diff --git a/Makefile b/Makefile
index 6170eedbd..0c92ed164 100644
--- a/Makefile
+++ b/Makefile
@@ -160,7 +160,8 @@ endif
 ifneq ($(filter ppc64%,$(UNAME_M)),)
 	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
 	ifneq (,$(findstring POWER9,$(POWER9_M)))
-		CFLAGS += -mpower9-vector
+		CFLAGS += -mcpu=power9
+		CXXFLAGS += -mcpu=power9
 	endif
 	# Require c++23's std::byteswap for big-endian support.
 	ifeq ($(UNAME_M),ppc64)
diff --git a/alpaca.sh b/alpaca.sh
index 2f36d6f54..d8a9f456a 100755
--- a/alpaca.sh
+++ b/alpaca.sh
@@ -3,4 +3,4 @@
 # Temporary script - will be removed in the future
 #
 
-./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7
+./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins -b 256 --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7
diff --git a/chat.sh b/chat.sh
index 24a0f10ad..5531315b3 100755
--- a/chat.sh
+++ b/chat.sh
@@ -3,4 +3,4 @@
 # Temporary script - will be removed in the future
 #
 
-./main -m ./models/7B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
+./main -m ./models/7B/ggml-model-q4_0.bin -b 128 -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
diff --git a/examples/chatLLaMa b/examples/chatLLaMa
index 97ababbc5..4265d7b66 100755
--- a/examples/chatLLaMa
+++ b/examples/chatLLaMa
@@ -13,7 +13,7 @@ N_PREDICTS="${N_PREDICTS:-2048}"
 
 # Note: you can also override the generation options by specifying them on the command line:
 # For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
-GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --repeat_penalty 1.17647}"
+GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
 
 # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
 ./main $GEN_OPTIONS \
diff --git a/expose.cpp b/expose.cpp
index f473d8f5f..7cba6a8f1 100644
--- a/expose.cpp
+++ b/expose.cpp
@@ -192,7 +192,7 @@ extern "C" {
 		std::string concat_output = "";  
     	
 		bool startedsampling = false;
-        printf("\nProcessing Prompt: ");
+        printf("\nProcessing Prompt (%d tokens): ",embd_inp.size());
 
 		while (remaining_tokens > 0) 
 		{
@@ -224,7 +224,7 @@ extern "C" {
             	if(!startedsampling)
                 {
                     startedsampling = true;
-                    printf("\nGenerating: ");
+                    printf("\nGenerating (%d tokens): ",params.n_predict);
                 }
 
 	            {
diff --git a/ggml.c b/ggml.c
index 0e4b1466c..db68ed144 100644
--- a/ggml.c
+++ b/ggml.c
@@ -1,5 +1,5 @@
-// Defines CLOCK_MONOTONIC on Linux
-#define _POSIX_C_SOURCE 199309L
+// Defines CLOCK_MONOTONIC and asprintf on Linux
+#define _GNU_SOURCE
 
 #include "ggml.h"
 
@@ -10,6 +10,7 @@
 #endif
 
 #include <assert.h>
+#include <errno.h>
 #include <time.h>
 #include <math.h>
 #include <stdlib.h>
@@ -31,7 +32,6 @@
 #else
 // ref: https://github.com/ggerganov/whisper.cpp/issues/168
 #include <windows.h>
-#include <errno.h>
 #endif
 
 typedef volatile LONG atomic_int;
@@ -83,6 +83,17 @@ typedef void* thread_ret_t;
 #define static_assert(cond, msg) _Static_assert(cond, msg)
 #endif
 
+#define GGML_MLOCK_SUPPORT 0
+
+#ifdef __has_include
+    #if __has_include(<sys/mman.h>)
+        #undef GGML_MLOCK_SUPPORT
+        #define GGML_MLOCK_SUPPORT 1
+        #include <sys/mman.h>
+    #endif
+#endif
+
+
 /*#define GGML_PERF*/
 #define GGML_DEBUG 0
 #define GGML_GELU_FP16
@@ -164,6 +175,39 @@ typedef double ggml_float;
 #define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
 #define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
 
+#elif defined(__POWER9_VECTOR__)
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+/* the inline asm below is about 12% faster than the lookup method */
+#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    register float f;
+    register double d;
+    __asm__(
+        "mtfprd %0,%2\n"
+        "xscvhpdp %0,%0\n"
+        "frsp %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=f"(f):
+        /* in */   "r"(h));
+    return f;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    register double d;
+    register ggml_fp16_t r;
+    __asm__( /* xscvdphp can work on double or single precision */
+        "xscvdphp %0,%2\n"
+        "mffprd %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=r"(r):
+        /* in */   "f"(f));
+    return r;
+}
+
 #else
 
 // FP16 <-> FP32
@@ -261,6 +305,7 @@ static float table_f32_f16[1 << 16];
 
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
+// This is also true for POWER9.
 #if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
 
 inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
@@ -451,7 +496,7 @@ static void quantize_row_q4_0_reference(const float * restrict x, void * restric
 void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
     assert(k % QK == 0);
 
