Add link to Roadmap discussion

Revert "Fix memory allocation issues and seg faults"
This reverts commit 4870e455b3. Will provide the correct fix later
27 changed files with 3075 additions and 1956 deletions
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@ -16,11 +16,7 @@ elif [[ $arg1 == '--quantize' || $arg1 == '-q' ]]; then
    ./quantize $arg2
 elif [[ $arg1 == '--run' || $arg1 == '-r' ]]; then
    ./main $arg2
-elif [[ $arg1 == '--download' || $arg1 == '-d' ]]; then
-    python3 ./download-pth.py $arg2
 elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
-    echo "Downloading model..."
-    python3 ./download-pth.py "$1" "$2"
    echo "Converting PTH to GGML..."
    for i in `ls $1/$2/ggml-model-f16.bin*`; do
        if [ -f "${i/f16/q4_0}" ]; then
@ -39,8 +35,6 @@ else
    echo "              ex: \"/models/7B/\" 1"
    echo "  --quantize (-q): Optimize with quantization process ggml"
    echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
-    echo "  --download (-d): Download original llama model from CDN: https://agi.gpt4.org/llama/"
-    echo "              ex: \"/models/\" 7B"
-    echo "  --all-in-one (-a): Execute --download, --convert & --quantize"
+    echo "  --all-in-one (-a): Execute --convert & --quantize"
    echo "              ex: \"/models/\" 7B"
 fi
--- a/.github/ISSUE_TEMPLATE/custom.md
+++ b/.github/ISSUE_TEMPLATE/custom.md
@ -0,0 +1,185 @@
+---
+name: Issue and enhancement template
+about: Used to report issues and request enhancements for llama.cpp
+title: "[User] Insert summary of your issue or enhancement.."
+labels: ''
+assignees: ''
+
+---
+
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
+- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
+- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
+- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
+
+# Expected Behavior
+
+Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do.
+
+# Current Behavior
+
+Please provide a detailed written description of what `llama.cpp` did, instead. 
+
+# Environment and Context 
+
+Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
+
+* Physical (or virtual) hardware you are using, e.g. for Linux:
+
+`$ lscpu`
+
+* Operating System, e.g. for Linux:
+
+`$ uname -a`
+
+* SDK version, e.g. for Linux:
+
+```
+$ python3 --version
+$ make --version
+$ g++ --version
+```
+
+# Failure Information (for bugs)
+
+Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
+
+# Steps to Reproduce
+
+Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.
+
+1. step 1
+2. step 2
+3. step 3
+4. etc.
+
+# Failure Logs
+
+Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
+
+Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.
+
+Example environment info:
+```
+llama.cpp$ git log | head -1
+commit 2af23d30434a677c6416812eea52ccc0af65119c
+
+llama.cpp$ lscpu | egrep "AMD|Flags"
+Vendor ID:                       AuthenticAMD
+Model name:                      AMD Ryzen Threadripper 1950X 16-Core Processor
+Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
+Virtualization:                  AMD-V
+
+llama.cpp$ python3 --version
+Python 3.10.9
+
+llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
+numpy                         1.24.2
+numpydoc                      1.5.0
+sentencepiece                 0.1.97
+torch                         1.13.1
+torchvision                   0.14.1
+
+llama.cpp$ make --version | head -1
+GNU Make 4.3
+
+$ md5sum ./models/65B/ggml-model-q4_0.bin
+dbdd682cce80e2d6e93cefc7449df487  ./models/65B/ggml-model-q4_0.bin
+```
+
+Example run with the Linux command [perf](https://www.brendangregg.com/perf.html)
+```
+llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
+main: seed = 1679149377
+llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
+llama_model_load: n_vocab = 32000
+llama_model_load: n_ctx   = 512
+llama_model_load: n_embd  = 8192
+llama_model_load: n_mult  = 256
+llama_model_load: n_head  = 64
+llama_model_load: n_layer = 80
+llama_model_load: n_rot   = 128
+llama_model_load: f16     = 2
+llama_model_load: n_ff    = 22016
+llama_model_load: n_parts = 8
+llama_model_load: ggml ctx size = 41477.73 MB
+llama_model_load: memory_size =  2560.00 MB, n_mem = 40960
+llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
+llama_model_load: .......................................................................................... done
+llama_model_load: model size =  4869.09 MB / num tensors = 723
+
+system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | 
+
+main: prompt: 'Please close your issue when it has been answered.'
+main: number of tokens in prompt = 11
+     1 -> ''
+ 12148 -> 'Please'
+  3802 -> ' close'
+   596 -> ' your'
+  2228 -> ' issue'
+   746 -> ' when'
+   372 -> ' it'
+   756 -> ' has'
+  1063 -> ' been'
+  7699 -> ' answered'
+ 29889 -> '.'
+
+sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
+
+
+Please close your issue when it has been answered.
+@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
+I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
+@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
+
+
+main: mem per token = 71159620 bytes
+main:     load time = 19309.95 ms
+main:   sample time =   168.62 ms
+main:  predict time = 223895.61 ms / 888.47 ms per token
+main:    total time = 246406.42 ms
+
+ Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
+
+        3636882.89 msec task-clock                #   14.677 CPUs utilized          
+             13509      context-switches          #    3.714 /sec                   
+              2436      cpu-migrations            #    0.670 /sec                   
+          10476679      page-faults               #    2.881 K/sec                  
+    13133115082869      cycles                    #    3.611 GHz                      (16.77%)
+       29314462753      stalled-cycles-frontend   #    0.22% frontend cycles idle     (16.76%)
+    10294402631459      stalled-cycles-backend    #   78.39% backend cycles idle      (16.74%)
+    23479217109614      instructions              #    1.79  insn per cycle         
+                                                  #    0.44  stalled cycles per insn  (16.76%)
+     2353072268027      branches                  #  647.002 M/sec                    (16.77%)
+        1998682780      branch-misses             #    0.08% of all branches          (16.76%)
+
+     247.802177522 seconds time elapsed
+
+    3618.573072000 seconds user
+      18.491698000 seconds sys
+```
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@ -41,20 +41,29 @@ jobs:

    steps:
      - name: Clone
+        id: checkout
        uses: actions/checkout@v1

      - name: Dependencies
+        id: depends
        run: |
          sudo apt-get update
          sudo apt-get install build-essential

      - name: Build
+        id: cmake_build
        run: |
          mkdir build
          cd build
          cmake ..
          cmake --build . --config Release

+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest --output-on-failure
+
  macOS-latest-make:
    runs-on: macos-latest

@ -78,19 +87,28 @@ jobs:

    steps:
      - name: Clone
+        id: checkout
        uses: actions/checkout@v1

      - name: Dependencies
+        id: depends
        run: |
          brew update

      - name: Build
+        id: cmake_build
        run: |
          mkdir build
          cd build
-          cmake ..
+          cmake -DLLAMA_AVX2=OFF ..
          cmake --build . --config Release

+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest --output-on-failure
+
  windows-latest-cmake:
    runs-on: windows-latest

@ -107,6 +125,12 @@ jobs:
          cmake ..
          cmake --build . --config Release

+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -C Release --output-on-failure
+
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@ -116,7 +140,7 @@ jobs:
        id: pack_artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        run: |
-          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-x64.zip .\build\Release\*
+          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-x64.zip .\build\bin\Release\*

      - name: Create release
        id: create_release
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@ -1,11 +1,37 @@
-cmake_minimum_required(VERSION 3.12)
+cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
 project("llama.cpp" C CXX)

+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()

+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+
+if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+    set(LLAMA_STANDALONE ON)
+
+    # 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
 #
@ -34,6 +60,9 @@ option(LLAMA_FMA                    "llama: enable FMA"
 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
 #
@ -178,31 +207,63 @@ else()
    message(STATUS "Unknown architecture")
 endif()

-
 #
-# Build library
+# Build libraries
 #

-add_executable(llama main.cpp)
+add_library(utils OBJECT
+            utils.cpp
+            utils.h)

-add_executable(quantize quantize.cpp)
+target_include_directories(utils PUBLIC .)
+target_compile_features(utils PUBLIC cxx_std_11) # don't bump
+target_link_libraries(utils PRIVATE ${LLAMA_EXTRA_LIBS})
+if (BUILD_SHARED_LIBS)
+    set_target_properties(utils PROPERTIES POSITION_INDEPENDENT_CODE ON)
+endif()

 add_library(ggml OBJECT
            ggml.c
            ggml.h)

