diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 9c1de5823..5b1b5ddfb 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -54,6 +54,7 @@ jobs:
           cd build
           cmake ..
           cmake --build . --config Release
+          ctest --output-on-failure
 
   macOS-latest-make:
     runs-on: macos-latest
@@ -90,6 +91,7 @@ jobs:
           cd build
           cmake ..
           cmake --build . --config Release
+          ctest --output-on-failure
 
   windows-latest-cmake:
     runs-on: windows-latest
@@ -106,6 +108,7 @@ jobs:
           cd build
           cmake ..
           cmake --build . --config Release
+          ctest --output-on-failure
 
       - name: Get commit hash
         id: commit
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index bc9aff7b7..d1a43caa6 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -40,7 +40,7 @@ jobs:
         uses: docker/login-action@v2
         with:
           registry: ghcr.io
-          username: ${{ github.actor }}
+          username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push Docker image (versioned)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 38e7266dc..bf0e77b4a 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,131 +1,252 @@
-cmake_minimum_required(VERSION 3.8)
-project("llama.cpp")
+cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
+project("llama.cpp" C CXX)
 
-set(CMAKE_CXX_STANDARD 20)
-set(CMAKE_CXX_STANDARD_REQUIRED true)
-set(CMAKE_C_STANDARD 11)
-set(THREADS_PREFER_PTHREAD_FLAG ON)
-find_package(Threads REQUIRED)
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
     set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()
 
-option(LLAMA_ALL_WARNINGS            "llama: enable all compiler warnings"                   ON)
-option(LLAMA_ALL_WARNINGS_3RD_PARTY  "llama: enable all compiler warnings in 3rd party libs" OFF)
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
-option(LLAMA_SANITIZE_THREAD         "llama: enable thread sanitizer"    OFF)
-option(LLAMA_SANITIZE_ADDRESS        "llama: enable address sanitizer"   OFF)
-option(LLAMA_SANITIZE_UNDEFINED      "llama: enable undefined sanitizer" OFF)
+if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+    set(LLAMA_STANDALONE ON)
 
-if (APPLE)
-    option(LLAMA_NO_ACCELERATE       "llama: disable Accelerate framework" OFF)
-    option(LLAMA_NO_AVX              "llama: disable AVX" OFF)
-    option(LLAMA_NO_AVX2             "llama: disable AVX2" OFF)
-    option(LLAMA_NO_FMA              "llama: disable FMA" OFF)
+    # configure project version
+    # TODO
+else()
+    set(LLAMA_STANDALONE OFF)
 endif()
 
+if (EMSCRIPTEN)
+    set(BUILD_SHARED_LIBS_DEFAULT OFF)
+
+    option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON)
+else()
+    if (MINGW)
+        set(BUILD_SHARED_LIBS_DEFAULT OFF)
+    else()
+        set(BUILD_SHARED_LIBS_DEFAULT ON)
+    endif()
+endif()
+
+
+#
+# Option list
+#
+
+# general
+option(LLAMA_STATIC                 "llama: static link libraries"                          OFF)
+option(LLAMA_NATIVE                 "llama: enable -march=native flag"                      OFF)
+option(LLAMA_LTO                    "llama: enable link time optimization"                  OFF)
+
+# debug
+option(LLAMA_ALL_WARNINGS           "llama: enable all compiler warnings"                   ON)
+option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
+option(LLAMA_GPROF                  "llama: enable gprof"                                   OFF)
+
+# sanitizers
+option(LLAMA_SANITIZE_THREAD        "llama: enable thread sanitizer"                        OFF)
+option(LLAMA_SANITIZE_ADDRESS       "llama: enable address sanitizer"                       OFF)
+option(LLAMA_SANITIZE_UNDEFINED     "llama: enable undefined sanitizer"                     OFF)
+
+# instruction set specific
+option(LLAMA_AVX                    "llama: enable AVX"                                     ON)
+option(LLAMA_AVX2                   "llama: enable AVX2"                                    ON)
+option(LLAMA_FMA                    "llama: enable FMA"                                     ON)
+
+# 3rd party libs
+option(LLAMA_ACCELERATE             "llama: enable Accelerate framework"                    ON)
+option(LLAMA_OPENBLAS               "llama: use OpenBLAS"                                   OFF)
+
+option(LLAMA_BUILD_TESTS            "llama: build tests"    ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_EXAMPLES         "llama: build examples" ${LLAMA_STANDALONE})
+
+#
+# Compile flags
+#
+
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+set(CMAKE_C_STANDARD_REQUIRED true)
+set(THREADS_PREFER_PTHREAD_FLAG ON)
+find_package(Threads REQUIRED)
+
 if (NOT MSVC)
     if (LLAMA_SANITIZE_THREAD)
-        set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -fsanitize=thread")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
+        add_compile_options(-fsanitize=thread)
     endif()
 
     if (LLAMA_SANITIZE_ADDRESS)
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
+        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
     endif()
 
     if (LLAMA_SANITIZE_UNDEFINED)
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
+        add_compile_options(-fsanitize=undefined)
     endif()
 endif()
 
-if (APPLE AND NOT LLAMA_NO_ACCELERATE)
+if (APPLE AND LLAMA_ACCELERATE)
     find_library(ACCELERATE_FRAMEWORK Accelerate)
     if (ACCELERATE_FRAMEWORK)
         message(STATUS "Accelerate framework found")
 
-        set(LLAMA_EXTRA_LIBS  ${LLAMA_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
-        set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
+        add_compile_definitions(GGML_USE_ACCELERATE)
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
     else()
         message(WARNING "Accelerate framework not found")
     endif()
 endif()
+if (LLAMA_OPENBLAS)
+    if (LLAMA_STATIC)
+        set(BLA_STATIC ON)
+    endif()
+
+    set(BLA_VENDOR OpenBLAS)
+    find_package(BLAS)
+    if (BLAS_FOUND)
+        message(STATUS "OpenBLAS found")
+
+        add_compile_definitions(GGML_USE_OPENBLAS)
+        add_link_options(${BLAS_LIBRARIES})
+    else()
+        message(WARNING "OpenBLAS not found")
+    endif()
+endif()
 
 if (LLAMA_ALL_WARNINGS)
     if (NOT MSVC)
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
-            -Wall                           \
-            -Wextra                         \
-            -Wpedantic                      \
-            -Wshadow                        \
-            -Wcast-qual                     \
-            -Wstrict-prototypes             \
-            -Wpointer-arith                 \
-            -Wno-unused-function            \
-        ")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
-            -Wall                           \
-            -Wextra                         \
-            -Wpedantic                      \
-            -Wcast-qual                     \
-        ")
+        set(c_flags
+            -Wall
+            -Wextra
+            -Wpedantic
+            -Wshadow
+            -Wcast-qual
+            -Wstrict-prototypes
+            -Wpointer-arith
+            -Wno-unused-function
+        )
+        set(cxx_flags
+            -Wall
+            -Wextra
+            -Wpedantic
+            -Wcast-qual
+        )
     else()
         # todo : msvc
     endif()
+
+    add_compile_options(
+            "$<$<COMPILE_LANGUAGE:C>:${c_flags}>"
+            "$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
+    )
+
 endif()
 
-message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
-
-if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
-    message(STATUS "ARM detected")
-else()
-    message(STATUS "x86 detected")
-    if (MSVC)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
-        set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
+if (LLAMA_LTO)
+    include(CheckIPOSupported)
+    check_ipo_supported(RESULT result OUTPUT output)
+    if (result)
+        set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
     else()
-        if(NOT LLAMA_NO_AVX)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
-        endif()
-        if(NOT LLAMA_NO_AVX2)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
-        endif()
-        if(NOT LLAMA_NO_FMA)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
-        endif()
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
+        message(WARNING "IPO is not supported: ${output}")
     endif()
 endif()
 
-# if (LLAMA_PERF)
-#     set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_PERF)
-# endif()
+# Architecture specific
+# TODO: probably these flags need to be tweaked on some architectures
+#       feel free to update the Makefile for your architecture and send a pull request or issue
+message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
+if (NOT MSVC)
+    if (LLAMA_STATIC)
+        add_link_options(-static)
+        if (MINGW)
+            add_link_options(-static-libgcc -static-libstdc++)
+        endif()
+    endif()
+    if (LLAMA_GPROF)
+        add_compile_options(-pg)
+    endif()
+    if (LLAMA_NATIVE)
+        add_compile_options(-march=native)
+    endif()
+endif()
 