-#if __ARM_NEON || defined(__AVX2__) || defined(__wasm_simd128__)
+#if __ARM_NEON || defined(__AVX2__) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__)
     const int nb = k / QK;
     const size_t bs = sizeof(float) + QK/2;
 
@@ -461,7 +506,52 @@ void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
     uint8_t pp[QK/2];
 #endif
 
-#if __ARM_NEON
+#if defined(__POWER9_VECTOR__)
+#if QK == 32
+    const vector float v85 = vec_splats(8.5f);
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        vector float srcv [8];
+        vector float asrcv[8];
+        vector float amaxv[8];
+
+        for (int l = 0; l < 8; l++) srcv[l]  = *(vector float *)(x + i*32 + 4*l);
+        for (int l = 0; l < 8; l++) asrcv[l] = vec_abs(srcv[l]);
+
+        for (int l = 0; l < 4; l++) amaxv[2*l] = vec_max(asrcv[2*l], asrcv[2*l+1]);
+        //for (int l = 0; l < 2; l++) amaxv[4*l] = vec_max(amaxv[4*l], amaxv[4*l+2]);
+        amaxv[0] = vec_max(amaxv[0], amaxv[2]);
+        amaxv[4] = vec_max(amaxv[4], amaxv[6]);
+        //for (int l = 0; l < 1; l++) amaxv[8*l] = vec_max(amaxv[8*l], amaxv[8*l+4]);
+        amaxv[0] = vec_max(amaxv[0], amaxv[4]);
+
+        amax = MAX(
+                MAX(vec_extract(amaxv[0], 0), vec_extract(amaxv[0], 1)),
+                MAX(vec_extract(amaxv[0], 2), vec_extract(amaxv[0], 3)));
+
+        const float d = amax / ((1 << 3) - 1);
+        const float id = d ? 1.0/d : 0.0;
+
+        *(float *)pd = d;
+        pd += bs;
+
+        const vector float vid = vec_splats(id);
+        for (int l = 0; l < 8; l++) {
+            const vector float vf  = vec_madd(srcv[l], vid, v85);
+            const vector signed int vi = vec_signed(vf);
+
+            pb[2*l + 0] = vec_extract(vi, 0) | (vec_extract(vi, 1) << 4);
+            pb[2*l + 1] = vec_extract(vi, 2) | (vec_extract(vi, 3) << 4);
+        }
+
+        //memcpy(pb, pp, sizeof(pp));
+        pb += bs;
+    }
+#else
+#error "not implemented for QK"
+#endif
+#elif __ARM_NEON
 #if QK == 32
     for (int i = 0; i < nb; i++) {
         float amax = 0.0f; // absolute max
@@ -2344,6 +2434,7 @@ struct ggml_context {
     size_t mem_size;
     void * mem_buffer;
     bool   mem_buffer_owned;
+    bool   mem_buffer_mlocked;
 
     int n_objects;
 
@@ -2619,16 +2710,19 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
     }
 
     *ctx = (struct ggml_context) {
-        /*.mem_size         =*/ params.mem_size,
-        /*.mem_buffer       =*/ params.mem_buffer ? params.mem_buffer : malloc(params.mem_size),
-        /*.mem_buffer_owned =*/ params.mem_buffer ? false : true,
-        /*.n_objects        =*/ 0,
-        /*.objects_begin    =*/ NULL,
-        /*.objects_end      =*/ NULL,
-        /*.scratch          =*/ { 0, 0, NULL, },
-        /*.scratch_save     =*/ { 0, 0, NULL, },
+        /*.mem_size           =*/ params.mem_size,
+        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : malloc(params.mem_size),
+        /*.mem_buffer_owned   =*/ params.mem_buffer ? false : true,
+        /*.mem_buffer_mlocked =*/ false,
+        /*.n_objects          =*/ 0,
+        /*.objects_begin      =*/ NULL,
+        /*.objects_end        =*/ NULL,
+        /*.scratch            =*/ { 0, 0, NULL, },
+        /*.scratch_save       =*/ { 0, 0, NULL, },
     };
 
+    GGML_ASSERT(ctx->mem_buffer != NULL); // check for allocation failure
+
     ggml_assert_aligned(ctx->mem_buffer);
 
     GGML_PRINT_DEBUG("%s: context initialized\n", __func__);
@@ -2651,6 +2745,14 @@ void ggml_free(struct ggml_context * ctx) {
             GGML_PRINT_DEBUG("%s: context %d with %d objects has been freed. memory used = %zu\n",
                     __func__, i, ctx->n_objects, ctx->objects_end->offs + ctx->objects_end->size);
 
+#if GGML_MLOCK_SUPPORT
+            if (ctx->mem_buffer_mlocked) {
+                if (munlock(ctx->mem_buffer, ctx->mem_size)) {
+                    fprintf(stderr, "%s: failed to munlock buffer: %s\n", __func__, strerror(errno));
+                }
+            }
+#endif
+
             if (ctx->mem_buffer_owned) {
                 free(ctx->mem_buffer);
             }
@@ -2679,6 +2781,37 @@ size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch)
     return result;
 }
 
