
+                vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
 
                 ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                    n_embd, kv_head,
-                    elt_size*n_embd, 0);
+                    n_embd_k_gqa, kv_head,
+                    elt_size*n_embd_k_gqa, 0);
 
                 ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                    kv_head, n_embd,
+                    kv_head, n_embd_v_gqa,
                     elt_size*n_ctx, 0);
 
                 ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d));
@@ -10763,7 +10879,7 @@ int llama_eval(
         struct llama_context * ctx,
                  llama_token * tokens,
                      int32_t   n_tokens,
-                         int   n_past) {
+                     int32_t   n_past) {
     llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
@@ -10778,7 +10894,7 @@ int llama_eval_embd(
             struct llama_context * ctx,
                            float * embd,
                          int32_t   n_tokens,
-                             int   n_past) {
+                         int32_t   n_past) {
     llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
@@ -10849,7 +10965,7 @@ void llama_batch_free(struct llama_batch batch) {
     if (batch.logits)   free(batch.logits);
 }
 
-int llama_decode(
+int32_t llama_decode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
     const int ret = llama_decode_internal(*ctx, batch);
@@ -10897,11 +11013,11 @@ llama_token llama_token_nl(const struct llama_model * model) {
     return model->vocab.linefeed_id;
 }
 
-int llama_add_bos_token(const struct llama_model * model) {
+int32_t llama_add_bos_token(const struct llama_model * model) {
     return model->vocab.special_add_bos;
 }
 
-int llama_add_eos_token(const struct llama_model * model) {
+int32_t llama_add_eos_token(const struct llama_model * model) {
     return model->vocab.special_add_eos;
 }
 
@@ -10921,12 +11037,12 @@ llama_token llama_token_eot(const struct llama_model * model) {
     return model->vocab.special_eot_id;
 }
 
-int llama_tokenize(
+int32_t llama_tokenize(
     const struct llama_model * model,
                   const char * text,
-                         int   text_len,
+                     int32_t   text_len,
                  llama_token * tokens,
-                         int   n_max_tokens,
+                     int32_t   n_max_tokens,
                         bool   add_bos,
                         bool   special) {
     auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
@@ -10954,7 +11070,7 @@ static std::string llama_decode_text(const std::string & text) {
 }
 
 // does not write null-terminator to buf
-int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
+int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
     if(OldBPETokenizerMode)
     {
         return llama_token_to_piece_old(model, token, buf, length);
diff --git a/llama.h b/llama.h
index 19518b8b4..0d6a25ba0 100644
--- a/llama.h
+++ b/llama.h
@@ -227,7 +227,7 @@ extern "C" {
 
     // model quantization parameters
     typedef struct llama_model_quantize_params {
-        int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        int32_t nthread;             // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
         enum llama_ftype ftype;      // quantize to this llama_ftype
         bool allow_requantize;       // allow quantizing non-f32/f16 tensors
         bool quantize_output_tensor; // quantize output.weight
@@ -311,21 +311,20 @@ extern "C" {
 
     LLAMA_API int64_t llama_time_us(void);
 
-    LLAMA_API int  llama_max_devices    (void);
+    LLAMA_API int32_t  llama_max_devices(void);
     LLAMA_API bool llama_mmap_supported (void);
     LLAMA_API bool llama_mlock_supported(void);
 
     LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 
-    // TODO: become more consistent with returned int types across the API
     LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
 
     LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
 
-    LLAMA_API int llama_n_vocab    (const struct llama_model * model);
-    LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
-    LLAMA_API int llama_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
+    LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@@ -336,19 +335,19 @@ extern "C" {
     // - GGUF array values are not supported by these functions
 
     // Get metadata value as a string by key name
-    LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
 
     // Get the number of metadata key/value pairs
-    LLAMA_API int llama_model_meta_count(const struct llama_model * model);
+    LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
 
     // Get metadata key name by index
-    LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
+    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 int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
+    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);
 
     // Get a string describing the model type
-    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+    LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
 
     // Returns the total size of all the tensors in the model in bytes
     LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
@@ -360,7 +359,7 @@ extern "C" {
     LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
 
     // Returns 0 on success
-    LLAMA_API int llama_model_quantize(
+    LLAMA_API uint32_t llama_model_quantize(
             const char * fname_inp,
             const char * fname_out,
             const llama_model_quantize_params * params);
@@ -371,20 +370,20 @@ extern "C" {
     // The model needs to be reloaded before applying a new adapter, otherwise the adapter
     // will be applied on top of the previous one
     // Returns 0 on success
-    LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
+    LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
             struct llama_context * ctx,
                       const char * path_lora,
                            float   scale,
                       const char * path_base_model,
-                             int   n_threads),
+                         int32_t   n_threads),
             "use llama_model_apply_lora_from_file instead");
 