-add_executable(llama
-    main.cpp
-    utils.cpp
-    utils.h)
+if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
+    message(STATUS "ARM detected")
+    if (MSVC)
+        # TODO: arm msvc?
+    else()
+        if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
+            add_compile_options(-mcpu=native)
+        endif()
+        # TODO: armv6,7,8 version specific flags
+    endif()
+elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$")
+    message(STATUS "x86 detected")
+    if (MSVC)
+        if (LLAMA_AVX2)
+            add_compile_options(/arch:AVX2)
+        elseif (LLAMA_AVX)
+            add_compile_options(/arch:AVX)
+        endif()
+    else()
+        add_compile_options(-mf16c)
+        if (LLAMA_FMA)
+            add_compile_options(-mfma)
+        endif()
+        if (LLAMA_AVX)
+            add_compile_options(-mavx)
+        endif()
+        if (LLAMA_AVX2)
+            add_compile_options(-mavx2)
+        endif()
+    endif()
+else()
+    # TODO: support PowerPC
+    message(STATUS "Unknown architecture")
+endif()
 
-add_executable(quantize
-    quantize.cpp
-    utils.cpp
-    utils.h)
 
-add_library(ggml
-    ggml.c
-    ggml.h)
+#
+# Build library
+#
 
-target_compile_definitions(ggml PUBLIC ${LLAMA_EXTRA_FLAGS})
-target_compile_definitions(llama PUBLIC ${LLAMA_EXTRA_FLAGS})
-target_compile_definitions(quantize PUBLIC ${LLAMA_EXTRA_FLAGS})
+add_executable(llama main.cpp)
+
+add_executable(quantize quantize.cpp)
+
+add_library(utils OBJECT
+            utils.cpp
+            utils.h)
+
+target_include_directories(utils PUBLIC .)
+target_compile_features(utils PUBLIC cxx_std_11) # don't bump
+
+add_library(ggml OBJECT
+            ggml.c
+            ggml.h)
 
-target_link_libraries(ggml PRIVATE ${LLAMA_EXTRA_LIBS})
 target_include_directories(ggml PUBLIC .)
-target_link_libraries(quantize PRIVATE ggml)
-target_link_libraries(llama PRIVATE ggml)
-target_link_libraries(ggml PRIVATE Threads::Threads)
+target_compile_features(ggml PUBLIC c_std_11) # don't bump
+
+#
+# Linking
+#
+
+target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
+target_link_libraries(llama PRIVATE ggml utils)
+target_link_libraries(quantize PRIVATE ggml utils)
+
+#
+# programs, examples and tests
+#
+
+if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
+    enable_testing()
+    add_subdirectory(tests)
+endif ()
+
+#if (LLAMA_BUILD_EXAMPLES)
+#    add_subdirectory(examples)
+#endif()
diff --git a/Makefile b/Makefile
index 44fb29840..071275470 100644
--- a/Makefile
+++ b/Makefile
@@ -17,7 +17,7 @@ CXXV := $(shell $(CXX) --version | head -n 1)
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
 ifeq ($(UNAME_S),Darwin)
 	ifneq ($(UNAME_P),arm)
-		SYSCTL_M := $(shell sysctl -n hw.optional.arm64)
+		SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
 		ifeq ($(SYSCTL_M),1)
 			# UNAME_P := arm
 			# UNAME_M := arm64
@@ -30,8 +30,9 @@ endif
 # Compile flags
 #
 
+# keep standard at C11 and C++11
 CFLAGS   = -I.              -O3 -DNDEBUG -std=c11   -fPIC
-CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC
+CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
 LDFLAGS  =
 
 # OS specific
@@ -52,6 +53,10 @@ ifeq ($(UNAME_S),NetBSD)
 	CFLAGS   += -pthread
 	CXXFLAGS += -pthread
 endif
+ifeq ($(UNAME_S),OpenBSD)
+	CFLAGS   += -pthread
+	CXXFLAGS += -pthread
+endif
 ifeq ($(UNAME_S),Haiku)
 	CFLAGS   += -pthread
 	CXXFLAGS += -pthread
@@ -95,30 +100,59 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 		ifneq (,$(findstring sse3,$(SSE3_M)))
 			CFLAGS += -msse3
 		endif
+		AVX512F_M := $(shell grep "avx512f " /proc/cpuinfo)
+		ifneq (,$(findstring avx512f,$(AVX512F_M)))
+			CFLAGS += -mavx512f
+		endif
+		AVX512BW_M := $(shell grep "avx512bw " /proc/cpuinfo)
+		ifneq (,$(findstring avx512bw,$(AVX512BW_M)))
+			CFLAGS += -mavx512bw
+		endif
+		AVX512DQ_M := $(shell grep "avx512dq " /proc/cpuinfo)
+		ifneq (,$(findstring avx512dq,$(AVX512DQ_M)))
+			CFLAGS += -mavx512dq
+		endif
+		AVX512VL_M := $(shell grep "avx512vl " /proc/cpuinfo)
+		ifneq (,$(findstring avx512vl,$(AVX512VL_M)))
+			CFLAGS += -mavx512vl
+		endif
+		AVX512CD_M := $(shell grep "avx512cd " /proc/cpuinfo)
+		ifneq (,$(findstring avx512cd,$(AVX512CD_M)))
+			CFLAGS += -mavx512cd
+		endif
+		AVX512ER_M := $(shell grep "avx512er " /proc/cpuinfo)
+		ifneq (,$(findstring avx512er,$(AVX512ER_M)))
+			CFLAGS += -mavx512er
+		endif
+		AVX512IFMA_M := $(shell grep "avx512ifma " /proc/cpuinfo)
+		ifneq (,$(findstring avx512ifma,$(AVX512IFMA_M)))
+			CFLAGS += -mavx512ifma
+		endif
+		AVX512PF_M := $(shell grep "avx512pf " /proc/cpuinfo)
+		ifneq (,$(findstring avx512pf,$(AVX512PF_M)))
+			CFLAGS += -mavx512pf
+		endif
 	else ifeq ($(UNAME_S),Haiku)
-		AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
-		ifneq (,$(findstring avx,$(AVX1_M)))
+		AVX1_M := $(shell sysinfo -cpu | grep -w "AVX")
+		ifneq (,$(findstring AVX,$(AVX1_M)))
 			CFLAGS += -mavx
 		endif
-		AVX2_M := $(shell sysinfo -cpu | grep "AVX2 ")
-		ifneq (,$(findstring avx2,$(AVX2_M)))
+		AVX2_M := $(shell sysinfo -cpu | grep -w "AVX2")
+		ifneq (,$(findstring AVX2,$(AVX2_M)))
 			CFLAGS += -mavx2
 		endif
-		FMA_M := $(shell sysinfo -cpu | grep "FMA ")
-		ifneq (,$(findstring fma,$(FMA_M)))
+		FMA_M := $(shell sysinfo -cpu | grep -w "FMA")
+		ifneq (,$(findstring FMA,$(FMA_M)))
 			CFLAGS += -mfma
 		endif
-		F16C_M := $(shell sysinfo -cpu | grep "F16C ")
-		ifneq (,$(findstring f16c,$(F16C_M)))
+		F16C_M := $(shell sysinfo -cpu | grep -w "F16C")
+		ifneq (,$(findstring F16C,$(F16C_M)))
 			CFLAGS += -mf16c
 		endif
 	else
 		CFLAGS += -mfma -mf16c -mavx -mavx2
 	endif
 endif
-ifeq ($(UNAME_M),amd64)
-	CFLAGS += -mavx -mavx2 -mfma -mf16c
-endif
 ifneq ($(filter ppc64%,$(UNAME_M)),)
 	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
 	ifneq (,$(findstring POWER9,$(POWER9_M)))
@@ -130,7 +164,8 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
 	endif
 endif
 ifndef LLAMA_NO_ACCELERATE
-	# Mac M1 - include Accelerate framework
+	# Mac M1 - include Accelerate framework.
+	# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
 	ifeq ($(UNAME_S),Darwin)
 		CFLAGS  += -DGGML_USE_ACCELERATE
 		LDFLAGS += -framework Accelerate
@@ -193,7 +228,7 @@ clean:
 
 main: main.cpp ggml.o utils.o
 	$(CXX) $(CXXFLAGS) main.cpp ggml.o utils.o -o main $(LDFLAGS)
-	./main -h
+	@echo "\x1b[36mrun ./main -h for help\x1b[0m"
 
 quantize: quantize.cpp ggml.o utils.o
 	$(CXX) $(CXXFLAGS) quantize.cpp ggml.o utils.o -o quantize $(LDFLAGS)
diff --git a/README.md b/README.md
index c7e5d33c7..d9a4b1bab 100644
--- a/README.md
+++ b/README.md
@@ -178,10 +178,15 @@ If you want a more ChatGPT-like experience, you can run in interactive mode by p
 In this mode, you can always interrupt generation by pressing Ctrl+C and enter one or more lines of text which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt which makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
 