+bool ggml_mlock_supported(void) {
+    return GGML_MLOCK_SUPPORT;
+}
+
+#if GGML_MLOCK_SUPPORT
+#ifdef __APPLE__
+    #define MLOCK_SUGGESTION "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or\n" \
+                             "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l)."
+#else
+    #define MLOCK_SUGGESTION "Try increasing RLIMIT_MLOCK (ulimit -l)."
+#endif
+bool ggml_mlock(struct ggml_context * ctx, char ** err_p) {
+    if (ctx->mem_buffer_mlocked) {
+        return true;
+    }
+    if (mlock(ctx->mem_buffer, ctx->mem_size)) {
+        int ret = asprintf(err_p, "failed to mlock %zu-byte buffer: %s\n" MLOCK_SUGGESTION,
+                           ctx->mem_size, strerror(errno));
+        GGML_ASSERT(ret >= 0);
+        return false;
+    }
+    ctx->mem_buffer_mlocked = true;
+    return true;
+}
+#else // GGML_MLOCK_SUPPORT
+bool ggml_mlock(struct ggml_context * ctx, char ** err_p) {
+    *err_p = strdup("can't mlock because it's not supported on this system");
+    return false;
+}
+#endif // GGML_MLOCK_SUPPORT
+
 ////////////////////////////////////////////////////////////////////////////////
 
 struct ggml_tensor * ggml_new_tensor_impl(
@@ -5713,17 +5846,28 @@ static bool ggml_compute_forward_mul_mat_use_blas(
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    UNUSED(src0);
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
 
     const int ne10 = src1->ne[0];
 
     const int ne0 = dst->ne[0];
     const int ne1 = dst->ne[1];
 
+    // TMP: disable BLAS for now there is definitely a bug
+    return false;
+
     // TODO: find the optimal values for these
     if (ggml_is_contiguous(src0) &&
         ggml_is_contiguous(src1) && ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
-        //printf("BLAS: %d %d %d\n", ne0, ne1, ne10);
+
+        // disable BLAS for Q4_0 and Q4_1
+        // there is a bug that has to be fixed before enabling
+        if (src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1) {
+            return false;
+        }
+
+        //printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);
         return true;
     }
 
diff --git a/ggml.h b/ggml.h
index c7e6814a8..ddb97318b 100644
--- a/ggml.h
+++ b/ggml.h
@@ -343,6 +343,9 @@ size_t ggml_used_mem(const struct ggml_context * ctx);
 
 size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
 
+bool ggml_mlock_supported(void);
+bool ggml_mlock(struct ggml_context * ctx, char ** err_p);
+
 struct ggml_tensor * ggml_new_tensor(
         struct ggml_context * ctx,
         enum   ggml_type type,
diff --git a/llama.cpp b/llama.cpp
index 710f83a10..fd922e426 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -5,12 +5,25 @@
 #include <cinttypes>
 #include <fstream>
 #include <random>
+#include <map>
 #include <unordered_map>
 #include <queue>
 #include <regex>
 #include <cassert>
 #include <cstring>
 
+#define LLAMA_USE_SCRATCH
+#define LLAMA_MAX_SCRATCH_BUFFERS 16
+
+#define LLAMA_ASSERT(x) \
+    do { \
+        if (!(x)) { \
+            fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
+            abort(); \
+        } \
+    } while (0)
+
+
 // determine number of model parts based on the dimension
 static const std::unordered_map<int, int> LLAMA_N_PARTS = {
     { 4096, 1 },
@@ -19,6 +32,52 @@ static const std::unordered_map<int, int> LLAMA_N_PARTS = {
     { 8192, 8 },
 };
 
+// available llama models
+enum e_model {
+    MODEL_UNKNOWN,
+    MODEL_7B,
+    MODEL_13B,
+    MODEL_30B,
+    MODEL_65B,
+};
+
+static const size_t MB = 1024*1024;
+
+// computed for n_ctx == 2048
+// TODO: dynamically determine these sizes
+//       needs modifications in ggml
+
+static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
+    { MODEL_7B,    512ull*MB },
+    { MODEL_13B,   512ull*MB },
+    { MODEL_30B,   512ull*MB },
+    { MODEL_65B,   512ull*MB },
+};
+
+static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
+    { MODEL_7B,    512ull*MB },
+    { MODEL_13B,   512ull*MB },
+    { MODEL_30B,   512ull*MB },
+    { MODEL_65B,   512ull*MB },
+};
+
+// 2*n_embd*n_ctx*n_layer*sizeof(float16)
+static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
+    { MODEL_7B,   1026ull*MB },
+    { MODEL_13B,  1608ull*MB },
+    { MODEL_30B,  3124ull*MB },
+    { MODEL_65B,  5120ull*MB },
+};
+
+// this is mostly needed for temporary mul_mat buffers to dequantize the data
+// not actually needed if BLAS is disabled
+static const std::map<e_model, size_t> MEM_REQ_EVAL = {
+    { MODEL_7B,   768ull*MB },
+    { MODEL_13B, 1024ull*MB },
+    { MODEL_30B, 1280ull*MB },
+    { MODEL_65B, 1536ull*MB },
+};
+
 // default hparams (LLaMA 7B)
 struct llama_hparams {
     int32_t n_vocab = 32000;
@@ -50,7 +109,20 @@ struct llama_layer {
     struct ggml_tensor * w3;
 };
 