-add_library(utils OBJECT
-            utils.cpp
-            utils.h)
-
 target_include_directories(ggml PUBLIC .)
-target_compile_features(ggml PUBLIC c_std_11)
-target_compile_features(utils PUBLIC cxx_std_17)
+target_compile_features(ggml PUBLIC c_std_11) # don't bump
+target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
+if (BUILD_SHARED_LIBS)
+    set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
+endif()
+
+add_library(llama
+            llama.cpp
+            llama.h)
+
+target_include_directories(llama PUBLIC .)
+target_compile_features(llama PUBLIC cxx_std_11) # don't bump
+target_link_libraries(llama PRIVATE utils ggml ${LLAMA_EXTRA_LIBS})
+if (BUILD_SHARED_LIBS)
+    set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
+endif()

 #
-# Linking
+# Executables
 #

-target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
-target_link_libraries(llama PRIVATE ggml utils)
-target_link_libraries(quantize PRIVATE ggml utils)
+add_executable(main main.cpp)
+target_link_libraries(main PRIVATE llama ggml utils)
+
+add_executable(quantize quantize.cpp)
+target_link_libraries(quantize PRIVATE llama 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()
--- a/78
+++ b/78
@ -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
@ -185,18 +220,23 @@ default: main quantize
 ggml.o: ggml.c ggml.h
 	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o

+llama.o: llama.cpp llama.h
+	$(CXX) $(CXXFLAGS) -c llama.cpp -o llama.o
+
 utils.o: utils.cpp utils.h
 	$(CXX) $(CXXFLAGS) -c utils.cpp -o utils.o

 clean:
 	rm -f *.o main quantize

-main: main.cpp ggml.o utils.o
-	$(CXX) $(CXXFLAGS) main.cpp ggml.o utils.o -o main $(LDFLAGS)
-	./main -h
+main: main.cpp ggml.o llama.o utils.o
+	$(CXX) $(CXXFLAGS) main.cpp ggml.o llama.o utils.o -o main $(LDFLAGS)
+	@echo
+	@echo '====  Run ./main -h for help.  ===='
+	@echo

-quantize: quantize.cpp ggml.o utils.o
-	$(CXX) $(CXXFLAGS) quantize.cpp ggml.o utils.o -o quantize $(LDFLAGS)
+quantize: quantize.cpp ggml.o llama.o utils.o
+	$(CXX) $(CXXFLAGS) quantize.cpp ggml.o llama.o utils.o -o quantize $(LDFLAGS)

 #
 # Tests
--- a/README.md
+++ b/README.md
@ -7,13 +7,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++

 **Hot topics:**

- [Added Alpaca support](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
+- [Roadmap (short-term)](https://github.com/ggerganov/llama.cpp/discussions/457)
+- New C-style API is now available: https://github.com/ggerganov/llama.cpp/pull/370
 - Cache input prompts for faster initialization: https://github.com/ggerganov/llama.cpp/issues/64
 - Create a `llama.cpp` logo: https://github.com/ggerganov/llama.cpp/issues/105

-**TEMPORARY NOTICE:**
-If you're updating to the latest master, you will need to regenerate your model files as the format has changed.
-
 ## Description

 The main goal is to run the model using 4-bit quantization on a MacBook
@ -178,28 +176,23 @@ 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)

 ### Instruction mode with Alpaca

-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
-```
-
-Now run the `main` tool like this:
+1. First, download the `ggml` Alpaca model into the `./models` folder
+2. Run the `main` tool like this:

 ```
 ./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins
@ -219,11 +212,67 @@ 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.
 > 
 ```

+### Obtaining and verifying the Facebook LLaMA original model and Stanford Alpaca model data
+
+* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [pull request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
+* Please verify the sha256 checksums of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
+* The following command will verify if you have all possible latest files in your self-installed `./models` subdirectory:
+
+  `sha256sum --ignore-missing -c SHA256SUMS` on Linux
+
+  or
+
+  `shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS
+
+* If your issue is with model generation quality then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
+  * LLaMA:
+    * [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
+    * [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
+  * GPT-3
+    * [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
+  * GPT-3.5 / InstructGPT / ChatGPT:
+    * [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
+    * [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
+    
+### Perplexity (Measuring model quality)
+
+You can pass `--perplexity` as a command line option to measure perplexity over the given prompt.  For more background,
+see https://huggingface.co/docs/transformers/perplexity.  However, in general, lower perplexity is better for LLMs.
+
+#### Latest measurements
+
+The latest perplexity scores for the various model sizes and quantizations are being tracked in [discussion #406](https://github.com/ggerganov/llama.cpp/discussions/406).  `llama.cpp` is measuring very well
+compared to the baseline implementations.  Quantization has a small negative impact to quality, but, as you can see, running
+13B at q4_0 beats the 7B f16 model by a significant amount.
+
+All measurements are done against wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with default options (512 length context).
+Note that the changing the context length will have a significant impact on perplexity (longer context = better perplexity).
+```
+Perplexity - model options
+5.5985 - 13B, q4_0
+5.9565 - 7B, f16
+6.3001 - 7B, q4_1
+6.5949 - 7B, q4_0
+6.5995 - 7B, q4_0, --memory_f16
+```
+
+#### How to run
+
+1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+2. Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+3. Output:
+```
+Calculating perplexity over 655 chunks
+24.43 seconds per pass - ETA 4.45 hours
+[1]4.5970,[2]5.1807,[3]6.0382,...
+```
+And after 4.45 hours, you will have the final perplexity.
+
 ### Android

 You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux).
@ -274,7 +323,6 @@ docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models

 ## Limitations

- We don't know yet how much the quantization affects the quality of the generated text
 - Probably the token sampling can be improved
 - The Accelerate framework is actually currently unused since I found that for tensor shapes typical for the Decoder,
  there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simply don't
@ -288,6 +336,7 @@ docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models
 - Collaborators will be invited based on contributions
 - Any help with managing issues and PRs is very appreciated!
 - Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
+- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)

 ### Coding guidelines

@ -297,3 +346,4 @@ docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models
 - There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
 - Clean-up any trailing whitespaces, use 4 spaces indentation, brackets on same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
+
--- a/20
+++ b/20
@ -0,0 +1,20 @@
+700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d  models/7B/consolidated.00.pth
+7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265  models/7B/params.json
+745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08  models/13B/consolidated.00.pth
+d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085  models/13B/consolidated.01.pth
+4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f  models/13B/params.json
+e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067  models/30B/consolidated.00.pth
+4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff  models/30B/consolidated.01.pth
+24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378  models/30B/consolidated.02.pth
+1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b  models/30B/consolidated.03.pth
+2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb  models/30B/params.json
+135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe  models/65B/consolidated.00.pth
+9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde  models/65B/consolidated.01.pth
+e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770  models/65B/consolidated.02.pth
+73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e  models/65B/consolidated.03.pth
+882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225  models/65B/consolidated.04.pth
+a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78  models/65B/consolidated.05.pth
+72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b  models/65B/consolidated.06.pth
+d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638  models/65B/consolidated.07.pth
+999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b  models/65B/params.json
+9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347  models/tokenizer.model
--- 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
--- a/chat.sh
+++ 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
--- a/convert-gptq-to-ggml.py
+++ b/convert-gptq-to-ggml.py
@ -0,0 +1,167 @@
+# 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", 0x67676d66)) # magic: ggmf in hex
+fout.write(struct.pack("i", 1)) # file version
+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):
+        text = " \u2047 ".encode("utf-8")
+    elif tokenizer.is_control(i):
+        text = b""
+    elif tokenizer.is_byte(i):
+        piece = tokenizer.id_to_piece(i)
+        if len(piece) != 6:
+            print(f"Invalid token: {piece}")
+            sys.exit(1)
+        byte_value = int(piece[3:-1], 16)
+        text = struct.pack("B", byte_value)
+    else:
+        text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
+    fout.write(struct.pack("f", tokenizer.get_score(i)))
+
+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("")
--- a/convert-pth-to-ggml.py
+++ b/convert-pth-to-ggml.py
@ -10,12 +10,10 @@
 #   - 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
@ -23,13 +21,15 @@ 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):
@ -67,7 +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)
@ -134,6 +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:
+            write_header(fout, hparams, ftype)
+            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):
@ -151,6 +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__":
--- a/download-pth.py
+++ b/download-pth.py
@ -1,66 +0,0 @@
-import os
-import sys
-from tqdm import tqdm
-import requests
-
-if len(sys.argv) < 3:
-    print("Usage: download-pth.py dir-model model-type\n")
-    print("  model-type: Available models 7B, 13B, 30B or 65B")
-    sys.exit(1)
-
-modelsDir = sys.argv[1]
-model = sys.argv[2]
-
-num = {
-    "7B": 1,
-    "13B": 2,
-    "30B": 4,
-    "65B": 8,
-}
-
-if model not in num:
-    print(f"Error: model {model} is not valid, provide 7B, 13B, 30B or 65B")
-    sys.exit(1)
-
-print(f"Downloading model {model}")
-
-files = ["checklist.chk", "params.json"]
-
-for i in range(num[model]):
-    files.append(f"consolidated.0{i}.pth")
-
-resolved_path = os.path.abspath(os.path.join(modelsDir, model))
-os.makedirs(resolved_path, exist_ok=True)
-
-for file in files:
-    dest_path = os.path.join(resolved_path, file)
-    
-    if os.path.exists(dest_path):
-        print(f"Skip file download, it already exists: {file}")
-        continue
-
-    url = f"https://agi.gpt4.org/llama/LLaMA/{model}/{file}"
-    response = requests.get(url, stream=True)
-    with open(dest_path, 'wb') as f:
-        with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
-            for chunk in response.iter_content(chunk_size=1024):
-                if chunk:
-                    f.write(chunk)
-                    t.update(len(chunk))
-
-files2 = ["tokenizer_checklist.chk", "tokenizer.model"]
-for file in files2:
-    dest_path = os.path.join(modelsDir, file)
-    
-    if os.path.exists(dest_path):
-        print(f"Skip file download, it already exists: {file}")
-        continue
-    
-    url = f"https://agi.gpt4.org/llama/LLaMA/{file}"
-    response = requests.get(url, stream=True)
-    with open(dest_path, 'wb') as f:
-        with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
-            for chunk in response.iter_content(chunk_size=1024):
-                if chunk:
-                    f.write(chunk)
-                    t.update(len(chunk))
--- a/examples/chatLLaMa
+++ 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 ${AI_NAME} say aloud to each other.
+The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
+The transcript only includes text, it does not include markup like HTML and Markdown.
+
+$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:" "$@"
--- a/flake.nix
+++ b/flake.nix
@ -28,8 +28,8 @@
          ];
          installPhase = ''
            mkdir -p $out/bin
-            mv llama $out/bin/llama
-            mv quantize $out/bin/quantize
+            mv bin/main $out/bin/llama
+            mv bin/quantize $out/bin/quantize
            echo "#!${llama-python}/bin/python" > $out/bin/convert-pth-to-ggml
            cat ${./convert-pth-to-ggml.py} >> $out/bin/convert-pth-to-ggml
            chmod +x $out/bin/convert-pth-to-ggml
--- a/ggml.c
+++ b/ggml.c
@ -1,8 +1,11 @@
+// Defines CLOCK_MONOTONIC on Linux
+#define _POSIX_C_SOURCE 199309L
+
 #include "ggml.h"