 Here is an example few-shot interaction, invoked with the command
-```
-./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
 
+```bash
+# default arguments using 7B model
+./chat.sh
+
+# custom arguments using 13B model
+./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
 ```
+
 Note the use of `--color` to distinguish between user input and generated text.
 
 ![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
@@ -192,11 +197,10 @@ First, download the `ggml` Alpaca model into the `./models` folder:
 
 ```
 # use one of these
-# NOTE: these are copied from the alpaca.cpp repo - not sure how long these will work
 # TODO: add a script to simplify the download
-curl -o ggml-alpaca-7b-q4.bin -C - https://gateway.estuary.tech/gw/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
-curl -o ggml-alpaca-7b-q4.bin -C - https://ipfs.io/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
-curl -o ggml-alpaca-7b-q4.bin -C - https://cloudflare-ipfs.com/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
+curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://gateway.estuary.tech/gw/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
+curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://ipfs.io/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
+curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://cloudflare-ipfs.com/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
 ```
 
 Now run the `main` tool like this:
@@ -219,7 +223,7 @@ Sample run:
 There 26 letters in the English Alphabet
 > What is the most common way of transportation in Amsterdam?
 The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
-> List 5 words that start with "ca".                                                                       
+> List 5 words that start with "ca".
 cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
 > 
 ```
diff --git a/alpaca.sh b/alpaca.sh
index 284989bc0..2f36d6f54 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 0.96 --repeat_penalty 1 -t 7
+./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
diff --git a/chat.sh b/chat.sh
new file mode 100755
index 000000000..24a0f10ad
--- /dev/null
+++ b/chat.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+#
+# 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
diff --git a/convert-gptq-to-ggml.py b/convert-gptq-to-ggml.py
new file mode 100644
index 000000000..7fccb4d56
--- /dev/null
+++ b/convert-gptq-to-ggml.py
@@ -0,0 +1,172 @@
+# Convert a GPTQ quantized LLaMA model to a ggml compatible file
+# Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa
+#
+import os
+import re
+import sys
+import json
+import struct
+import numpy as np
+import torch
+from sentencepiece import SentencePieceProcessor
+
+if len(sys.argv) != 4:
+    print("Usage: convert-gptq-to-ggml.py llamaXXb-4bit.pt tokenizer.model out.bin\n")
+    sys.exit(1)
+
+fname_model = sys.argv[1]
+fname_tokenizer = sys.argv[2]
+dir_out = sys.argv[3]
+
+model = torch.load(fname_model, map_location="cpu")
+
+n_vocab, n_embd = model['model.embed_tokens.weight'].shape
+n_layer = 1 + max(int(m.group(1)) for name in model
+                  if (m := re.match(r'model\.layers\.([0-9]+)', name)))
+
+# hardcoded:
+n_mult = 256
+n_head = {32: 32, 40: 40, 60: 52, 80: 64}[n_layer]
+
+tokenizer = SentencePieceProcessor(fname_tokenizer)
+
+assert tokenizer.vocab_size() == n_vocab
+
+fname_out = sys.argv[3]
+
+fout = open(fname_out, "wb")
+
+fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
+fout.write(struct.pack("i", n_vocab))
+fout.write(struct.pack("i", n_embd))
+fout.write(struct.pack("i", n_mult))
+fout.write(struct.pack("i", n_head))
+fout.write(struct.pack("i", n_layer))
+fout.write(struct.pack("i", n_embd // n_head)) # rot (obsolete)
+fout.write(struct.pack("i", 4))
+
+
+# This loop unchanged from convert-pth-to-ggml.py:
+for i in range(tokenizer.vocab_size()):
+    if tokenizer.is_unknown(i):
+        # "<unk>" token (translated as ??)
+        text = " \u2047 ".encode("utf-8")
+        fout.write(struct.pack("i", len(text)))
+        fout.write(text)
+    elif tokenizer.is_control(i):
+        # "<s>"/"</s>" tokens
+        fout.write(struct.pack("i", 0))
+    elif tokenizer.is_byte(i):
+        # "<U+XX>" tokens (which may be invalid UTF-8)
+        piece = tokenizer.id_to_piece(i)
+        if len(piece) != 6:
+            print("Invalid token: " + piece)
+            sys.exit(1)
+        byte_value = int(piece[3:-1], 16)
+        fout.write(struct.pack("i", 1))
+        fout.write(struct.pack("B", byte_value))
+    else:
+        # normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
+        text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
+        fout.write(struct.pack("i", len(text)))
+        fout.write(text)
+
+def write_header(shape, dst_name, ftype_cur):
+    sname = dst_name.encode('utf-8')
+    fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
+    fout.write(struct.pack("i" * len(shape), *shape[::-1]))
+    fout.write(sname)
+
+def convert_non_q4(src_name, dst_name):
+    v = model[src_name]
+    shape = v.shape
+    print("Processing non-Q4 variable: " + src_name + " with shape: ", shape, " and type: ", v.dtype)
+    if len(shape) == 1:
+        print("  Converting to float32")
+        v = v.to(torch.float32)
+
+    ftype_cur = {torch.float16: 1, torch.float32: 0}[v.dtype]
+
+    # header
+    write_header(shape, dst_name, ftype_cur)
+
+    # data
+    v.numpy().tofile(fout)
+
+def convert_q4(src_name, dst_name, permute=False):
+    zeros = model[f"{src_name}.zeros"].numpy()
+    scales = model[f"{src_name}.scales"].numpy()
+    bias = model[f"{src_name}.bias"].numpy()
+    qweight = model[f"{src_name}.qweight"].numpy().T # transpose
+
+    # Q4_1 does not support bias; good thing the bias is always all zeros.
+    assert not np.any(bias)
+
+    # Each int32 item is actually 8 int4 items packed together, and it's transposed.
+    shape = (qweight.shape[0], qweight.shape[1] * 8)
+
+    print("Processing Q4 variable: " + src_name + " with shape: ", shape)
+
+    # The output format has the int4 weights in groups of 32 rather than 8.
+    # It looks like this:
+    # For each row:
+    #   For each group of 32 columns:
+    #     - addend (float32, 4 bytes)
+    #     - scale (float32, 4 bytes)
+    #     - weights (int4 * 32, 16 bytes)
+    # Note that in the input, the scales and addends are shared between all
+    # the columns in a row, so we end up wasting quite a bit of memory with
+    # repeated scales and addends.
+
+    addends = -zeros # flip sign
+
+    # Since the output format is mixed between integers and floats, we have
+    # to hackily view the floats as int32s just so numpy will let us
+    # concatenate them.
+    addends_view = addends.view(dtype=np.int32)
+    scales_view = scales.view(dtype=np.int32)
+
+    # Split into groups of 4 columns (i.e. 32 columns of quantized data):
+    grouped = qweight.reshape([qweight.shape[0], qweight.shape[1] // 4, 4])
+
+    # Repeat addends and scales:
+    addends_rep = np.atleast_3d(addends_view).repeat(grouped.shape[1], axis=1)
+    scales_rep = np.atleast_3d(scales_view).repeat(grouped.shape[1], axis=1)
+
+    blob = np.concatenate([scales_rep, addends_rep, grouped], axis=2, casting='no')
+
+    if permute:
+        # Permute some rows to undo the permutation done by convert_llama_weights_to_hf.py.
+        # This can be done after the above conversion because it doesn't affect column order/layout.
+        blob = (blob.reshape(n_head, 2, shape[0] // n_head // 2, *blob.shape[1:])
+                    .swapaxes(1, 2)
+                    .reshape(blob.shape))
+
+    # header
+    write_header(shape, dst_name, 3) # ftype = Q4_1
+
+    # data
+    blob.tofile(fout)
+
+convert_non_q4("model.embed_tokens.weight", "tok_embeddings.weight")
+convert_non_q4("model.norm.weight", "norm.weight")
+convert_non_q4("lm_head.weight", "output.weight")
+
+for i in range(n_layer):
+    convert_q4(f"model.layers.{i}.self_attn.q_proj", f"layers.{i}.attention.wq.weight", permute=True)
+    convert_q4(f"model.layers.{i}.self_attn.k_proj", f"layers.{i}.attention.wk.weight", permute=True)
+    convert_q4(f"model.layers.{i}.self_attn.v_proj", f"layers.{i}.attention.wv.weight")
+    convert_q4(f"model.layers.{i}.self_attn.o_proj", f"layers.{i}.attention.wo.weight")
+
+    convert_q4(f"model.layers.{i}.mlp.gate_proj", f"layers.{i}.feed_forward.w1.weight")
+    convert_q4(f"model.layers.{i}.mlp.down_proj", f"layers.{i}.feed_forward.w2.weight")
+    convert_q4(f"model.layers.{i}.mlp.up_proj",   f"layers.{i}.feed_forward.w3.weight")
+
+    convert_non_q4(f"model.layers.{i}.input_layernorm.weight", f"layers.{i}.attention_norm.weight")
+    convert_non_q4(f"model.layers.{i}.post_attention_layernorm.weight", f"layers.{i}.ffn_norm.weight")
+
+
+fout.close()
+
+print("Done. Output file: " + fname_out)
+print("")
diff --git a/convert-pth-to-ggml.py b/convert-pth-to-ggml.py
index 42f537769..db5b00fec 100644
--- a/convert-pth-to-ggml.py
+++ b/convert-pth-to-ggml.py
@@ -10,25 +10,26 @@
 #   - Name (char[name_length])
 #   - Data (float[n_dims])
 #
-# By default, the bigger matrices are converted to 16-bit floats.
-# This can be disabled by adding the "use-f32" CLI argument.
-#
 # At the start of the ggml file we write the model parameters
 # and vocabulary.
 #
+
 import argparse
+import os
 import sys
 import json
 import struct
 import numpy as np
 import torch
+
 from sentencepiece import SentencePieceProcessor
 