+struct llama_kv_cache {
+    struct ggml_tensor * k;
+    struct ggml_tensor * v;
+
+    struct ggml_context * ctx;
+
+    std::vector<uint8_t> buf;
+
+    int n; // number of tokens currently in the cache
+};
+
 struct llama_model {
+    e_model type = MODEL_UNKNOWN;
+
     llama_hparams hparams;
 
     struct ggml_tensor * tok_embeddings;
@@ -60,12 +132,18 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
-    // key + value memory
-    struct ggml_tensor * memory_k;
-    struct ggml_tensor * memory_v;
-
-    //
+    // context
     struct ggml_context * ctx;
+
+    // key + value cache for the self attention
+    // TODO: move to llama_state
+    struct llama_kv_cache kv_self;
+
+    // the model memory buffer
+    std::vector<uint8_t> buf;
+
+    // tensors
+    int n_loaded;
     std::unordered_map<std::string, struct ggml_tensor *> tensors;
 };
 
@@ -102,8 +180,91 @@ struct llama_context {
     // decode output (2-dimensional array: [n_tokens][n_vocab])
     std::vector<float> logits;
     bool logits_all = false;
+
+    // input embedding (1-dimensional array: [n_embd])
+    std::vector<float> embedding;
+
+    // memory buffers used to evaluate the model
+    // TODO: move in llama_state
+    std::vector<uint8_t> buf_compute;
+    std::vector<uint8_t> buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
+
+    int    buf_last = 0;
+    size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
+
+    void use_buf(struct ggml_context * ctx, int i) {
+#if defined(LLAMA_USE_SCRATCH)
+        size_t last_size = 0;
+
+        if (i == -1) {
+            last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
+        } else {
+            auto & buf = buf_scratch[i];
+            last_size = ggml_set_scratch(ctx, { 0, buf.size(), buf.data(), });
+        }
+
+        if (buf_last >= 0) {
+            buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
+        }
+
+        buf_last = i;
+#else
+        (void) i;
+        (void) ctx;
+#endif
+    }
+
+    size_t get_buf_max_mem(int i) const {
+#if defined(LLAMA_USE_SCRATCH)
+        return buf_max_size[i];
+#else
+        (void) i;
+        return 0;
+#endif
+    }
 };
 
+//
+// kv cache
+//
+
+static bool kv_cache_init(
+        const struct llama_hparams & hparams,
+             struct llama_kv_cache & cache,
+                         ggml_type   wtype,
+                               int   n_ctx) {
+    const int n_embd  = hparams.n_embd;
+    const int n_layer = hparams.n_layer;
+
+    const int n_mem      = n_layer*n_ctx;
+    const int n_elements = n_embd*n_mem;
+
+    cache.buf.resize(2*n_elements*ggml_type_size(wtype) + 2u*MB);
+
+    struct ggml_init_params params;
+    params.mem_size   = cache.buf.size();
+    params.mem_buffer = cache.buf.data();
+
+    cache.ctx = ggml_init(params);
+
+    if (!cache.ctx) {
+        fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
+        return false;
+    }
+
+    cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
+    cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
+
+    return true;
+}
+
+static void kv_cache_free(struct llama_kv_cache & cache) {
+    if (cache.ctx) {
+        ggml_free(cache.ctx);
+        cache.ctx = nullptr;
+    }
+}
+
 struct llama_context_params llama_context_default_params() {
     struct llama_context_params result = {
         /*.n_ctx      =*/ 512,
@@ -112,6 +273,8 @@ struct llama_context_params llama_context_default_params() {
         /*.f16_kv     =*/ false,
         /*.logits_all =*/ false,
         /*.vocab_only =*/ false,
+        /*.use_mlock  =*/ false,
+        /*.embedding  =*/ false,
     };
 
     return result;
@@ -203,6 +366,22 @@ static bool llama_model_load(
             fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
         }
 
+        if (hparams.n_layer == 32) {
+            model.type = e_model::MODEL_7B;
+        }
+
+        if (hparams.n_layer == 40) {
+            model.type = e_model::MODEL_13B;
+        }
+
+        if (hparams.n_layer == 60) {
+            model.type = e_model::MODEL_30B;
+        }
+
+        if (hparams.n_layer == 80) {
+            model.type = e_model::MODEL_65B;
+        }
+
         fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
         fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
         fprintf(stderr, "%s: n_embd  = %d\n", __func__, hparams.n_embd);
@@ -213,6 +392,7 @@ static bool llama_model_load(
         fprintf(stderr, "%s: f16     = %d\n", __func__, hparams.f16);
         fprintf(stderr, "%s: n_ff    = %d\n", __func__, n_ff);
         fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
+        fprintf(stderr, "%s: type    = %d\n", __func__, model.type);
     }
 