 #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 +364,7 @@ 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 )
@ -397,13 +400,58 @@ 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)
+
+// reference implementation for deterministic creation of model files
+static void quantize_row_q4_0_reference(const float * restrict x, void * restrict y, int k) {
+    assert(k % QK == 0);
+    const int nb = k / QK;
+
+    const size_t bs = sizeof(float) + QK/2;
+
+    uint8_t * restrict pd = ((uint8_t *)y + 0*bs);
+    uint8_t * restrict pb = ((uint8_t *)y + 0*bs + sizeof(float));
+
+    uint8_t pp[QK/2];
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int l = 0; l < QK; l++) {
+            const float v = x[i*QK + l];
+            amax = MAX(amax, fabsf(v));
+        }
+
+        const float d = amax / ((1 << 3) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        *(float *)pd = d;
+        pd += bs;
+
+        for (int l = 0; l < QK; l += 2) {
+            const float v0 = x[i*QK + l + 0]*id;
+            const float v1 = x[i*QK + l + 1]*id;
+
+            const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
+            const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
+
+            assert(vi0 >= 0 && vi0 < 16);
+            assert(vi1 >= 0 && vi1 < 16);
+
+            pp[l/2] = vi0 | (vi1 << 4);
+        }
+
+        memcpy(pb, pp, sizeof(pp));
+        pb += bs;
+    }
+}
+
 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__)
    const int nb = k / QK;
    const size_t bs = sizeof(float) + QK/2;

@ -411,6 +459,7 @@ void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
    uint8_t * restrict pb = ((uint8_t *)y + 0*bs + sizeof(float));

    uint8_t pp[QK/2];
+#endif

 #if __ARM_NEON
 #if QK == 32
@ -567,36 +616,7 @@ void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
 #endif
 #else
    // scalar
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-
-        for (int l = 0; l < QK; l++) {
-            const float v = x[i*QK + l];
-            amax = MAX(amax, fabsf(v));
-        }
-
-        const float d = amax / ((1 << 3) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        *(float *)pd = d;
-        pd += bs;
-
-        for (int l = 0; l < QK; l += 2) {
-            const float v0 = x[i*QK + l + 0]*id;
-            const float v1 = x[i*QK + l + 1]*id;
-
-            const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
-            const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
-
-            assert(vi0 >= 0 && vi0 < 16);
-            assert(vi1 >= 0 && vi1 < 16);
-
-            pp[l/2] = vi0 | (vi1 << 4);
-        }
-
-        memcpy(pb, pp, sizeof(pp));
-        pb += bs;
-    }
+    quantize_row_q4_0_reference(x, y, k);
 #endif
 }

@ -1262,6 +1282,47 @@ 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 +1478,40 @@ 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 +2023,7 @@ 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++) {
@ -10628,6 +10723,68 @@ enum ggml_opt_result ggml_opt(

 ////////////////////////////////////////////////////////////////////////////////

+size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int qk, int64_t * hist) {
+    const int nb = k / qk;
+    const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
+    const size_t row_size = nb*bs;
+
+    assert(k % qk == 0);
+
+    char * pdst = (char *) dst;
+
+    for (int j = 0; j < n; j += k) {
+        uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
+        uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
+
+        quantize_row_q4_0_reference(src + j, pd, k);
+
+        for (int i = 0; i < nb; i++) {
+            for (int l = 0; l < qk; l += 2) {
+                const uint8_t vi0 = pb[l/2] & 0xF;
+                const uint8_t vi1 = pb[l/2] >> 4;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+            pb += bs;
+        }
+    }
+
+    return (n/k)*row_size;
+}
+
+size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int qk, int64_t * hist) {
+    const int nb = k / qk;
+    const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
+    const size_t row_size = nb*bs;
+
+    assert(k % qk == 0);
+
+    char * pdst = (char *) dst;
+
+    for (int j = 0; j < n; j += k) {
+        uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
+        uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
+
+        quantize_row_q4_1(src + j, pd, k);
+
+        for (int i = 0; i < nb; i++) {
+            for (int l = 0; l < qk; l += 2) {
+                const uint8_t vi0 = pb[l/2] & 0xF;
+                const uint8_t vi1 = pb[l/2] >> 4;
+
+                hist[vi0]++;
+                hist[vi1]++;
+            }
+            pb += bs;
+        }
+    }
+
+    return (n/k)*row_size;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
 int ggml_cpu_has_avx(void) {
 #if defined(__AVX__)
    return 1;
--- a/ggml.h
+++ b/ggml.h
@ -741,6 +741,13 @@ enum ggml_opt_result ggml_opt(
        struct ggml_opt_params params,
        struct ggml_tensor * f);