 def parse_args():
 
     parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
-    parser.add_argument('dir_model', help='directory containing the model checkpoint')
-    parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
+    parser.add_argument('dir_model',  help='directory containing the model checkpoint')
+    parser.add_argument('ftype',      help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1)
+    parser.add_argument('vocab_only', help='only write vocab to file', type=int, default=0, nargs='?')
     return parser.parse_args()
 
 def get_n_parts(dim):
@@ -44,8 +45,14 @@ def get_n_parts(dim):
 
 def load_hparams_and_tokenizer(dir_model):
 
+    # `dir_model` is something like `models/7B` or `models/7B/`.
+    # "tokenizer.model" is expected under model's parent dir.
+    # When `dir_model` is a symlink, f"{dir_model}/../tokenizer.model" would not be found.
+    # Let's use the model's parent dir directly.
+    model_parent_dir = os.path.dirname(os.path.normpath(dir_model))
+
     fname_hparams = f"{dir_model}/params.json"
-    fname_tokenizer = f"{dir_model}/../tokenizer.model"
+    fname_tokenizer = f"{model_parent_dir}/tokenizer.model"
 
     with open(fname_hparams, "r") as f:
         hparams = json.load(f)
@@ -60,7 +67,7 @@ def write_header(fout, hparams, ftype):
 
     keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
     values = [
-        0x67676d66,  # magic: ggml in hex
+        0x67676d66,  # magic: ggmf in hex
         1, # file version
         *[hparams[key] for key in keys],
         hparams["dim"] // hparams["n_heads"],  # rot (obsolete)
@@ -127,6 +134,29 @@ def main():
     ftype_str = ["f32", "f16"]
 
     hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
+
+    print(args)
+
+    # if only writing vocab to file
+    if args.vocab_only:
+
+        fname_model = f"{dir_model}/consolidated.00.pth"
+        fname_out = f"{dir_model}/ggml-vocab.bin"
+
+        print(f"Extracting only the vocab from '{fname_model}'\n")
+
+        model = torch.load(fname_model, map_location="cpu")
+
+        with open(fname_out, "wb") as fout:
+            fout.write(struct.pack("i", hparams["vocab_size"]))
+            write_tokens(fout, tokenizer)
+
+        del model
+
+        print(f"Done. Output file: {fname_out}\n")
+
+        return
+
     n_parts = get_n_parts(hparams["dim"])
 
     for p in range(n_parts):
@@ -144,6 +174,7 @@ def main():
             process_and_write_variables(fout, model, ftype)
 
         del model
+
         print(f"Done. Output file: {fname_out}, (part {p})\n")
 
 if __name__ == "__main__":
diff --git a/examples/chatLLaMa b/examples/chatLLaMa
new file mode 100755
index 000000000..97c48ac87
--- /dev/null
+++ b/examples/chatLLaMa
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+cd "$(dirname "$0")/.." || exit
+
+MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}"
+USER_NAME="${USER_NAME:-User}"
+AI_NAME="${AI_NAME:-ChatLLaMa}"
+
+# Adjust to the number of CPU cores you want to use.
+N_THREAD="${N_THREAD:-8}"
+# Number of tokens to predict (made it larger than default because we want a long interaction)
+N_PREDICTS="${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}"
+
+# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
+./main $GEN_OPTIONS \
+  --model "$MODEL" \
+  --threads "$N_THREAD" \
+  --n_predict "$N_PREDICTS" \
+  --color --interactive \
+  --reverse-prompt "${USER_NAME}:" \
+  --prompt "
+Text transcript of a never ending dialog, where ${USER_NAME} interacts with an AI assistant named ${AI_NAME}.
+${AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer ${USER_NAME}’s requests immediately and with details and precision.
+There are no annotations like (30 seconds passed...) or (to himself), just what ${USER_NAME} and ${AI_NAME} say alound to each other.
+The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+
+$USER_NAME: Hello, $AI_NAME!
+$AI_NAME: Hello $USER_NAME! How may I help you today?
+$USER_NAME: What time is it?
+$AI_NAME: It is $(date +%H:%M).
+$USER_NAME: What year is it?
+$AI_NAME: We are in $(date +%Y).
+$USER_NAME: Please tell me the largest city in Europe.
+$AI_NAME: The largest city in Europe is Moscow, the capital of Russia.
+$USER_NAME: What can you tell me about Moscow?
+$AI_NAME: Moscow, on the Moskva River in western Russia, is the nation’s cosmopolitan capital. In its historic core is the Kremlin, a complex that’s home to the president and tsarist treasures in the Armoury. Outside its walls is Red Square, Russia’s symbolic center.
+$USER_NAME: What is a cat?
+$AI_NAME: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
+$USER_NAME: How do I pass command line arguments to a Node.js program?
+$AI_NAME: The arguments are stored in process.argv.
+
+    argv[0] is the path to the Node. js executable.
+    argv[1] is the path to the script file.
+    argv[2] is the first argument passed to the script.
+    argv[3] is the second argument passed to the script and so on.
+$USER_NAME: Name a color.
+$AI_NAME: Blue
+$USER_NAME:" "$@"
diff --git a/flake.nix b/flake.nix
index dae4ff60f..da4bd7ba3 100644
--- a/flake.nix
+++ b/flake.nix
@@ -34,6 +34,7 @@
             cat ${./convert-pth-to-ggml.py} >> $out/bin/convert-pth-to-ggml
             chmod +x $out/bin/convert-pth-to-ggml
           '';
+          meta.mainProgram = "llama";
         };
         devShells.default = pkgs.mkShell {
           packages = with pkgs; [
diff --git a/ggml.c b/ggml.c
index 4813f74c8..8daac3510 100644
--- a/ggml.c
+++ b/ggml.c
@@ -2,7 +2,7 @@
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
-#elif !defined(__FreeBSD__) && !defined(__NetBSD__)
+#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
 #include <alloca.h>
 #endif
 
@@ -361,7 +361,7 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
 
 // AVX routines provided by GH user Const-me
 // ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
-#if __AVX2__
+#if __AVX2__ || __AVX512F__
 // Unpack 32 4-bit fields into 32 bytes
 // The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
 static inline __m256i bytesFromNibbles( const uint8_t* rsi )
@@ -397,7 +397,6 @@ static inline __m128i packNibbles( __m256i bytes )
 }
 #endif
 