     // load vocab
@@ -309,11 +489,32 @@ static bool llama_model_load(
         fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
     }
 
+    // print memory requirements
+    {
+        const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
+
+        // this is the total memory required to run the inference
+        const size_t mem_required =
+            ctx_size +
+            MEM_REQ_SCRATCH0.at(model.type) +
+            MEM_REQ_SCRATCH1.at(model.type) +
+            MEM_REQ_EVAL.at    (model.type);
+
+        // this is the memory required by one llama_state
+        const size_t mem_required_state =
+            scale*MEM_REQ_KV_SELF.at(model.type);
+
+        fprintf(stderr, "%s: mem required  = %7.2f MB (+ %7.2f MB per state)\n", __func__,
+                mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
+    }
+
     // create the ggml context
     {
+        lctx.model.buf.resize(ctx_size);
+
         struct ggml_init_params params = {
-            /*.mem_size   =*/ ctx_size,
-            /*.mem_buffer =*/ NULL,
+            /*.mem_size   =*/ lctx.model.buf.size(),
+            /*.mem_buffer =*/ lctx.model.buf.data(),
         };
 
         model.ctx = ggml_init(params);
@@ -376,25 +577,6 @@ static bool llama_model_load(
         }
     }
 
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-
-        const int n_mem      = n_layer*n_ctx;
-        const int n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
-        model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
-
-        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
-
-        fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
     const size_t file_offset = fin.tellg();
 
     fin.close();
@@ -418,9 +600,10 @@ static bool llama_model_load(
 
         // load weights
         {
-            int n_tensors = 0;
             size_t total_size = 0;
 
+            model.n_loaded = 0;
+
             fprintf(stderr, "%s: ", __func__);
 
             while (true) {
@@ -585,7 +768,10 @@ static bool llama_model_load(
                 }
 
                 //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
-                if (++n_tensors % 8 == 0) {
+                model.n_loaded++;
+
+                // progress
+                if (model.n_loaded % 8 == 0) {
                     fprintf(stderr, ".");
                     fflush(stderr);
                 }
@@ -593,14 +779,18 @@ static bool llama_model_load(
 
             fprintf(stderr, " done\n");
 
-            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
+            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
+            if (model.n_loaded == 0) {
+                fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
+            } else if (model.n_loaded != (int) model.tensors.size()) {
+                fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
+                return false;
+            }
         }
 
         fin.close();
     }
 
-    lctx.logits.reserve(lctx.model.hparams.n_ctx);
-
     lctx.t_load_us = ggml_time_us() - t_start_us;
 
     return true;
@@ -626,6 +816,10 @@ static bool llama_eval_internal(
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
 
+    auto & kv_self = model.kv_self;
+
+    LLAMA_ASSERT(!!kv_self.ctx);
+
     const int n_embd  = hparams.n_embd;
     const int n_layer = hparams.n_layer;
     const int n_ctx   = hparams.n_ctx;
@@ -634,27 +828,11 @@ static bool llama_eval_internal(
     const int n_rot   = hparams.n_embd/hparams.n_head;
 
     auto & mem_per_token = lctx.mem_per_token;
-
-    // TODO: fix this hardcoded size
-    static size_t buf_size = 512u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
-        const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
-        //fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        buf_size = buf_size_new;
-        buf = realloc(buf, buf_size);
-        if (buf == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-            return false;
-        }
-    }
+    auto & buf_compute   = lctx.buf_compute;
 
     struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_size,
-        /*.mem_buffer =*/ buf,
+        /*.mem_size   =*/ buf_compute.size(),
+        /*.mem_buffer =*/ buf_compute.data(),
     };
 
     struct ggml_context * ctx0 = ggml_init(params);
@@ -671,6 +849,8 @@ static bool llama_eval_internal(
 
         struct ggml_tensor * cur;
 
+        lctx.use_buf(ctx0, 0);
+
         // norm
         {
             cur = ggml_rms_norm(ctx0, inpL);
@@ -689,8 +869,8 @@ static bool llama_eval_internal(
 
             // store key and value to memory
             if (N >= 1) {
-                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctx0, kv_self.v, N*n_embd, (ggml_element_size(kv_self.v)*n_embd)*(il*n_ctx + n_past));
 
                 ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
                 ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
@@ -711,7 +891,7 @@ static bool llama_eval_internal(
                 ggml_permute(ctx0,
                         ggml_rope(ctx0,
                             ggml_reshape_3d(ctx0,
-                                ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                                ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
                                 n_embd/n_head, n_head, n_past + N),
                             n_past, n_rot, 1),
                         0, 2, 1, 3);
@@ -737,7 +917,7 @@ static bool llama_eval_internal(
                 ggml_cpy(ctx0,
                     ggml_permute(ctx0,
                             ggml_reshape_3d(ctx0,
-                                ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+                                ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.v)*n_embd),
                                 n_embd/n_head, n_head, n_past + N),
                             1, 2, 0, 3),
                     ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
@@ -759,6 +939,8 @@ static bool llama_eval_internal(
                     cur);
         }
 