+//
+// quantization
+//
+
+size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
+size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
+
 //
 // system info
 //
--- a/llama.cpp
+++ b/llama.cpp
--- a/llama.h
+++ b/llama.h
@ -0,0 +1,139 @@
+#ifndef LLAMA_H
+#define LLAMA_H
+
+#include <stddef.h>
+#include <stdint.h>
+#include <stdbool.h>
+
+#ifdef LLAMA_SHARED
+#    ifdef _WIN32
+#        ifdef LLAMA_BUILD
+#            define LLAMA_API __declspec(dllexport)
+#        else
+#            define LLAMA_API __declspec(dllimport)
+#        endif
+#    else
+#        define LLAMA_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define LLAMA_API
+#endif
+
+#define LLAMA_FILE_VERSION 1
+#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
+#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    //
+    // C interface
+    //
+    // TODO: show sample usage
+    //
+
+    struct llama_context;
+
+    typedef int llama_token;
+
+    typedef struct llama_token_data {
+        llama_token id;  // token id
+
+        float p;     // probability of the token
+        float plog;  // log probability of the token
+
+    } llama_token_data;
+
+    struct llama_context_params {
+        int n_ctx;   // text context
+        int n_parts; // -1 for default
+        int seed;    // RNG seed, 0 for random
+
+        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
+    };
+
+    LLAMA_API struct llama_context_params llama_context_default_params();
+
+    // Various functions for loading a ggml llama model.
+    // Allocate (almost) all memory needed for the model.
+    // Return NULL on failure
+    LLAMA_API struct llama_context * llama_init_from_file(
+                             const char * path_model,
+            struct llama_context_params   params);
+
+    // Frees all allocated memory
+    LLAMA_API void llama_free(struct llama_context * ctx);
+
+    // TODO: not great API - very likely to change
+    // Returns 0 on success
+    LLAMA_API int llama_model_quantize(
+            const char * fname_inp,
+            const char * fname_out,
+                   int   itype,
+                   int   qk);
+
+    // Run the llama inference to obtain the logits and probabilities for the next token.
+    // tokens + n_tokens is the provided batch of new tokens to process
+    // n_past is the number of tokens to use from previous eval calls
+    // Returns 0 on success
+    LLAMA_API int llama_eval(
+            struct llama_context * ctx,
+               const llama_token * tokens,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads);
+
+    // Convert the provided text into tokens.
+    // The tokens pointer must be large enough to hold the resulting tokens.
+    // Returns the number of tokens on success, no more than n_max_tokens
+    // Returns a negative number on failure - the number of tokens that would have been returned
+    // TODO: not sure if correct
+    LLAMA_API int llama_tokenize(
+            struct llama_context * ctx,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    LLAMA_API int llama_n_vocab(struct llama_context * ctx);
+    LLAMA_API int llama_n_ctx  (struct llama_context * ctx);
+
+    // Token logits obtained from the last call to llama_eval()
+    // The logits for the last token are stored in the last row
+    // Can be mutated in order to change the probabilities of the next token
+    // Rows: n_tokens
+    // Cols: n_vocab
+    LLAMA_API float * llama_get_logits(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);
+
+    // Special tokens
+    LLAMA_API llama_token llama_token_bos();
+    LLAMA_API llama_token llama_token_eos();
+
+    // TODO: improve the last_n_tokens interface ?
+    LLAMA_API llama_token llama_sample_top_p_top_k(
+              llama_context * ctx,
+          const llama_token * last_n_tokens_data,
+                        int   last_n_tokens_size,
+                        int   top_k,
+                     double   top_p,
+                     double   temp,
+                     double   repeat_penalty);
+
+    // Performance information
+    LLAMA_API void llama_print_timings(struct llama_context * ctx);
+    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
+
+    // Print system information
+    LLAMA_API const char * llama_print_system_info(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
--- a/main.cpp
+++ b/main.cpp
--- a/models/ggml-vocab.bin
+++ b/models/ggml-vocab.bin
--- a/quantize.cpp
+++ b/quantize.cpp
@ -1,317 +1,17 @@
 #include "ggml.h"
+#include "llama.h"

-#include "utils.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
 #include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
 #include <string>
-#include <vector>
-#include <regex>
-
-// TODO: move somewhere else
-#define QK 32
-
-// default hparams (LLaMA76B)
-struct llama_hparams {
-    int32_t n_vocab = 32000;
-    int32_t n_ctx   = 512;   // this is provided as user input?
-    int32_t n_embd  = 4096;
-    int32_t n_mult  = 256;
-    int32_t n_head  = 32;
-    int32_t n_layer = 32;
-    int32_t n_rot   = 64;
-    int32_t f16     = 1;
-};
-
-
-// quantize a model
-bool llama_model_quantize(const std::string & fname_inp, const std::string & fname_out, int itype) {
-    ggml_type type = GGML_TYPE_Q4_1;
-
-    switch (itype) {
-        case 2: type = GGML_TYPE_Q4_0; break;
-        case 3: type = GGML_TYPE_Q4_1; break;
-        default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1;
-    };
-
-    if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) {
-        fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type);
-        return false;
-    }
-
-    gpt_vocab vocab;
-
-    printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
-
-    auto finp = std::ifstream(fname_inp, std::ios::binary);
-    if (!finp) {
-        fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
-        return false;
-    }
-
-    auto fout = std::ofstream(fname_out, std::ios::binary);
-    if (!fout) {
-        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
-        return false;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        finp.read((char *) &magic, sizeof(magic));
-        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 != FILE_MAGIC) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
-            return false;
-        }
-
-        fout.write((char *) &magic, sizeof(magic));

-        uint32_t format_version;
-        finp.read((char *) &format_version, sizeof(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;
-        }
-
-        fout.write((char *) &format_version, sizeof(format_version));
-    }
-
-    llama_hparams hparams;
-
-    // load hparams
-    {
-        finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        //finp.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        finp.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        finp.read((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
-        finp.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        finp.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
-        finp.read((char *) &hparams.f16,     sizeof(hparams.f16));
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_mult  = %d\n", __func__, hparams.n_mult);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: f16     = %d\n", __func__, hparams.f16);
-
-        fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        //fout.write((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fout.write((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fout.write((char *) &hparams.n_mult,  sizeof(hparams.n_mult));
-        fout.write((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fout.write((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
-        fout.write((char *) &itype,           sizeof(hparams.f16));
-    }
-
-    // load vocab
-    {
-        const int32_t n_vocab = hparams.n_vocab;
-
-        if (n_vocab != hparams.n_vocab) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-                    __func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
-            return false;
-        }
-
-        std::string word;
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            finp.read ((char *) &len, sizeof(len));
-            fout.write((char *) &len, sizeof(len));
-
-            word.resize(len);
-            finp.read ((char *) word.data(), len);
-            fout.write((char *) word.data(), len);
-
-            float score;
-            finp.read ((char *) &score, sizeof(score));
-            fout.write((char *) &score, sizeof(score));
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-            vocab.score[i] = score;
-        }
-    }
-
-    // load weights
-    {
-        size_t total_size_org = 0;
-        size_t total_size_new = 0;
-
-        std::vector<float> work;
-
-        std::vector<uint8_t>     data_u8;
-        std::vector<ggml_fp16_t> data_f16;
-        std::vector<float>       data_f32;
-
-        std::vector<int64_t> hist_all(1 << 4, 0);
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ftype;
-
-            finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            finp.read(reinterpret_cast<char *>(&length), sizeof(length));
-            finp.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
-
-            if (finp.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            finp.read (&name[0], length);
-
-            {
-                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
-                printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
-            }
-
-            // regexes of tensor names to be quantized
-            const std::vector<std::string> k_names = {
-                ".*weight",
-            };
-
-            bool quantize = false;
-            for (const auto & s : k_names) {
-                if (std::regex_match(name, std::regex(s))) {
-                    quantize = true;
-                    break;
-                }
-            }
-
-            // quantize only 2D tensors
-            quantize &= (n_dims == 2);
-
-            if (quantize) {
-                if (ftype != 0 && ftype != 1) {
-                    fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype);
-                    return false;
-                }
-
-                if (ftype == 1) {
-                    data_f16.resize(nelements);
-                    finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
-                    data_f32.resize(nelements);
-                    for (int i = 0; i < nelements; ++i) {
-                        data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
-                    }
-                } else {
-                    data_f32.resize(nelements);
-                    finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
-                }
-
-                ftype = itype;
-            } else {
-                const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t);
-
-                data_u8.resize(nelements*bpe);
-                finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
-            }
-
-            fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fout.write(reinterpret_cast<char *>(&length), sizeof(length));
-            fout.write(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
-            for (int i = 0; i < n_dims; ++i) {
-                fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-            }
-            fout.write(&name[0], length);
-
-            if (quantize) {
-                printf("quantizing .. ");
-                work.resize(nelements); // for quantization
-
-                size_t cur_size = 0;
-                std::vector<int64_t> hist_cur(1 << 4, 0);
-
-                switch (type) {
-                    case GGML_TYPE_Q4_0:
-                        {
-                            cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], QK, hist_cur.data());
-                        } break;
-                    case GGML_TYPE_Q4_1:
-                        {
-                            cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], QK, hist_cur.data());
-                        } break;
-                    default:
-                        {
-                            fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type);
-                            return false;
-                        }
-                }
-
-                fout.write(reinterpret_cast<char *>(work.data()), cur_size);
-                total_size_new += cur_size;
-
-                printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
-                for (int i = 0; i < hist_cur.size(); ++i) {
-                    hist_all[i] += hist_cur[i];
-                }
-
-                for (int i = 0; i < hist_cur.size(); ++i) {
-                    printf("%5.3f ", hist_cur[i] / (float)nelements);
-                }
-                printf("\n");
-            } else {
-                printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
-                fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
-                total_size_new += data_u8.size();
-            }
-
-            total_size_org += nelements * sizeof(float);
-        }
-
-        printf("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
-        printf("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
-
-        {
-            int64_t sum_all = 0;
-            for (int i = 0; i < hist_all.size(); ++i) {
-                sum_all += hist_all[i];
-            }
-
-            printf("%s: hist: ", __func__);
-            for (int i = 0; i < hist_all.size(); ++i) {
-                printf("%5.3f ", hist_all[i] / (float)sum_all);
-            }
-            printf("\n");
-        }
-    }
-
-    finp.close();
-    fout.close();
-
-    return true;
-}
+const int QK = 32;