-    LLAMA_API int llama_model_apply_lora_from_file(
+    LLAMA_API int32_t llama_model_apply_lora_from_file(
             const struct llama_model * model,
                       const char * path_lora,
                            float   scale,
                       const char * path_base_model,
-                             int   n_threads);
+                         int32_t   n_threads);
 
     //
     // KV cache
@@ -440,10 +439,10 @@ extern "C" {
 
     // Returns the number of tokens in the KV cache (slow, use only for debug)
     // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-    LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
 
     // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-    LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
 
     // Clear the KV cache
     LLAMA_API void llama_kv_cache_clear(
@@ -536,7 +535,7 @@ extern "C" {
             struct llama_context * ctx,
                      llama_token * tokens,
                          int32_t   n_tokens,
-                             int   n_past),
+                         int32_t   n_past),
             "use llama_decode() instead");
 
     // Same as llama_eval, but use float matrix input directly.
@@ -545,7 +544,7 @@ extern "C" {
             struct llama_context * ctx,
                            float * embd,
                          int32_t   n_tokens,
-                             int   n_past),
+                         int32_t   n_past),
             "use llama_decode() instead");
 
     // Return batch for single sequence of tokens starting at pos_0
@@ -577,7 +576,7 @@ extern "C" {
     //   0 - success
     //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
     // < 0 - error
-    LLAMA_API int llama_decode(
+    LLAMA_API int32_t llama_decode(
             struct llama_context * ctx,
               struct llama_batch   batch);
 
@@ -617,10 +616,10 @@ extern "C" {
     LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int         llama_add_bos_token(const struct llama_model * model);
+    LLAMA_API int32_t         llama_add_bos_token(const struct llama_model * model);
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int         llama_add_eos_token(const struct llama_model * model);
+    LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
 
     // codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
@@ -638,12 +637,12 @@ extern "C" {
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
     /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
     ///                Does not insert a leading space.
-    LLAMA_API int llama_tokenize(
+    LLAMA_API int32_t llama_tokenize(
         const struct llama_model * model,
                       const char * text,
-                             int   text_len,
+                         int32_t   text_len,
                      llama_token * tokens,
-                             int   n_max_tokens,
+                         int32_t   n_max_tokens,
                             bool   add_bos,
                             bool   special);
 
@@ -651,11 +650,11 @@ extern "C" {
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
     // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
-    LLAMA_API int llama_token_to_piece(
+    LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
-                                  int    length);
+                               int32_t   length);
 
     //
     // Grammar
@@ -707,7 +706,7 @@ extern "C" {
     LLAMA_API void llama_sample_top_k(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
-                             int   k,
+                         int32_t   k,
                           size_t   min_keep);
 
     /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
@@ -766,7 +765,7 @@ extern "C" {
           llama_token_data_array * candidates,
                            float   tau,
                            float   eta,
-                             int   m,
+                         int32_t   m,
                            float * mu);
 
     /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
@@ -839,8 +838,8 @@ extern "C" {
         llama_beam_search_callback_fn_t   callback,
                                    void * callback_data,
                                  size_t   n_beams,
-                                    int   n_past,
-                                    int   n_predict);
+                                int32_t   n_past,
+                                int32_t   n_predict);
 
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index b115299c0..eff063b2d 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -15,19 +15,18 @@
 #include <thread>
 #include <vector>
 
-
 static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
     size_t size = ggml_nelements(tensor);
     std::vector<float> data(size);
 
 #if 0
-    std::default_random_engine generator(rd());
+    static std::default_random_engine generator(1234);
     std::uniform_real_distribution<float> distribution(min, max);
 
     for (size_t i = 0; i < size; i++) {
         data[i] = distribution(generator);
     }
-#endif
+#else
     auto init_thread = [&](size_t start, size_t end) {
         std::random_device rd;
         std::default_random_engine generator(rd());
@@ -49,6 +48,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
     for (auto & t : threads) {
         t.join();
     }
+#endif
 
     if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
         ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
@@ -437,7 +437,7 @@ struct test_case {
             double err = nmse(f1.data(), f2.data(), f1.size());
             if (err > ud->max_err) {
                 printf("[%s] NMSE = %f ", ggml_op_desc(t1), err);
-                //for (int i = 0; i < f1.size(); i++) {
+                //for (int i = 0; i < (int) f1.size(); i++) {
                 //    printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
                 //}
                 //printf("\n");