+        lctx.use_buf(ctx0, 1);
+
         struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
 
         // feed-forward network
@@ -777,7 +959,6 @@ static bool llama_eval_internal(
                     model.layers[il].w3,
                     cur);
 
-
             cur = ggml_mul_mat(ctx0,
                     model.layers[il].w1,
                     cur);
@@ -792,26 +973,34 @@ static bool llama_eval_internal(
                     cur);
         }
 
-        cur  = ggml_add(ctx0, cur, inpFF);
+        cur = ggml_add(ctx0, cur, inpFF);
 
         // input for next layer
         inpL = cur;
     }
 
+    lctx.use_buf(ctx0, 0);
+
+    // used at the end to optionally extract the embeddings
+    struct ggml_tensor * embeddings = NULL;
+
     // norm
     {
+
         inpL = ggml_rms_norm(ctx0, inpL);
 
         // inpL = norm*inpL
         inpL = ggml_mul(ctx0,
                     ggml_repeat(ctx0, model.norm, inpL),
                     inpL);
+
+        embeddings = inpL;
     }
 
     // lm_head
-    {
-        inpL = ggml_mul_mat(ctx0, model.output, inpL);
-    }
+    inpL = ggml_mul_mat(ctx0, model.output, inpL);
+
+    lctx.use_buf(ctx0, -1);
 
     // logits -> probs
     //inpL = ggml_soft_max(ctx0, inpL);
@@ -828,21 +1017,38 @@ static bool llama_eval_internal(
     //embd_w.resize(n_vocab*N);
     //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
 
-    auto & logits_out = lctx.logits;
+    // extract logits
+    {
+        auto & logits_out = lctx.logits;
 
-    if (lctx.logits_all) {
-        logits_out.resize(n_vocab * N);
-        memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
-    } else {
-        // return result for just the last token
-        logits_out.resize(n_vocab);
-        memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+        if (lctx.logits_all) {
+            logits_out.resize(n_vocab * N);
+            memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+        } else {
+            // return result for just the last token
+            logits_out.resize(n_vocab);
+            memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+        }
+    }
+
+    // extract embeddings
+    if (lctx.embedding.size()) {
+        auto & embedding_out = lctx.embedding;
+
+        embedding_out.resize(n_embd);
+        memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
     }
 
     if (mem_per_token == 0) {
         mem_per_token = ggml_used_mem(ctx0)/N;
     }
-    //fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
+
+#if 0
+    printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\n", __func__,
+            ggml_used_mem(ctx0)/1024.0/1024.0,
+            lctx.get_buf_max_mem(0)/1024.0/1024.0,
+            lctx.get_buf_max_mem(1)/1024.0/1024.0);
+#endif
 
     ggml_free(ctx0);
 
@@ -1415,19 +1621,64 @@ struct llama_context * llama_init_from_file(
     ctx->rng = std::mt19937(params.seed);
     ctx->logits_all = params.logits_all;
 
-    ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
+    ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
-    if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory, params.vocab_only)) {
+    if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, memory_type,
+                          params.vocab_only)) {
         fprintf(stderr, "%s: failed to load model\n", __func__);
-        delete ctx;
+        llama_free(ctx);
         return nullptr;
     }
 
+    if (params.use_mlock) {
+        char *err;
+        if (!ggml_mlock(ctx->model.ctx, &err)) {
+            fprintf(stderr, "%s\n", err);
+            free(err);
+            llama_free(ctx);
+            return nullptr;
+        }
+    }
+
+    // reserve memory for context buffers
+    {
+        if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx)) {
+            fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
+            llama_free(ctx);
+            return nullptr;
+        }
+
+        {
+            const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
+            fprintf(stderr, "%s: kv self size  = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
+        }
+
+        const auto & hparams = ctx->model.hparams;
+        if (params.logits_all) {
+            ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
+        } else {
+            ctx->logits.reserve(hparams.n_ctx);
+        }
+
+        if (params.embedding){
+            ctx->embedding.reserve(hparams.n_embd);
+        }
+
+        ctx->buf_compute.resize(MEM_REQ_EVAL.at(ctx->model.type));
+
+        ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0.at(ctx->model.type));
+        ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1.at(ctx->model.type));
+    }
+
     return ctx;
 }
 
 void llama_free(struct llama_context * ctx) {
-    ggml_free(ctx->model.ctx);
+    kv_cache_free(ctx->model.kv_self);
+
+    if (ctx->model.ctx) {
+        ggml_free(ctx->model.ctx);
+    }
 
     delete ctx;
 }
@@ -1491,6 +1742,10 @@ float * llama_get_logits(struct llama_context * ctx) {
     return ctx->logits.data();
 }
 