 // usage:
 //  ./llama-quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
 //
 int main(int argc, char ** argv) {
    ggml_time_init();
+
    if (argc != 4) {
        fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
        fprintf(stderr, "  type = 2 - q4_0\n");
@ -339,7 +39,7 @@ int main(int argc, char ** argv) {
    {
        const int64_t t_start_us = ggml_time_us();

-        if (!llama_model_quantize(fname_inp, fname_out, itype)) {
+        if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype, QK)) {
            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
            return 1;
        }
--- a/quantize.py
+++ b/quantize.py
@ -57,6 +57,7 @@ def main():
    # )

    args = parser.parse_args()
+    args.models_path = os.path.abspath(args.models_path)

    if not os.path.isfile(args.quantize_script_path):
        print(
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@ -0,0 +1,9 @@
+function(llama_add_test source)
+    get_filename_component(TEST_TARGET ${source} NAME_WE)
+    add_executable(${TEST_TARGET} ${source})
+    target_link_libraries(${TEST_TARGET} PRIVATE llama ggml utils)
+    add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
+endfunction()
+
+llama_add_test(test-quantize.c)
+llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
--- a/tests/test-quantize.c
+++ b/tests/test-quantize.c
@ -0,0 +1,42 @@
+#include "ggml.h"
+#undef NDEBUG
+#include <assert.h>
+#include <math.h>
+
+int main(void) {
+    #define QK 32
+    float src[QK];
+    uint8_t dst[24];
+    int64_t hist[16];
+
+    for (int i = 0; i < QK; i++) {
+        src[i] = (float)(i + 1);
+    }
+
+    size_t size = ggml_quantize_q4_0(src, dst, QK, QK, QK, hist);
+    assert(size == 20);
+    float max_result = ((float *)dst)[0];
+    float max_expected = src[31] / ((1 << 3) - 1);
+    assert(max_result == max_expected);
+    for (int i = 0; i < QK; i++) {
+        uint8_t q4_result = (i % 2) ? (dst[sizeof(float) + i/2] >> 4) : (dst[sizeof(float) + i/2] & 0xF);
+        uint8_t q4_expected = roundf(src[i] / max_expected) + 8;
+        assert(q4_result == q4_expected);
+    }
+
+    size = ggml_quantize_q4_1(src, dst, QK, QK, QK, hist);
+    assert(size == 24);
+    float delta_result = ((float *)dst)[0];
+    float delta_expected = (src[31] - src[0]) / ((1 << 4) - 1);
+    assert(delta_result == delta_expected);
+    float min_result = ((float *)dst)[1];
+    float min_expected = src[0];
+    assert(min_result == min_expected);
+    for (int i = 0; i < QK; i++) {
+        uint8_t q4_result = (i % 2) ? (dst[sizeof(float)*2 + i/2] >> 4) : (dst[sizeof(float)*2 + i/2] & 0xF);
+        uint8_t q4_expected = roundf((src[i] - min_expected) / delta_expected);
+        assert(q4_result == q4_expected);
+    }
+
+    return 0;
+}
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@ -0,0 +1,79 @@
+#include "utils.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+
+static const std::map<std::string, std::vector<llama_token>> 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_context * ctx;
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        ctx = llama_init_from_file(fname.c_str(), lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+    }
+
+    const int n_vocab = llama_n_vocab(ctx);
+
+    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(ctx, 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;
+}
--- a/utils.cpp
+++ b/utils.cpp
@ -3,16 +3,13 @@
 #include <cassert>
 #include <cstring>
 #include <fstream>
-#include <regex>
-#include <iostream>
-#include <iterator>
-#include <queue>
 #include <string>
-#include <math.h>
+#include <iterator>
+#include <algorithm>

 #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

@ -29,51 +26,119 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
        params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());
    }

+    bool invalid_param = false;
+    std::string arg;
    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
+        arg = argv[i];

        if (arg == "-s" || arg == "--seed") {
-            params.seed = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.seed = std::stoi(argv[i]);
        } else if (arg == "-t" || arg == "--threads") {
-            params.n_threads = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_threads = std::stoi(argv[i]);
        } else if (arg == "-p" || arg == "--prompt") {
-            params.prompt = argv[++i];
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.prompt = argv[i];
        } else if (arg == "-f" || arg == "--file") {
-            std::ifstream file(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::ifstream file(argv[i]);
            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
            if (params.prompt.back() == '\n') {
                params.prompt.pop_back();
            }
        } else if (arg == "-n" || arg == "--n_predict") {
-            params.n_predict = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_predict = std::stoi(argv[i]);
        } else if (arg == "--top_k") {
-            params.top_k = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.top_k = std::stoi(argv[i]);
        } else if (arg == "-c" || arg == "--ctx_size") {
-            params.n_ctx = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_ctx = std::stoi(argv[i]);
        } else if (arg == "--memory_f16") {
            params.memory_f16 = true;
        } else if (arg == "--top_p") {
-            params.top_p = std::stof(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.top_p = std::stof(argv[i]);
        } else if (arg == "--temp") {
-            params.temp = std::stof(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.temp = std::stof(argv[i]);
        } else if (arg == "--repeat_last_n") {
-            params.repeat_last_n = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.repeat_last_n = std::stoi(argv[i]);
        } else if (arg == "--repeat_penalty") {
-            params.repeat_penalty = std::stof(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.repeat_penalty = std::stof(argv[i]);
        } else if (arg == "-b" || arg == "--batch_size") {
-            params.n_batch = std::stoi(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_batch = std::stoi(argv[i]);
        } else if (arg == "-m" || arg == "--model") {
-            params.model = argv[++i];
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model = argv[i];
        } else if (arg == "-i" || arg == "--interactive") {
            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 == "-r" || arg == "--reverse-prompt") {
-            params.antiprompt.push_back(argv[++i]);
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            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") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_parts = std::stoi(argv[i]);
        } else if (arg == "-h" || arg == "--help") {
            gpt_print_usage(argc, argv, params);
            exit(0);
@ -82,9 +147,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
        } else {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            gpt_print_usage(argc, argv, params);
-            exit(0);
+            exit(1);
        }
    }
+    if (invalid_param) {
+        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+        gpt_print_usage(argc, argv, params);
+        exit(1);
+    }

    return true;
 }
@ -95,12 +165,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    fprintf(stderr, "options:\n");
    fprintf(stderr, "  -h, --help            show this help message and exit\n");
    fprintf(stderr, "  -i, --interactive     run in interactive mode\n");
+    fprintf(stderr, "  --interactive-first   run in interactive mode and wait for input right away\n");
    fprintf(stderr, "  -ins, --instruct      run in instruction mode (use with Alpaca models)\n");
    fprintf(stderr, "  -r PROMPT, --reverse-prompt PROMPT\n");
-    fprintf(stderr, "                        in interactive mode, poll user input upon seeing PROMPT (can be\n");
+    fprintf(stderr, "                        run in interactive mode and poll user input upon seeing PROMPT (can be\n");
    fprintf(stderr, "                        specified more than once for multiple prompts).\n");
    fprintf(stderr, "  --color               colorise output to distinguish prompt and user input from generations\n");
-    fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1)\n");
+    fprintf(stderr, "  -s SEED, --seed SEED  RNG seed (default: -1, use random seed for <= 0)\n");
    fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
    fprintf(stderr, "  -p PROMPT, --prompt PROMPT\n");
    fprintf(stderr, "                        prompt to start generation with (default: empty)\n");
@ -116,7 +187,9 @@ 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");
@ -141,535 +214,13 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
    return "The";
 }