-
 // method 5
 // blocks of QK elements
 // represented with a single float (delta) and QK/2 8-bit ints (i.e QK 4-bit signed integer factors)
@@ -1262,6 +1261,47 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
     *s = sumf;
 }
 
+#if __AVX512F__ && QK == 32
+static inline __m512 dot_q4_0_oneblock_avx512(
+    __m512 acc,
+    const uint8_t * pd0,
+    const uint8_t * pd1,
+    const uint8_t * pb0,
+    const uint8_t * pb1,
+    size_t bs,
+    int i
+) {
+    const float * d0_0 = (const float *) (pd0 + i*bs);
+    const float * d1_0 = (const float *) (pd1 + i*bs);
+
+    const uint8_t * restrict p0 = pb0 + (i+0)*bs;
+    const uint8_t * restrict p1 = pb1 + (i+0)*bs;
+
+    // Compute combined scale for the block
+    float scaleScalar = d0_0[0] * d1_0[0];
+    __m512 scale = _mm512_set1_ps( scaleScalar );
+
+    __m256i bx = bytesFromNibbles( p0 );
+    __m256i by = bytesFromNibbles( p1 );
+
+    // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
+    const __m256i off = _mm256_set1_epi8( 8 );
+    bx = _mm256_sub_epi8( bx, off );
+    by = _mm256_sub_epi8( by, off );
+
+    // Sign-extend 16 signed bytes into int16_t
+    __m512i x32 = _mm512_cvtepi8_epi16( bx );
+    __m512i y32 = _mm512_cvtepi8_epi16( by );
+    // Compute products of int16_t integers, add pairwise
+    __m512i i64 = _mm512_madd_epi16( x32, y32 );
+
+    // Convert int32_t to float
+    __m512 p = _mm512_cvtepi32_ps( i64 );
+    // Apply the scale, and accumulate
+    return _mm512_fmadd_ps( scale, p, acc );
+}
+#endif
+
 inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
     ggml_float sumf = 0.0;
 
@@ -1417,6 +1457,40 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
 #else
 #error "not implemented for QK"
 #endif
+#elif defined(__AVX512F__)
+
+#if QK == 32
+    // Initialize accumulator with zeros
+    __m512 acc0 = _mm512_setzero_ps();
+    __m512 acc1 = _mm512_setzero_ps();
+
+    const int superblock_size = 8;
+    const int superblock_count = nb / superblock_size;
+    const int remainder = nb % superblock_size;
+
+    for (int superblock_ix = 0; superblock_ix < superblock_count; superblock_ix += 1) {
+        int i = superblock_ix * superblock_size;
+
+        acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+0 );
+        acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+1 );
+        acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+2 );
+        acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+3 );
+        acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+4 );
+        acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+5 );
+        acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+6 );
+        acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+7 );
+    }
+
+    // Remainders
+    for (int i = superblock_count * superblock_size; i < nb; ++i) {
+        acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i );
+    }
+
+    // Horizontal sum of all lanes of the accumulator
+    sumf = _mm512_reduce_add_ps( acc0 ) + _mm512_reduce_add_ps( acc1 );
+#else
+#error "not implemented for QK"
+#endif
 #elif defined(__AVX2__)
 #if QK == 32
     const size_t countBlocks = nb;
@@ -1928,7 +2002,7 @@ inline static void ggml_vec_mad_q4_1(const int n, float * restrict y, void * res
     const size_t bs = 2*sizeof(float) + QK/2;
 
     const uint8_t * restrict pd = ((const uint8_t *)x + 0*bs);
-    const uint8_t * restrict pm = ((const uint8_t *)x + 0*bs +   sizeof(float)); 
+    const uint8_t * restrict pm = ((const uint8_t *)x + 0*bs +   sizeof(float));
     const uint8_t * restrict pb = ((const uint8_t *)x + 0*bs + 2*sizeof(float));
 
     for (int i = 0; i < nb; i++) {
diff --git a/main.cpp b/main.cpp
index dc8876dc0..ce41658d8 100644
--- a/main.cpp
+++ b/main.cpp
@@ -19,6 +19,13 @@
 #include <signal.h>
 #endif
 
+#if defined (_WIN32)
+#pragma comment(lib,"kernel32.lib")
+extern "C" __declspec(dllimport) void* __stdcall GetStdHandle(unsigned long nStdHandle);
+extern "C" __declspec(dllimport) int __stdcall GetConsoleMode(void* hConsoleHandle, unsigned long* lpMode);
+extern "C" __declspec(dllimport) int __stdcall SetConsoleMode(void* hConsoleHandle, unsigned long dwMode);
+#endif
+
 #define ANSI_COLOR_RED     "\x1b[31m"
 #define ANSI_COLOR_GREEN   "\x1b[32m"
 #define ANSI_COLOR_YELLOW  "\x1b[33m"
@@ -89,7 +96,8 @@ struct llama_model {
 };
 
 // load the model's weights from a file
-bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
+
+bool llama_model_load(const std::string & fname, llama_model & model, llama_vocab & vocab, int n_ctx, int n_parts, ggml_type memory_type = GGML_TYPE_F32) {
     fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
 
     std::vector<char> f_buf(1024*1024);
@@ -105,12 +113,12 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     {
         uint32_t magic;
         fin.read((char *) &magic, sizeof(magic));
-        if (magic == 0x67676d6c) {
+        if (magic == FILE_MAGIC_UNVERSIONED) {
             fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
                     __func__, fname.c_str());
             return false;
         }
-        if (magic != 0x67676d66) {
+        if (magic != FILE_MAGIC) {
             fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
             return false;
         }
@@ -118,15 +126,14 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         uint32_t format_version;
         fin.read((char *) &format_version, sizeof(format_version));
 
-        if (format_version != 1) {
-            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
-                    __func__, fname.c_str(), format_version);
+        if (format_version != FILE_VERSION) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
+                    __func__, fname.c_str(), format_version, FILE_VERSION);
             return false;
         }
     }
 
     int n_ff = 0;
-    int n_parts = 0;
 
     // load hparams
     {
@@ -144,7 +151,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         hparams.n_ctx = n_ctx;
 
         n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
-        n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
+
+        if (n_parts < 1) {
+            n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
+        }
+
+        // temp warning to tell the user to use "--n_parts"
+        if (hparams.f16 == 4 && n_parts != 1) {
+            fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
+            fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
+        }
 
         fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
         fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
@@ -162,12 +178,20 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     {
         std::string word;
         vocab.id_to_token.resize(model.hparams.n_vocab);
+        std::vector<char> tmp(64);
+
         for (int i = 0; i < model.hparams.n_vocab; i++) {
             uint32_t len;
             fin.read((char *) &len, sizeof(len));
 
             word.resize(len);
-            fin.read((char *) word.data(), len);
+            if (len > 0) {
+                tmp.resize(len);
+                fin.read(tmp.data(), len);
+                word.assign(tmp.data(), len);
+            } else {
+                word.clear();
+            }
 
             float score;
             fin.read((char *) &score, sizeof(score));
@@ -177,21 +201,19 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             auto &tok_score = vocab.id_to_token[i];
             tok_score.tok = word;
             tok_score.score = score;
-
-            //if (i < 30000) {
-            //    fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
-            //}
         }
     }
 
     // for the big tensors, we have the option to store the data in 16-bit floats or quantized
     // in order to save memory and also to speed up the computation
-    ggml_type wtype = GGML_TYPE_COUNT;
+    // wtype is for per-layer weights, while vtype is for other weights
+    ggml_type wtype, vtype;
     switch (model.hparams.f16) {
-        case 0: wtype = GGML_TYPE_F32;  break;
-        case 1: wtype = GGML_TYPE_F16;  break;
-        case 2: wtype = GGML_TYPE_Q4_0; break;
-        case 3: wtype = GGML_TYPE_Q4_1; break;
+        case 0: wtype = vtype = GGML_TYPE_F32;  break;
+        case 1: wtype = vtype = GGML_TYPE_F16;  break;
+        case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
+        case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
+        case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
         default:
                 {
                     fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
@@ -212,11 +234,11 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         const int n_ctx   = hparams.n_ctx;
         const int n_vocab = hparams.n_vocab;
 
-        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // tok_embeddings
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
 
         ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
 
-        ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // output
+        ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
 
         ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
 
@@ -263,10 +285,10 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
         model.layers.resize(n_layer);
 