+float * llama_get_embeddings(struct llama_context * ctx) {
+    return ctx->embedding.data();
+}
+
 const char * llama_token_to_str(struct llama_context * ctx, llama_token token) {
     if (token >= llama_n_vocab(ctx)) {
         return nullptr;
@@ -1576,4 +1831,3 @@ const char * llama_print_system_info(void) {
 
     return s.c_str();
 }
-
diff --git a/llama.h b/llama.h
index 3df9ed1fd..9943d96ba 100644
--- a/llama.h
+++ b/llama.h
@@ -53,6 +53,8 @@ extern "C" {
         bool f16_kv;     // use fp16 for KV cache
         bool logits_all; // the llama_eval() call computes all logits, not just the last one
         bool vocab_only; // only load the vocabulary, no weights
+        bool use_mlock;  // force system to keep model in RAM
+        bool embedding;  // embedding mode only
     };
 
     LLAMA_API struct llama_context_params llama_context_default_params();
@@ -108,6 +110,10 @@ extern "C" {
     // Cols: n_vocab
     LLAMA_API float * llama_get_logits(struct llama_context * ctx);
 
+    // Get the embeddings for the input
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+
     // Token Id -> String. Uses the vocabulary in the provided context
     LLAMA_API const char * llama_token_to_str(struct llama_context * ctx, llama_token token);
 
diff --git a/llamacpp.dll b/llamacpp.dll
index 0430f18fa..4b09ce6a6 100644
Binary files a/llamacpp.dll and b/llamacpp.dll differ
diff --git a/main.cpp b/main.cpp
index 5ba6d5a75..3f49ad997 100644
--- a/main.cpp
+++ b/main.cpp
@@ -199,6 +199,8 @@ int main(int argc, char ** argv) {
         lparams.seed       = params.seed;
         lparams.f16_kv     = params.memory_f16;
         lparams.logits_all = params.perplexity;
+        lparams.use_mlock  = params.use_mlock;
+        lparams.embedding  = params.embedding;
 
         ctx = llama_init_from_file(params.model.c_str(), lparams);
 
@@ -215,11 +217,23 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
-    // determine the required inference memory per token:
-    // TODO: better way to do that
-    {
-        const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
-        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
+    // determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters
+    // uncomment the "used_mem" line in llama.cpp to see the results
+    if (params.mem_test) {
+        {
+            const std::vector<llama_token> tmp(params.n_batch, 0);
+            llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
+        }
+
+        {
+            const std::vector<llama_token> tmp = { 0, };
+            llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
+        }
+
+        llama_print_timings(ctx);
+        llama_free(ctx);
+
+        return 0;
     }
 
     if (params.perplexity) {
@@ -292,6 +306,7 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd;
 
+
     int last_n_size = params.repeat_last_n;
     std::vector<llama_token> last_n_tokens(last_n_size);
     std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
@@ -324,6 +339,27 @@ int main(int argc, char ** argv) {
     // the first thing we will do is to output the prompt, so set color accordingly
     set_console_state(CONSOLE_STATE_PROMPT);
 
+    if (params.embedding){
+        embd = embd_inp;
+
+        if (embd.size() > 0) {
+            if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return 1;
+            }
+        }
+
+        const auto embeddings = llama_get_embeddings(ctx);
+
+        // TODO: print / use the embeddings
+
+        if (params.use_color) {
+            printf(ANSI_COLOR_RESET);
+        }
+
+        return 0;
+    }
+
     while (remaining_tokens > 0 || params.interactive) {
         // predict
         if (embd.size() > 0) {
@@ -336,7 +372,7 @@ int main(int argc, char ** argv) {
         n_past += embd.size();
         embd.clear();
 
-        if ((int) embd_inp.size() <= input_consumed) {
+        if ((int) embd_inp.size() <= input_consumed && !is_interacting) {
             // out of user input, sample next token
             const float top_k          = params.top_k;
             const float top_p          = params.top_p;
@@ -363,7 +399,7 @@ int main(int argc, char ** argv) {
             }
 
             // replace end of text token with newline token when in interactive mode
-            if (id == llama_token_eos() && params.interactive) {
+            if (id == llama_token_eos() && params.interactive && !params.instruct) {
                 id = llama_token_newline.front();
                 if (params.antiprompt.size() != 0) {
                     // tokenize and inject first reverse prompt
@@ -415,13 +451,16 @@ int main(int argc, char ** argv) {
             }
 
             // Check if each of the reverse prompts appears at the end of the output.
-            for (std::string antiprompt : params.antiprompt) {
+            for (std::string & antiprompt : params.antiprompt) {
                 if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
                     is_interacting = true;
+                    set_console_state(CONSOLE_STATE_USER_INPUT);
+                    fflush(stdout);
                     break;
                 }
             }
-            if (is_interacting) {
+
+            if (n_past > 0 && is_interacting) {
                 // potentially set color to indicate we are taking user input
                 set_console_state(CONSOLE_STATE_USER_INPUT);
 
@@ -459,13 +498,20 @@ int main(int argc, char ** argv) {
 
                 input_noecho = true; // do not echo this again
             }
-            is_interacting = false;
+
+            if (n_past > 0) {
+                is_interacting = false;
+            }
         }
 