-void replace(std::string & str, const std::string & needle, const std::string & replacement) {
-    size_t pos = 0;
-    while ((pos = str.find(needle, pos)) != std::string::npos) {
-        str.replace(pos, needle.length(), replacement);
-        pos += replacement.length();
-    }
-}
-
-std::map<std::string, int32_t> json_parse(const std::string & fname) {
-    std::map<std::string, int32_t> result;
-
-    // read file into string
-    std::string json;
-    {
-        std::ifstream ifs(fname);
-        if (!ifs) {
-            fprintf(stderr, "Failed to open %s\n", fname.c_str());
-            exit(1);
-        }
-
-        json = std::string((std::istreambuf_iterator<char>(ifs)),
-                (std::istreambuf_iterator<char>()));
-    }
-
-    if (json[0] != '{') {
-        return result;
-    }
-
-    // parse json
-    {
-        bool has_key  = false;
-        bool in_token = false;
-
-        std::string str_key = "";
-        std::string str_val = "";
-
-        int n = json.size();
-        for (int i = 1; i < n; ++i) {
-            if (!in_token) {
-                if (json[i] == ' ') continue;
-                if (json[i] == '"') {
-                    in_token = true;
-                    continue;
-                }
-            } else {
-                if (json[i] == '\\' && i+1 < n) {
-                    if (has_key == false) {
-                        str_key += json[i];
-                    } else {
-                        str_val += json[i];
-                    }
-                    ++i;
-                } else if (json[i] == '"') {
-                    if (has_key == false) {
-                        has_key = true;
-                        ++i;
-                        while (json[i] == ' ') ++i;
-                        ++i; // :
-                        while (json[i] == ' ') ++i;
-                        if (json[i] != '\"') {
-                            while (json[i] != ',' && json[i] != '}') {
-                                str_val += json[i++];
-                            }
-                            has_key = false;
-                        } else {
-                            in_token = true;
-                            continue;
-                        }
-                    } else {
-                        has_key = false;
-                    }
-
-                    ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
-                    ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
-                    ::replace(str_key, "\\\"",    "\""); // \\\"   -> "
-
-                    try {
-                        result[str_key] = std::stoi(str_val);
-                    } catch (...) {
-                        //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
-
-                    }
-                    str_key = "";
-                    str_val = "";
-                    in_token = false;
-                    continue;
-                }
-                if (has_key == false) {
-                    str_key += json[i];
-                } else {
-                    str_val += json[i];
-                }
-            }
-        }
-    }
-
-    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;
-    return lookup[highbits];
-}
-
-struct llama_sp_symbol {
-    using index = int;
-    index prev;
-    index next;
-    std::string_view text;
-};
-
-struct llama_sp_bigram {
-    struct comparator {
-        bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
-            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
-        }
-    };
-    using queue_storage = std::vector<llama_sp_bigram>;
-    using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
-    llama_sp_symbol::index left;
-    llama_sp_symbol::index right;
-    float score;
-    size_t size;
-};
-
-struct llama_tokenizer {
-    llama_tokenizer(const gpt_vocab & vocab): vocab_(vocab) {}
-
-    void tokenize(std::string_view text, std::vector<gpt_vocab::id> & output) {
-        // split string into utf8 chars
-        int index = 0;
-        while (!text.empty()) {
-            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);
-            sym.prev = index - 1;
-            text.remove_prefix(char_len);
-            sym.next = text.empty() ? -1 : index + 1;
-            index++;
-            symbols_.emplace_back(std::move(sym));
-        }
-
-        // seed the work queue with all possible 2-character tokens.
-        for (size_t i = 1; i < symbols_.size(); ++i) {
-            try_add_bigram(i - 1, i);
-        }
-
-        // keep substituting the highest frequency pairs for as long as we can.
-        while (!work_queue_.empty()) {
-            auto bigram = work_queue_.top();
-            work_queue_.pop();
-
-            auto & left_sym = symbols_[bigram.left];
-            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) {
-                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("");
-
-            // remove the right sym from the chain
-            left_sym.next = right_sym.next;
-            if (right_sym.next >= 0) {
-                symbols_[right_sym.next].prev = bigram.left;
-            }
-
-            // find more substitutions
-            try_add_bigram(left_sym.prev, bigram.left);
-            try_add_bigram(bigram.left, left_sym.next);
-        }
-
-        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));
-
-            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;
-                    output.push_back(token_id);
-                }
-            } else {
-                output.push_back((*token).second);
-            }
-        }
-    }
-
-private:
-    void try_add_bigram(int left, int right) {
-        if (left == -1 || right == -1) {
-            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));
-
-        if (token == vocab_.token_to_id.end()) {
-            return;
-        }
-
-        auto score = vocab_.score.find((*token).second);
-
-        if (score == vocab_.score.end()) {
-            return;
-        }
-
-        llama_sp_bigram bigram;
-        bigram.left = left;
-        bigram.right = right;
-        bigram.score = (*score).second;
-        bigram.size = text.size();
-        work_queue_.push(bigram);
-    }
-
-    const gpt_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) {
-    llama_tokenizer tokenizer(vocab);
-    std::vector<gpt_vocab::id> output;
-
-    if (text.size() == 0) {
-        return output;
-    }
-
-    if (bos) {
-        output.push_back(1);
-    }
-
-    tokenizer.tokenize(text, output);
-    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);
-
-    for (const auto & kv : vocab.token_to_id) {
-        vocab.id_to_token[kv.second] = 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) {
-    // 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) {
-        return a.first > b.first;
-    });
-
-    logits_id.resize(top_k);
-}
-
-gpt_vocab::id llama_sample_top_p_top_k(
-        const gpt_vocab & vocab,
-        const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
-        double repeat_penalty,
-        int top_k,
-        double top_p,
-        double temp,
-        std::mt19937 & rng) {
-    int n_logits = vocab.id_to_token.size();
-
-    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
-    logits_id.reserve(n_logits);
-
-    {
-        const double scale = 1.0/temp;
-        for (int i = 0; i < n_logits; ++i) {
-            // repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
-            // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
-            if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
-                // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
-                if (logits[i] < 0.0) {
-                    logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
-                } else {
-                    logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
-                }
-            } else {
-                logits_id.push_back(std::make_pair(logits[i]*scale, i));
-            }
-        }
-    }
-
-    sample_top_k(logits_id, top_k);
-
-    double maxl = -INFINITY;
-    for (const auto & kv : logits_id) {
-        maxl = std::max(maxl, kv.first);
-    }
-
-    // compute probs for the top K tokens
-    std::vector<double> probs;
-    probs.reserve(logits_id.size());
-
-    double sum = 0.0;
-    for (const auto & kv : logits_id) {
-        double p = exp(kv.first - maxl);
-        probs.push_back(p);
-        sum += p;
-    }
-
-    // normalize the probs
-    for (auto & p : probs) {
-        p /= sum;
-    }
-
-    if (top_p < 1.0f) {
-        double cumsum = 0.0f;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            cumsum += probs[i];
-            if (cumsum >= top_p) {
-                probs.resize(i + 1);
-                logits_id.resize(i + 1);
-                break;
-            }
-        }
-
-        cumsum = 1.0/cumsum;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            probs[i] *= cumsum;
-        }
-    }
-
-    //printf("\n");
-    //for (int i = 0; i < (int) 10; i++) {
-    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
-    //}
-    //printf("\n\n");
-    //exit(0);
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    return logits_id[idx].second;
-}
-
-
-size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
-    const int nb = k / qk;
-    const size_t bs = (sizeof(float) + sizeof(uint8_t)*qk/2);
-    const size_t row_size = nb*bs;
-
-    assert(k % qk == 0);
-
-    const size_t pp_size = qk / 2;
-    uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
-
-    char * pdst = (char *) dst;
-
-    for (int j = 0; j < n; j += k) {
-        uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
-        uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
-
-        for (int i = 0; i < nb; i++) {
-            float amax = 0.0f; // absolute max
-
-            {
-                for (int l = 0; l < qk; l++) {
-                    const float v = src[j + i*qk + l];
-                    amax = std::max(amax, fabsf(v));
-                }
-
-                const float d = amax / ((1 << 3) - 1);
-                const float id = d ? 1.0f/d : 0.0f;
-
-                *(float *) pd = d;
-                pd += bs;
-
-                for (int l = 0; l < qk; l += 2) {
-                    const float v0 = (src[j + i*qk + l + 0])*id;
-                    const float v1 = (src[j + i*qk + l + 1])*id;
-
-                    const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
-                    const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
-
-                    assert(vi0 >= 0 && vi0 < 16);
-                    assert(vi1 >= 0 && vi1 < 16);
-
-                    hist[vi0]++;
-                    hist[vi1]++;
-
-                    pp[l/2] = vi0 | (vi1 << 4);
-                }
-
-                memcpy(pb, pp, pp_size);
-                pb += bs;
-            }
-        }
-    }
-
-    return (n/k)*row_size;
-}
-
-size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist) {
-    const int nb = k / qk;
-    const size_t bs = (2*sizeof(float) + sizeof(uint8_t)*qk/2);
-    const size_t row_size = nb*bs;
-
-    assert(k % qk == 0);
-
-    const size_t pp_size = qk / 2;
-    uint8_t *pp = static_cast<uint8_t*>(alloca(pp_size));
-
-    char * pdst = (char *) dst;
-
-    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));
-
-        //printf("n = %d, k = %d, nb = %d, row_size = %d, j = %d, pm = %p, pd = %p, pb = %p\n", n, k, nb, row_size, j, pm, pd, pb);
-
-        for (int i = 0; i < nb; i++) {
-            float min = std::numeric_limits<float>::max();
-            float max = std::numeric_limits<float>::min();
-
-            {
-                for (int l = 0; l < qk; l++) {
-                    const float v = src[j + i*qk + l];
-                    if (v < min) min = v;
-                    if (v > max) max = v;
-                }
-
-                const float d = (max - min) / ((1 << 4) - 1);
-                const float id = d ? 1.0f/d : 0.0f;
-
-                *(float *) pd = d;
-                *(float *) pm = min;
-                pd += bs; 
-                pm += bs;
-
-                for (int l = 0; l < qk; l += 2) {
-                    const float v0 = (src[j + i*qk + l + 0] - min)*id;
-                    const float v1 = (src[j + i*qk + l + 1] - min)*id;
-
-                    const uint8_t vi0 = round(v0);
-                    const uint8_t vi1 = round(v1);
-
-                    assert(vi0 >= 0 && vi0 < 16);
-                    assert(vi1 >= 0 && vi1 < 16);
-
-                    hist[vi0]++;
-                    hist[vi1]++;
-
-                    pp[l/2] = vi0 | (vi1 << 4);
-                }
-
-                memcpy(pb, pp, pp_size);
-                pb += bs;
-            }
-        }
-    }
+// TODO: not great allocating this every time
+std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
+    // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
+    std::vector<llama_token> res(text.size() + (int)add_bos);
+    int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
+    assert(n >= 0);
+    res.resize(n);