-        model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
+        model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
 
         model.norm   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-        model.output = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        model.output = ggml_new_tensor_2d(ctx, vtype,         n_embd, n_vocab);
 
         // map by name
         model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
@@ -546,9 +568,10 @@ bool llama_eval(
         const llama_model & model,
         const int n_threads,
         const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token) {
+        const std::vector<llama_vocab::id> & embd_inp,
+              std::vector<float>           & embd_w,
+              size_t                       & mem_per_token,
+              bool return_all_logits = false) {
     const int N = embd_inp.size();
 
     const auto & hparams = model.hparams;
@@ -566,7 +589,7 @@ bool llama_eval(
     static void * buf = malloc(buf_size);
 
     if (mem_per_token > 0 && mem_per_token*N > buf_size) {
-        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
+        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
@@ -752,9 +775,14 @@ bool llama_eval(
     //embd_w.resize(n_vocab*N);
     //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
 
-    // return result for just the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+    if (return_all_logits) {
+        embd_w.resize(n_vocab * N);
+        memcpy(embd_w.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+    } else {
+        // return result for just the last token
+        embd_w.resize(n_vocab);
+        memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+    }
 
     if (mem_per_token == 0) {
         mem_per_token = ggml_used_mem(ctx0)/N;
@@ -766,6 +794,76 @@ bool llama_eval(
     return true;
 }
 
+std::vector<double> softmax(const std::vector<float>& logits) {
+    std::vector<double> probs(logits.size());
+    float max_logit = logits[0];
+    for (float v : logits) max_logit = std::max(max_logit, v);
+    double sum_exp = 0.0;
+    for (size_t i = 0; i < logits.size(); i++) {
+        // Subtract the maximum logit value from the current logit value for numerical stability
+        float logit = logits[i] - max_logit;
+        double exp_logit = std::exp(logit);
+        sum_exp += exp_logit;
+        probs[i] = exp_logit;
+    }
+    for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
+    return probs;
+}
+
+void perplexity(const llama_vocab &vocab, const llama_model &model, const gpt_params &params, size_t mem_per_token) {
+    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+    // Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+    // Output: `perplexity: 13.5106 [114/114]`
+    std::vector<llama_vocab::id> tokens = ::llama_tokenize(vocab, params.prompt, true);
+
+    int count = 0;
+    double nll = 0.0;
+    int seq_count = tokens.size() / params.n_ctx;
+    printf("Calculating perplexity over %d chunks\n", seq_count);
+    for (int i = 0; i < seq_count; ++i) {
+        int start = i * params.n_ctx;
+        int end = start + params.n_ctx - 1;
+        std::vector<llama_vocab::id> embd(tokens.begin() + start, tokens.begin() + end);
+        std::vector<float> logits;
+        auto start_t = std::chrono::high_resolution_clock::now();
+        if (!llama_eval(model, params.n_threads, 0, embd, logits, mem_per_token, true)) {
+            fprintf(stderr, "Failed to predict\n");
+            return;
+        }
+        auto end_t = std::chrono::high_resolution_clock::now();
+        if (i == 0) {
+            double seconds = std::chrono::duration<double>(end_t - start_t).count();
+            printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
+        }
+        // We get the logits for all the tokens in the context window (params.n_ctx)
+        // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
+        // calculate the perplexity over the last half the window (so the model always has
+        // some context to predict the token).
+        //
+        // We rely on the fact that attention in the forward pass only looks at previous
+        // tokens here, so the logits returned for each token are an accurate representation
+        // of what the model would have predicted at that point.
+        //
+        // Example, we have a context window of 512, we will compute perplexity for each of the
+        // last 256 tokens.  Then, we split the input up into context window size chunks to
+        // process the entire prompt.
+        for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
+            // Calculate probability of next token, given the previous ones.
+            int n_vocab = model.hparams.n_vocab;
+            std::vector<float> tok_logits(
+                logits.begin() + j * n_vocab,
+                logits.begin() + (j + 1) * n_vocab);
+            double prob = softmax(tok_logits)[tokens[start + j + 1]];
+            nll += -std::log(prob);
+            ++count;
+        }
+        // perplexity is e^(average negative log-likelihood)
+        printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        fflush(stdout);
+    }
+    printf("\n");
+}
+
 static bool is_interacting = false;
 
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
@@ -834,14 +932,14 @@ int main(int argc, char ** argv) {
 
     int64_t t_load_us = 0;
 
-    gpt_vocab vocab;
+    llama_vocab vocab;
     llama_model model;
 
     // load the model
     {
         const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
         const int64_t t_start_us = ggml_time_us();
-        if (!llama_model_load(params.model, model, vocab, params.n_ctx, memory_type)) {
+        if (!llama_model_load(params.model, model, vocab, params.n_ctx, params.n_parts, memory_type)) {
             fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
             return 1;
         }
@@ -856,23 +954,32 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
+    std::vector<float> logits;
+
+    // determine the required inference memory per token:
+    size_t mem_per_token = 0;
+    llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+
+    if (params.perplexity) {
+        perplexity(vocab, model, params, mem_per_token);
+        exit(0);
+    }
+
     int n_past = 0;
 
     int64_t t_sample_us  = 0;
     int64_t t_predict_us = 0;
 
-    std::vector<float> logits;
-
     // Add a space in front of the first character to match OG llama tokenizer behavior
     params.prompt.insert(0, 1, ' ');
     // tokenize the prompt
-    std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
+    std::vector<llama_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
 
     params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
 
     // prefix & suffix for instruct mode
-    const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
-    const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
+    const std::vector<llama_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
+    const std::vector<llama_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
 
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
@@ -880,15 +987,8 @@ int main(int argc, char ** argv) {
         params.antiprompt.push_back("### Instruction:\n\n");
     }
 
-    // tokenize the reverse prompt
-    std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
-    
-    for (auto antiprompt : params.antiprompt) {
-        antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
-    }
-
     // enable interactive mode if reverse prompt is specified
-    if (antipromptv_inp.size() != 0) {
+    if (params.antiprompt.size() != 0) {
         params.interactive = true;
     }
 
@@ -912,29 +1012,19 @@ int main(int argc, char ** argv) {
 
         fprintf(stderr, "%s: interactive mode on.\n", __func__);
 
-        if(antipromptv_inp.size()) {
-            for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
-                auto antiprompt_inp = antipromptv_inp.at(apindex);
-                fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
-                fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
-                for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
-                    fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).tok.c_str());
-                }
-                fprintf(stderr, "\n");
+        if(params.antiprompt.size()) {
+            for (auto antiprompt : params.antiprompt) {
+                fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
             }
         }
     }
     fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
     fprintf(stderr, "\n\n");
 
-    std::vector<gpt_vocab::id> embd;
-
-    // determine the required inference memory per token:
-    size_t mem_per_token = 0;
-    llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+    std::vector<llama_vocab::id> embd;
 
     int last_n_size = params.repeat_last_n;
-    std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
+    std::vector<llama_vocab::id> last_n_tokens(last_n_size);
     std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
 
     if (params.interactive) {
@@ -954,6 +1044,14 @@ int main(int argc, char ** argv) {
 
     // set the color for the prompt which will be output initially
     if (params.use_color) {
+#if defined (_WIN32)
+        // Enable ANSI colors on Windows 10+
+        unsigned long dwMode = 0;
+        void* hConOut = GetStdHandle((unsigned long)-11); // STD_OUTPUT_HANDLE (-11)
+        if (hConOut && hConOut != (void*)-1 && GetConsoleMode(hConOut, &dwMode) && !(dwMode & 0x4)) {
+            SetConsoleMode(hConOut, dwMode | 0x4); // ENABLE_VIRTUAL_TERMINAL_PROCESSING (0x4)
+        }
+#endif
         printf(ANSI_COLOR_YELLOW);
     }
 
@@ -973,7 +1071,7 @@ int main(int argc, char ** argv) {
         n_past += embd.size();
         embd.clear();
 
-        if (embd_inp.size() <= input_consumed) {
+        if ((int) embd_inp.size() <= input_consumed) {
             // out of user input, sample next token
             const float top_k = params.top_k;
             const float top_p = params.top_p;
@@ -982,7 +1080,7 @@ int main(int argc, char ** argv) {
 
             const int n_vocab = model.hparams.n_vocab;
 
-            gpt_vocab::id id = 0;
+            llama_vocab::id id = 0;
 