         // end of text token
         if (embd.back() == llama_token_eos()) {
-            fprintf(stderr, " [end of text]\n");
-            break;
+            if (params.instruct) {
+                is_interacting = true;
+            } else {
+                fprintf(stderr, " [end of text]\n");
+                break;
+            }
         }
 
         // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
@@ -480,7 +526,6 @@ int main(int argc, char ** argv) {
 #endif
 
     llama_print_timings(ctx);
-
     llama_free(ctx);
 
     set_console_state(CONSOLE_STATE_DEFAULT);
diff --git a/main.exe b/main.exe
index 47624ee83..bc89eb362 100644
Binary files a/main.exe and b/main.exe differ
diff --git a/quantize.exe b/quantize.exe
index c90b88d73..979c0f4b2 100644
Binary files a/quantize.exe and b/quantize.exe differ
diff --git a/utils.cpp b/utils.cpp
index 45c9cabb1..2f995c12d 100644
--- a/utils.cpp
+++ b/utils.cpp
@@ -1,3 +1,5 @@
+#include "ggml.h"
+
 #include "utils.h"
 
 #include <cassert>
@@ -77,8 +79,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--memory_f16") {
-            params.memory_f16 = true;
+        } else if (arg == "--memory_f32") {
+            params.memory_f16 = false;
         } else if (arg == "--top_p") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -109,6 +111,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
+            params.n_batch = std::min(512, params.n_batch);
         } else if (arg == "-m" || arg == "--model") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -117,12 +120,20 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.model = argv[i];
         } else if (arg == "-i" || arg == "--interactive") {
             params.interactive = true;
+        } else if (arg == "--embedding") {
+            params.embedding = true;
+        } else if (arg == "--interactive-start") {
+            params.interactive = true;
         } else if (arg == "--interactive-first") {
             params.interactive_start = true;
         } else if (arg == "-ins" || arg == "--instruct") {
             params.instruct = true;
         } else if (arg == "--color") {
             params.use_color = true;
+        } else if (arg == "--mlock") {
+            params.use_mlock = true;
+        } else if (arg == "--mtest") {
+            params.mem_test = true;
         } else if (arg == "-r" || arg == "--reverse-prompt") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -185,11 +196,15 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --repeat_penalty N    penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
     fprintf(stderr, "  -c N, --ctx_size N    size of the prompt context (default: %d)\n", params.n_ctx);
     fprintf(stderr, "  --ignore-eos          ignore end of stream token and continue generating\n");
-    fprintf(stderr, "  --memory_f16          use f16 instead of f32 for memory key+value\n");
+    fprintf(stderr, "  --memory_f32          use f32 instead of f16 for memory key+value\n");
     fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
     fprintf(stderr, "  --n_parts N           number of model parts (default: -1 = determine from dimensions)\n");
     fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --perplexity          compute perplexity over the prompt\n");
+    if (ggml_mlock_supported()) {
+        fprintf(stderr, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
+    }
+    fprintf(stderr, "  --mtest               compute maximum memory usage\n");
     fprintf(stderr, "  -m FNAME, --model FNAME\n");
     fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
     fprintf(stderr, "\n");
diff --git a/utils.h b/utils.h
index b0de556c9..d469bc6a0 100644
--- a/utils.h
+++ b/utils.h
@@ -14,12 +14,13 @@
 //
 
 struct gpt_params {
-    int32_t seed          = -1;  // RNG seed
+    int32_t seed          = -1;   // RNG seed
     int32_t n_threads     = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t n_predict     = 128; // new tokens to predict
-    int32_t repeat_last_n = 64;  // last n tokens to penalize
-    int32_t n_parts       = -1;  // amount of model parts (-1 = determine from model dimensions)
-    int32_t n_ctx         = 512; //context size
+    int32_t n_predict     = 128;  // new tokens to predict
+    int32_t repeat_last_n = 64;   // last n tokens to penalize
+    int32_t n_parts       = -1;   // amount of model parts (-1 = determine from model dimensions)
+    int32_t n_ctx         = 512;  // context size
+    int32_t n_batch       = 8;    // batch size for prompt processing
 
     // sampling parameters
     int32_t top_k = 40;
@@ -27,21 +28,25 @@ struct gpt_params {
     float   temp  = 0.80f;
     float   repeat_penalty  = 1.10f;
 
-    int32_t n_batch = 8; // batch size for prompt processing
-
     std::string model  = "models/lamma-7B/ggml-model.bin"; // model path
     std::string prompt = "";
 
+
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
 
-    bool memory_f16        = false; // use f16 instead of f32 for memory kv
+    bool memory_f16        = true;  // use f16 instead of f32 for memory kv
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
     bool interactive       = false; // interactive mode
+
+    bool embedding         = false; // get only sentence embedding
     bool interactive_start = false; // wait for user input immediately
+
     bool instruct          = false; // instruction mode (used for Alpaca models)
     bool ignore_eos        = false; // do not stop generating after eos
     bool perplexity        = false; // compute perplexity over the prompt
+    bool use_mlock         = false; // use mlock to keep model in memory
+    bool mem_test          = false; // compute maximum memory usage
 };
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);