-    return (n/k)*row_size;
+    return res;
 }
--- a/utils.h
+++ b/utils.h
@ -2,8 +2,9 @@

 #pragma once

+#include "llama.h"
+
 #include <string>
-#include <map>
 #include <vector>
 #include <random>
 #include <thread>
@ -13,33 +14,34 @@
 //

 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     = "";
-
-    bool random_prompt = false;
+    std::string model  = "models/lamma-7B/ggml-model.bin"; // model path
+    std::string prompt = "";

-    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; // 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 gpt_params_parse(int argc, char ** argv, gpt_params & params);
@ -48,72 +50,8 @@ 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 {
-    using id    = int32_t;
-    using token = std::string;
-
-    std::map<token, id> token_to_id;
-    std::map<id, token> id_to_token;
-    std::map<id, float> score;
-};
-
-void replace(std::string & str, const std::string & needle, const std::string & replacement);
-
-// poor-man's JSON parsing
-std::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: 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);
-
-// 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,
-        const float * logits,
-        std::vector<gpt_vocab::id> & last_n_tokens,
-        double repeat_penalty,
-        int top_k,
-        double top_p,
-        double temp,
-        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);
-
-//
-// Quantization
-//
-
-size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist);
-size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist);
+std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);
Author	SHA1	Message	Date
Georgi Gerganov	b6b268d441	Add link to Roadmap discussion	3 years ago
Georgi Gerganov	3cd8dde0d1	Revert "Fix memory allocation issues and seg faults" This reverts commit `4870e455b3`. Will provide the correct fix later	3 years ago
Georgi Gerganov	4870e455b3	Fix memory allocation issues and seg faults	3 years ago
Georgi Gerganov	483bab2e3d	Avoid the transposed X branch in the Z = X * Y matrix multiplication (#439 ) Should make results reproducible for different number of threads and batch sizes	3 years ago
Jed Fox	404e1da38e	Fix quantize script not finding models in parent directory (#428 )	3 years ago
Georgi Gerganov	4cc053b6d5	Remove oboslete command from Docker script	3 years ago
Georgi Gerganov	0ba5a3a9a5	Obsolete	3 years ago
rabidcopy	2e17dfd80a	Replace EOS with newline to prevent context/memory being flushed by EOS in interactive mode (#333 ) * Improve interactive mode's coherence after EOS Aims to improve coherence and ability to resume the interactive session when the user is given input back after an end of text token is reached. Not sure what token 13 is or why it seems to help. See conversation for examples. * Make newline token a constant * dynamically determine newline token * relocate previous newline token const * cleanup whitespace * print a new line on end of text in interactive this may need to be looked into further when not using a reverse prompt * only print manual newline with reverse prompt fix formatting of reverse prompts so they don't end up at the end of the current line while not introducing unnecessary new lines otherwise * alternate approach to replace end of text tokens * Inject the reverse prompt again after eos in interactive mode * tokenize reverse prompt when needed makes this PR compatible with https://github.com/ggerganov/llama.cpp/pull/330 * tokenize and inject only first reverse prompt thanks to tjohnman * tokenize first reverse prompt once * add newline token * add newline token * tokenize/inject reverse prompt for refactor this doesn't seem right though * tokenize nothing for antiprompt if no reverse * Update main.cpp * Update main.cpp * tokenize and inject reverse prompt as needed this doesn't seem to work if the reverse prompt is tokenized outside earlier on * not needed * remove newline token * remove newline token * tokenize newline token * add space to comment * Update main.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Timmy Knight	20a1a4e09c	Fix GPTQ converter (#423 ) * Fix GPTQ converter * Fix comment --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
nusu-github	ad072fc5ad	Generate library with CMake (#430 ) * Generate library with CMake BUILD_SHARED_LIBS to allow llama library to be generated. * Turn ON PIC when BUILD_SHARED_LIBS is ON	3 years ago
anzz1	ea10d3ded2	Command line args bounds checking (#424 ) * command line args bounds checking * unknown and invalid param exit codes 0 -> 1	3 years ago
Ben Siraphob	a18c19259a	Fix Nix build	3 years ago
Stephan Walter	a50e39c6fe	Revert "Delete SHA256SUMS for now" (#429 ) * Revert "Delete SHA256SUMS for now (#416)" This reverts commit `8eea5ae0e5`. * Remove ggml files until they can be verified * Remove alpaca json * Add also model/tokenizer.model to SHA256SUMS + update README --------- Co-authored-by: Pavol Rusnak <pavol@rusnak.io>	3 years ago
Kerfuffle	a140219e81	Fix Makefile echo escape codes (by removing them). (#418 )	3 years ago
Gary Mulder	8a3e5ef801	Move model section from issue template to README.md (#421 ) * Update custom.md * Removed Model section as it is better placed in README.md * Updates to README.md model section * Inserted text that was removed from issue template about obtaining models from FB and links to papers describing the various models * Removed IPF down links for the Alpaca 7B models as these look to be in the old data format and probably shouldn't be directly linked to, anyway * Updated the perplexity section to point at Perplexity scores #406 discussion	3 years ago
anzz1	8eea5ae0e5	Delete SHA256SUMS for now (#416 ) Delete this for now to avoid confusion since it contains some wrong checksums from the old tokenizer format Re-add after #374 is resolved	3 years ago
Georgi Gerganov	93208cfb92	Adjust repetition penalty ..	3 years ago
Georgi Gerganov	03ace14cfd	Add link to recent podcast about whisper.cpp and llama.cpp	3 years ago
anzz1	e4412b45e3	CI: CMake: Separate build and test steps (#376 ) * CI: Separate Build and Test steps (CMake) * CI: Make sure build passes before running tests (CMake) * CI: Standardise step id names	3 years ago
tjohnman	f7dc43bc0d	Fix instruct mode broken by PR #354 (#409 ) Co-authored-by: Johnman <tjohnman@github>	3 years ago
Gary Mulder	ee8a788786	Update issue template so people will use it (#404 )	3 years ago
Stephan Walter	69c92298a9	Deduplicate q4 quantization functions (#383 ) * Deduplicate q4 quantization functions * Use const; add basic test * Re-enable quantization test * Disable AVX2 flags in CI --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Valentyn Bezshapkin	97940520e8	fix: add POSIX functionality for Linux compilation (#51 ) * fix: add POSIX functionality for Linux compilation * fix: older standard for compatibility	3 years ago
tjohnman	305ba6f0e6	Don't force immediate interactive without `-i` (#354 ) * Don't force immediate interactive without -i Sometimes we might want to use a reverse prompt but we want to let the model generate tokens right after the initial prompt. So we don't force user input mode if the -i flag wasn't specified and instead let it run until we encounter the reverse prompt. This gives use some more flexibility, since it doesn't force the user to enter a newline if they want to let the model generate text right after the initial prompt and only be asked for input if the reverse prompt is encountered. The `--interactive-first` flag is reintroduced to force the old behavior. `-r` behaves like `-i` plus introduces a reverse prompt (it can be specified more than once). * Update help output. --------- Co-authored-by: Johnman <tjohnman@github>	3 years ago
Erik Scholz	4122dffff9	cmake: make llama an actual library (#392 )	3 years ago
Erik Scholz	56e659a0b2	fix perplexity after c-api refactor (#390 ) * preallocate a buffer of fitting size for tokenization (utils.cpp) * don't create a new std::string (especially here, where it's usually large)	3 years ago
Gary Linscott	40ea807a97	Add details on perplexity to README.md (#395 )	3 years ago