             {
                 const int64_t t_start_sample_us = ggml_time_us();
@@ -1010,7 +1108,7 @@ int main(int argc, char ** argv) {
             --remaining_tokens;
         } else {
             // some user input remains from prompt or interaction, forward it to processing
-            while (embd_inp.size() > input_consumed) {
+            while ((int) embd_inp.size() > input_consumed) {
                 embd.push_back(embd_inp[input_consumed]);
                 last_n_tokens.erase(last_n_tokens.begin());
                 last_n_tokens.push_back(embd_inp[input_consumed]);
@@ -1035,11 +1133,16 @@ int main(int argc, char ** argv) {
 
         // in interactive mode, and not currently processing queued inputs;
         // check if we should prompt the user for more
-        if (params.interactive && embd_inp.size() <= input_consumed) {
+        if (params.interactive && (int) embd_inp.size() <= input_consumed) {
             // check for reverse prompt
-            for (auto antiprompt_inp : antipromptv_inp) {
-                if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
-                    // reverse prompt found
+            std::string last_output;
+            for (auto id : last_n_tokens) {
+                last_output += vocab.id_to_token[id].tok;
+            }
+
+            // Check if each of the reverse prompts appears at the end of the output.
+            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;
                     break;
                 }
@@ -1068,7 +1171,7 @@ int main(int argc, char ** argv) {
                 } while (another_line);
                 if (params.use_color) printf(ANSI_COLOR_RESET);
 
-                std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
+                std::vector<llama_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
                 embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
 
                 if (params.instruct) {
diff --git a/models/ggml-vocab.bin b/models/ggml-vocab.bin
new file mode 100644
index 000000000..aba94bd10
Binary files /dev/null and b/models/ggml-vocab.bin differ
diff --git a/quantize.cpp b/quantize.cpp
index 129518e2b..52b7ac9b3 100644
--- a/quantize.cpp
+++ b/quantize.cpp
@@ -43,7 +43,7 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
         return false;
     }
 
-    gpt_vocab vocab;
+    llama_vocab vocab;
 
     printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
 
@@ -63,12 +63,12 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
     {
         uint32_t magic;
         finp.read((char *) &magic, sizeof(magic));
-        if (magic == 0x67676d6c) {
+        if (magic == FILE_MAGIC_UNVERSIONED) {
             fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
                     __func__, fname_inp.c_str());
             return false;
         }
-        if (magic != 0x67676d66) {
+        if (magic != FILE_MAGIC) {
             fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
             return false;
         }
@@ -78,9 +78,9 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
         uint32_t format_version;
         finp.read((char *) &format_version, sizeof(format_version));
 
-        if (format_version != 1) {
-            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
-                    __func__, fname_inp.c_str(), format_version);
+        if (format_version != FILE_VERSION) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
+                    __func__, fname_inp.c_str(), format_version, FILE_VERSION);
             return false;
         }
 
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
new file mode 100644
index 000000000..a2c1e3fa2
--- /dev/null
+++ b/tests/CMakeLists.txt
@@ -0,0 +1,4 @@
+set(TEST_TARGET test-tokenizer-0)
+add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
+target_link_libraries(${TEST_TARGET} PRIVATE utils)
+add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
new file mode 100644
index 000000000..6bc49f281
--- /dev/null
+++ b/tests/test-tokenizer-0.cpp
@@ -0,0 +1,69 @@
+#include "utils.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+
+static const std::map<std::string, std::vector<llama_vocab::id>> k_tests = {
+    { "Hello World",        { 1,  10994,   2787, }, },
+    { " Hello World",       { 1,  15043,   2787, }, },
+    { " Hello World!",      { 1,  15043,   2787,  29991, }, },
+    { " this is 🦙.cpp",    { 1,    445,    338,  29871,    243,    162,    169,    156,  29889,   8223, }, },
+    { "w048 7tuijk dsdfhu", { 1,  29893,  29900,  29946,  29947,  29871,  29955,   9161,  13535,  18031,   2176,   6905, }, },
+    { "нещо на Български",  { 1,    821,   4851,    665,   1386,  29713,   1305, }, },
+};
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_vocab vocab;
+
+    if (!llama_vocab_load(fname, vocab)) {
+        fprintf(stderr, "%s : failed to load vocab from: '%s'\n", __func__, fname.c_str());
+        return 1;
+    }
+
+    const int n_vocab = vocab.id_to_token.size();
+
+    if (n_vocab != 32000) {
+        fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
+        return 2;
+    }
+
+    for (const auto & test_kv : k_tests) {
+        const auto res = llama_tokenize(vocab, test_kv.first, true);
+
+        bool correct = res.size() == test_kv.second.size();
+
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (res[i] != test_kv.second[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            return 3;
+        }
+    }
+
+    return 0;
+}
diff --git a/utils.cpp b/utils.cpp
index 5481b6380..b15c68ade 100644
--- a/utils.cpp
+++ b/utils.cpp
@@ -12,7 +12,7 @@
 
  #if defined(_MSC_VER) || defined(__MINGW32__)
  #include <malloc.h> // using malloc.h with MSC/MINGW
- #elif !defined(__FreeBSD__) && !defined(__NetBSD__)
+ #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
  #include <alloca.h>
  #endif
 
@@ -72,8 +72,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.use_color = true;
         } else if (arg == "-r" || arg == "--reverse-prompt") {
             params.antiprompt.push_back(argv[++i]);
+        } else if (arg == "--perplexity") {
+            params.perplexity = true;
         } else if (arg == "--ignore-eos") {
             params.ignore_eos = true;
+        } else if (arg == "--n_parts") {
+            params.n_parts = std::stoi(argv[++i]);
         } else if (arg == "-h" || arg == "--help") {
             gpt_print_usage(argc, argv, params);
             exit(0);
@@ -116,7 +120,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     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, "  --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");
     fprintf(stderr, "  -m FNAME, --model FNAME\n");
     fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
     fprintf(stderr, "\n");
@@ -240,61 +246,6 @@ std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
     return result;
 }
 
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
-    std::vector<std::string> words;
-
-    // first split the text into words
-    {
-        std::string str = text;
-        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
-
-        std::regex re(pat);
-        std::smatch m;
-
-        while (std::regex_search(str, m, re)) {
-            for (auto x : m) {
-                words.push_back(x);
-            }
-            str = m.suffix();
-        }
-    }
-
-    // find the longest tokens that form the words:
-    std::vector<gpt_vocab::id> tokens;
-    for (const auto & word : words) {
-        if (word.size() == 0) continue;
-
-        int i = 0;
-        int n = word.size();
-        while (i < n) {
-            int j = n;
-            while (j > i) {
-                auto it = vocab.token_to_id.find(word.substr(i, j-i));
-                if (it != vocab.token_to_id.end()) {
-                    tokens.push_back(it->second);
-                    i = j;
-                    break;
-                }
-                --j;
-            }
-            if (i == n) {
-                break;
-            }
-            if (j == i) {
-                auto sub = word.substr(i, 1);
-                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
-                    tokens.push_back(vocab.token_to_id.at(sub));
-                } else {
-                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
-                }
-                ++i;
-            }
-        }
-    }
-
-    return tokens;
-}
-
 static size_t utf8_len(char src) {
     const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
     uint8_t highbits = static_cast<uint8_t>(src) >> 4;
@@ -305,7 +256,8 @@ struct llama_sp_symbol {
     using index = int;
     index prev;
     index next;
-    std::string_view text;
+    const char * text;
+    size_t n;
 };
 
 struct llama_sp_bigram {
@@ -322,19 +274,23 @@ struct llama_sp_bigram {
     size_t size;
 };
 
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
 struct llama_tokenizer {
-    llama_tokenizer(const gpt_vocab & vocab): vocab_(vocab) {}
+    llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
 
-    void tokenize(std::string_view text, std::vector<gpt_vocab::id> & output) {
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         // split string into utf8 chars
         int index = 0;
-        while (!text.empty()) {
+        size_t offs = 0;
+        while (offs < text.size()) {
             llama_sp_symbol sym;
-            size_t char_len = std::min(text.size(), utf8_len(text.data()[0]));
-            sym.text = std::string_view(text.data(), char_len);
+            size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
+            sym.text = text.c_str() + offs;
+            sym.n = char_len;
+            offs += char_len;
             sym.prev = index - 1;
-            text.remove_prefix(char_len);
-            sym.next = text.empty() ? -1 : index + 1;
+            sym.next = offs == text.size() ? -1 : index + 1;
             index++;
             symbols_.emplace_back(std::move(sym));
         }
@@ -353,14 +309,16 @@ struct llama_tokenizer {
             auto & right_sym = symbols_[bigram.right];
 