Yusuf Kağan Hanoğlu	d5850c53ca	Add missing header for memcpy (#386 ) fixed: memcpy is not defined	3 years ago
Georgi Gerganov	ae44e23ee3	When seed <= 0 - use the clock to generate one	3 years ago
Georgi Gerganov	928480ef5b	Init llama_context_params properly from CLI (#370 )	3 years ago
Georgi Gerganov	56817b1f88	Remove temporary notice and update hot topics	3 years ago
Georgi Gerganov	f5a77a629b	Introduce C-style API (#370 ) * Major refactoring - introduce C-style API * Clean up * Add <cassert> * Add <iterator> * Add <algorithm> .... * Fix timing reporting and accumulation * Measure eval time only for single-token calls * Change llama_tokenize return meaning	3 years ago
Gary Mulder	da0e9fe90c	Add SHA256SUMS file and instructions to README how to obtain and verify the downloads Hashes created using: sha256sum models/B/.pth models/[7136]B/ggml-model-f16.bin models/[7136]B/ggml-model-q4_0.bin > SHA256SUMS	3 years ago
anzz1	e6c9e0986c	Fix bin dir for win ci	3 years ago
Erik Scholz	01a297b099	specify build type for ctest on windows (#371 )	3 years ago
Georgi Gerganov	3366853e41	Add notice about pending change	3 years ago
Mathieu Nayrolles	3f9c6135e4	fix typo in chatLLaMa (#368 ) The prompt contains a typo where 'alound' is used instead of 'aloud'.	3 years ago
Georgi Gerganov	0f61352708	Update issue templates	3 years ago
Fabio R. Sluzala	353ec251a4	We could use std::unordered_map over std::map (#305 ) * Improve performance by changing std::map to std::unordered_map and std::map<id, token> id_to_token; to std::vector<token> id_to_token; * fix last commit on gpt_vocab_init add vocab.id_to_token.resize(vocab.token_to_id.size()); * Removed include <map> * Nest struct token score inside gpt_vocab * renamed token to tok	3 years ago
Matvey Soloviev	89d5d90f3b	Fix color codes emitting mid-UTF8 code. (#312 )	3 years ago
comex	16ffc013c6	Importer for GPTQ quantized LLaMA models (#301 ) * [WIP, broken] Importer for GPTQ quantized LLaMA models Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa Current status: Something is busted. The output starts out decent, but quickly degrades into gibberish. This doesn't happen with either the original GPTQ-for-LLaMa using the same weights, or llama.cpp when using weights quantized by its own quantizer. Is there a bug in the conversion script that somehow only comes into play with a large context size? I did notice one potential issue. It's clearly not the main cause of the gibberish, since it doesn't happen when using q4_1 weights quantized by llama.cpp itself, but it seems concerning. When doing a matrix multiplication of f16 * f32 => f32 or q4_1 * f32 => f32, at least when the multiplication is not done with BLAS, the intermediate results are stored in the smaller format rather than f32. This seems like an unnecessary waste of precision, especially in the q4_1 case. I was originally hoping to validate the results by matching the Python implementation's output exactly, but precision and non-associativity issues make this very difficult, including when performing matrix multiplications and, especially, computing norms. Anyway, design details: The models being imported store per-layer weights in essentially q4_1 format, although the addend and scale are shared across an entire row rather than every group of 32 weights. This script duplicates the addend and scale to match ggml's expectations, at the cost of wasting some memory. However, there are two differences which I accommodated changing the output format (and adding corresponding support to main.cpp) rather than having the script match the existing one: - The tok_embeddings and output weights (i.e. the weights that aren't per-layer) are f16 instead of q4_1. They could be converted to q4_1, and the impact of the loss of precision would probably be low, but this would rule out exactly matching the Python implementation's output for validation. - There is no sharding, since the input doesn't have it, and for a CPU-only implementation it seems more useful to avoid having to deal with multiple files. The new format is differentiated from existing q4_1 format by changing the 'f16' header flag to a new value, 4. That said, I think a cleaner approach would be to change main.cpp to support loading each tensor with an arbitrary sharding configuration and type rather than hardcoding specific combinations of types. So far I've wasted too much time debugging to try implementing this... * Add missing permutation. Now it works. --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Gary Linscott	486ae645fd	Compute perplexity over prompt (#270 ) * Compute perplexity over prompt * More accurate perplexity calculation - over all logits in the context window (so 512x more tokens!) * Output all perplexitiies * Add timing/ETA	3 years ago
Jean-Christophe Hoelt	3ab3e6582f	Add chatLLaMa script (#198 ) * Add chatLLaMa script * Fix shellcheck errors and do some cleanup * Move chatLLaMa script to `examples` directory * Reduce chatLLaMa context size to 2048 Ref `d7def1a752` * Include n_predict to 2048 in examples/chatLLaMa	3 years ago
Alex von Gluck IV	f157088cb7	makefile: Fix CPU feature detection on Haiku (#218 )	3 years ago
anzz1	c86ba036e6	Enable ANSI colors on Windows 10+ (#311 ) * Enable ANSI colors on Windows 10+ On older versions function will silently fail without any ill effects * Do not call SetConsoleMode if the mode is already set * Update main.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Georgi Gerganov	1daf4dd712	Minor style changes	3 years ago
Georgi Gerganov	dc6a845b85	Add chat.sh script	3 years ago
tjohnman	6a612959e1	Check for reverse prompt by characters instead of tokens (#292 ) (#330 ) * Check for reverse prompt by characters instead of tokens (#292) * Update main.cpp Wording. * Cleanup. * Remove unnecessary use of std::stringstream. --------- Co-authored-by: Johnman <tjohnman@github> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
tjohnman	d5f56a5e5a	Check for reverse prompt by characters instead of tokens (#292 ) (#330 ) * Check for reverse prompt by characters instead of tokens (#292) * Update main.cpp Wording. * Cleanup. * Remove unnecessary use of std::stringstream. --------- Co-authored-by: Johnman <tjohnman@github> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Georgi Gerganov	3bfa3b43b7	Fix convert script, warnings alpaca instructions, default params	3 years ago
Kevin Lo	715d292ee0	Add OpenBSD support (#314 )	3 years ago
Mack Straight	c98ae02668	fix typo in comment (#318 )	3 years ago
Qingyou Meng	c3b2306b18	Makefile: slightly cleanup for Mac Intel; echo instead of run ./main -h (#335 )	3 years ago
anzz1	975d2cebf9	cmdline option for custom amount of model parts (--n_parts N) (#348 ) * cmdline option for custom amount of model parts (--n_parts N) * Update main.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	3 years ago
Kevin Kwok	e0ffc861fa	Update IPFS links to quantized alpaca with new tokenizer format (#352 )	3 years ago
Georgi Gerganov	8f644a0a85	Change default repeat_penalty to 1.0 I feel this penalty is not really helping. Especially for the example from the README it makes results pretty bad	3 years ago
Georgi Gerganov	eb34620aec	Add tokenizer test + revert to C++11 (#355 ) * Add test-tokenizer-0 to do a few tokenizations - feel free to expand * Added option to convert-pth-to-ggml.py script to dump just the vocabulary * Added ./models/ggml-vocab.bin containing just LLaMA vocab data (used for tests) * Added utility to load vocabulary file from previous point (temporary implementation) * Avoid using std::string_view and drop back to C++11 (hope I didn't break something) * Rename gpt_vocab -> llama_vocab * All CMake binaries go into ./bin/ now	3 years ago
Casey Primozic	2e664f1ff4	Add initial AVX512 support for dot product on Linux (#320 ) * Update Makefile to detect AVX512 support and add compiler flags if it's available * Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time * Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16 * Use built-in AVX512 horizontal reduce add to get sum at the end * Manual unrolling on inner dot product loop to reduce loop counter overhead	3 years ago