             // if one of the symbols already got merged, skip it.
-            if (left_sym.text.empty() || right_sym.text.empty() ||
-                left_sym.text.size() + right_sym.text.size() != bigram.size) {
+            if (left_sym.n == 0 || right_sym.n == 0 ||
+                left_sym.n + right_sym.n != bigram.size) {
                 continue;
             }
 
             // merge the right sym into the left one
-            left_sym.text = std::string_view(left_sym.text.data(), left_sym.text.size() + right_sym.text.size());
-            right_sym.text = std::string_view("");
+            left_sym.n += right_sym.n;
+            right_sym.n = 0;
+
+            //printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
 
             // remove the right sym from the chain
             left_sym.next = right_sym.next;
@@ -374,13 +332,13 @@ struct llama_tokenizer {
         }
 
         for (int i = 0; i != -1; i = symbols_[i].next) {
-            auto& symbol = symbols_[i];
-            auto token = vocab_.token_to_id.find(std::string(symbol.text));
+            auto & symbol = symbols_[i];
+            auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
 
             if (token == vocab_.token_to_id.end()) {
                 // output any symbols that did not form tokens as bytes.
-                for (int j = 0; j < symbol.text.size(); ++j) {
-                    gpt_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
+                for (int j = 0; j < (int) symbol.n; ++j) {
+                    llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
                     output.push_back(token_id);
                 }
             } else {
@@ -395,8 +353,8 @@ private:
             return;
         }
 
-        std::string_view text(symbols_[left].text.data(), symbols_[left].text.size() + symbols_[right].text.size());
-        auto token = vocab_.token_to_id.find(std::string(text));
+        const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
+        auto token = vocab_.token_to_id.find(text);
 
         if (token == vocab_.token_to_id.end()) {
             return;
@@ -416,14 +374,56 @@ private:
         work_queue_.push(bigram);
     }
 
-    const gpt_vocab & vocab_;
+    const llama_vocab & vocab_;
     std::vector<llama_sp_symbol> symbols_;
     llama_sp_bigram::queue work_queue_;
 };
 
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos) {
+// TODO: temporary code duplication with llama.cpp
+//       will resolve after #77 is merged
+bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
+    std::ifstream fin(fname, std::ios::binary);
+    if (!fin.is_open()) {
+        return false;
+    }
+
+    int n_vocab = 0;
+    fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+    std::string word;
+    std::vector<char> tmp(64);
+
+    vocab.id_to_token.resize(n_vocab);
+
+    for (int i = 0; i < n_vocab; i++) {
+        uint32_t len;
+        fin.read((char *) &len, sizeof(len));
+
+        word.resize(len);
+        if (len > 0) {
+            tmp.resize(len);
+            fin.read(tmp.data(), len);
+            word.assign(tmp.data(), len);
+        } else {
+            word.clear();
+        }
+
+        float score;
+        fin.read((char *) &score, sizeof(score));
+
+        vocab.token_to_id[word] = i;
+
+        auto &tok_score = vocab.id_to_token[i];
+        tok_score.tok = word;
+        tok_score.score = score;
+    }
+
+    return true;
+}
+
+std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
     llama_tokenizer tokenizer(vocab);
-    std::vector<gpt_vocab::id> output;
+    std::vector<llama_vocab::id> output;
 
     if (text.size() == 0) {
         return output;
@@ -437,43 +437,22 @@ std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_v
     return output;
 }
 
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
-    printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
-
-    vocab.token_to_id = ::json_parse(fname);
-
-    vocab.id_to_token.resize(vocab.token_to_id.size());
-    for (const auto & kv : vocab.token_to_id) {
-        vocab.id_to_token[kv.second].tok = kv.first;
-    }
-
-    printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
-
-    // print the vocabulary
-    //for (auto kv : vocab.token_to_id) {
-    //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
-    //}
-
-    return true;
-}
-
-
-void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
+void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
     // find the top K tokens
     std::partial_sort(
             logits_id.begin(),
             logits_id.begin() + top_k, logits_id.end(),
-            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
+            [](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
         return a.first > b.first;
     });
 
     logits_id.resize(top_k);
 }
 
-gpt_vocab::id llama_sample_top_p_top_k(
-        const gpt_vocab & vocab,
+llama_vocab::id llama_sample_top_p_top_k(
+        const llama_vocab & vocab,
         const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
+        std::vector<llama_vocab::id> & last_n_tokens,
         double repeat_penalty,
         int top_k,
         double top_p,
@@ -481,7 +460,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
         std::mt19937 & rng) {
     int n_logits = vocab.id_to_token.size();
 
-    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
+    std::vector<std::pair<double, llama_vocab::id>> logits_id;
     logits_id.reserve(n_logits);
 
     {
@@ -624,7 +603,7 @@ size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t
 
     char * pdst = (char *) dst;
 
-    for (int j = 0; j < n; j += k) { 
+    for (int j = 0; j < n; j += k) {
         uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
         uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs +   sizeof(float));
         uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
@@ -647,7 +626,7 @@ size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t
 
                 *(float *) pd = d;
                 *(float *) pm = min;
-                pd += bs; 
+                pd += bs;
                 pm += bs;
 
                 for (int l = 0; l < qk; l += 2) {
diff --git a/utils.h b/utils.h
index f91688bea..312903859 100644
--- a/utils.h
+++ b/utils.h
@@ -13,33 +13,34 @@
 //
 
 struct gpt_params {
-    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 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_ctx = 512; //context size
-    bool memory_f16 = false; // use f16 instead of f32 for memory kv
+    int32_t n_parts       = -1;  // amount of model parts (-1 = determine from model dimensions)
+    int32_t n_ctx         = 512; //context size
 
     // sampling parameters
     int32_t top_k = 40;
     float   top_p = 0.95f;
     float   temp  = 0.80f;
-    float   repeat_penalty  = 1.30f;
+    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::string model  = "models/lamma-7B/ggml-model.bin"; // model path
+    std::string prompt = "";
 
-    bool random_prompt = false;
-
-    bool use_color = false; // use color to distinguish generations and inputs
-
-    bool interactive = false; // interactive mode
-    bool interactive_start = false; // reverse prompt immediately
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
-    bool instruct    = false; // instruction mode (used for Alpaca models)
-    bool ignore_eos = false; // do not stop generating after eos
+
+    bool memory_f16        = false; // 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 interactive_start = false; // reverse prompt 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 gpt_params_parse(int argc, char ** argv, gpt_params & params);
@@ -48,11 +49,19 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
 
 std::string gpt_random_prompt(std::mt19937 & rng);
 
+//
+// Model file parsing
+//
+
+#define FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
+#define FILE_MAGIC 0x67676d66 // 'ggmf' in hex
+#define FILE_VERSION 1
+
 //
 // Vocab utils
 //
 
-struct gpt_vocab {
+struct llama_vocab {
     using id    = int32_t;
     using token = std::string;
 
@@ -70,34 +79,22 @@ void replace(std::string & str, const std::string & needle, const std::string &
 // poor-man's JSON parsing
 std::unordered_map<std::string, int32_t> json_parse(const std::string & fname);
 
-// split text into tokens
-//
-// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
-//
-// Regex (Python):
-// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
-//
-// Regex (C++):
-// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
-//
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
+// TODO: temporary until #77 is merged, need this now for some tokenizer tests
+bool llama_vocab_load(const std::string & fname, llama_vocab & vocab);
 
 // TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
 // ref: https://github.com/google/sentencepiece
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, std::string_view text, bool bos);
-
-// load the tokens from encoder.json
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
+std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos);
 
 // sample next token given probabilities for each embedding
 //
 //   - consider only the top K tokens
 //   - from them, consider only the top tokens with cumulative probability > P
 //
-gpt_vocab::id llama_sample_top_p_top_k(
-        const gpt_vocab & vocab,
+llama_vocab::id llama_sample_top_p_top_k(
+        const llama_vocab & vocab,
         const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
+        std::vector<llama_vocab::id> & last_n_tokens,
         double repeat_penalty,
         int top_k,
         double top_p,
@@ -105,7 +102,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
         std::mt19937 & rng);
 
 // filer to top K tokens from list of logits
-void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k);
+void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);
 
 //
 // Quantization