Docker - Fix publish docker image in GitHub Registry (#235)

* fix publish permission

* try to fix docker pipeline using as password github_token & username repository_owner

.devops/full.Dockerfile

@@ -6,7 +6,7 @@ RUN apt-get update && \
     apt-get install -y build-essential python3 python3-pip
 
 RUN pip install --upgrade pip setuptools wheel \
-    && pip install torch torchvision torchaudio sentencepiece numpy
+    && pip install numpy requests sentencepiece torch tqdm
 
 WORKDIR /app
 
@@ -14,4 +14,4 @@ COPY . .
 
 RUN make
 
-ENTRYPOINT ["/app/.devops/tools.sh"]
+ENTRYPOINT ["/app/.devops/tools.sh"]

.github/workflows/docker.yml

@@ -40,7 +40,7 @@ jobs:
         uses: docker/login-action@v2
         with:
           registry: ghcr.io
-          username: ${{ github.actor }}
+          username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push Docker image (versioned)

Makefile

@@ -31,7 +31,7 @@ endif
 #
 
 CFLAGS   = -I.              -O3 -DNDEBUG -std=c11   -fPIC
-CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
+CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++17 -fPIC
 LDFLAGS  =
 
 # OS specific

README.md

@@ -7,10 +7,13 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 **Hot topics:**
 
-- RMSNorm implementation / fixes: https://github.com/ggerganov/llama.cpp/issues/173
+- [Added Alpaca support](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
 - 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
@@ -147,7 +150,7 @@ python3 -m pip install torch numpy sentencepiece
 python3 convert-pth-to-ggml.py models/7B/ 1
 
 # quantize the model to 4-bits
-./quantize.sh 7B
+python3 quantize.py 7B
 
 # run the inference
 ./main -m ./models/7B/ggml-model-q4_0.bin -n 128
@@ -176,21 +179,51 @@ In this mode, you can always interrupt generation by pressing Ctrl+C and enter o
 
 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:" \
-                                           -p \
-"Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
-
-User: Hello, Bob.
-Bob: Hello. How may I help you today?
-User: Please tell me the largest city in Europe.
-Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
-User:"
+./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:
+
+```
+./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins
+```
+
+Sample run:
+
+```
+== Running in interactive mode. ==
+ - Press Ctrl+C to interject at any time.
+ - Press Return to return control to LLaMa.
+ - If you want to submit another line, end your input in '\'.
+
+ Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+> How many letters are there in the English alphabet?
+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".                                                                       
+cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
+> 
+```
+
 ### Android
 
 You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux).

alpaca.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+#
+# 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

convert-pth-to-ggml.py

@@ -16,7 +16,7 @@
 # At the start of the ggml file we write the model parameters
 # and vocabulary.
 #
-import os
+import argparse
 import sys
 import json
 import struct
@@ -24,136 +24,83 @@ import numpy as np
 import torch
 from sentencepiece import SentencePieceProcessor
 
-if len(sys.argv) < 3:
-    print("Usage: convert-ckpt-to-ggml.py dir-model ftype\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
-    sys.exit(1)
+def parse_args():
 
-# output in the same directory as the model
-dir_model = sys.argv[1]
-
-fname_hparams   = sys.argv[1] + "/params.json"
-fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
+    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)')
+    return parser.parse_args()
 
 def get_n_parts(dim):
-    if dim == 4096:
-        return 1
-    elif dim == 5120:
-        return 2
-    elif dim == 6656:
-        return 4
-    elif dim == 8192:
-        return 8
-    else:
-        print("Invalid dim: " + str(dim))
+
+    mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
+    n_parts = mappings.get(dim)
+    if n_parts is None:
+        print(f"Invalid dim: {dim}")
         sys.exit(1)
 
-# possible data types
-#   ftype == 0 -> float32
-#   ftype == 1 -> float16
-#
-# map from ftype to string
-ftype_str = ["f32", "f16"]
+    print(f"n_parts = {n_parts}\n")
+    return n_parts
 
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
-        sys.exit(1)
-    fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
+def load_hparams_and_tokenizer(dir_model):
 
-if os.path.exists(fname_out):
-    print(f"Skip conversion, it already exists: {fname_out}")
-    sys.exit(0)
+    fname_hparams = f"{dir_model}/params.json"
+    fname_tokenizer = f"{dir_model}/../tokenizer.model"
 
-with open(fname_hparams, "r") as f:
-    hparams = json.load(f)
+    with open(fname_hparams, "r") as f:
+        hparams = json.load(f)
+        print(hparams)
 
-tokenizer = SentencePieceProcessor(fname_tokenizer)
+    tokenizer = SentencePieceProcessor(fname_tokenizer)
+    hparams.update({"vocab_size": tokenizer.vocab_size()})
 
-hparams.update({"vocab_size": tokenizer.vocab_size()})
+    return hparams, tokenizer
 
-n_parts = get_n_parts(hparams["dim"])
+def write_header(fout, hparams, ftype):
 
-print(hparams)
-print('n_parts = ', n_parts)
+    keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
+    values = [
+        0x67676d66,  # magic: ggml in hex
+        1, # file version
+        *[hparams[key] for key in keys],
+        hparams["dim"] // hparams["n_heads"],  # rot (obsolete)
+        ftype
+    ]
+    fout.write(struct.pack("i" * len(values), *values))
 
-for p in range(n_parts):
-    print('Processing part ', p)
+def write_tokens(fout, tokenizer):
 
-    #fname_model = sys.argv[1] + "/consolidated.00.pth"
-    fname_model = sys.argv[1] + "/consolidated.0" + str(p) + ".pth"
-    fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
-    if (p > 0):
-        fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
-
-    model = torch.load(fname_model, map_location="cpu")
-
-    fout = open(fname_out, "wb")
-
-    fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
-    fout.write(struct.pack("i", hparams["vocab_size"]))
-    fout.write(struct.pack("i", hparams["dim"]))
-    fout.write(struct.pack("i", hparams["multiple_of"]))
-    fout.write(struct.pack("i", hparams["n_heads"]))
-    fout.write(struct.pack("i", hparams["n_layers"]))
-    fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
-    fout.write(struct.pack("i", ftype))
-
-    # Is this correct??
     for i in range(tokenizer.vocab_size()):
         if tokenizer.is_unknown(i):
-            # "<unk>" token (translated as ??)
             text = " \u2047 ".encode("utf-8")
-            fout.write(struct.pack("i", len(text)))
-            fout.write(text)
         elif tokenizer.is_control(i):
-            # "<s>"/"</s>" tokens
-            fout.write(struct.pack("i", 0))
+            text = b""
         elif tokenizer.is_byte(i):
-            # "<U+XX>" tokens (which may be invalid UTF-8)
             piece = tokenizer.id_to_piece(i)
             if len(piece) != 6:
-                print("Invalid token: " + piece)
+                print(f"Invalid token: {piece}")
                 sys.exit(1)
             byte_value = int(piece[3:-1], 16)
-            fout.write(struct.pack("i", 1))
-            fout.write(struct.pack("B", byte_value))
+            text = struct.pack("B", byte_value)
         else:
-            # normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
             text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
-            fout.write(struct.pack("i", len(text)))
-            fout.write(text)
+        fout.write(struct.pack("i", len(text)))
+        fout.write(text)
+        fout.write(struct.pack("f", tokenizer.get_score(i)))
 
-    for k, v in model.items():
-        name = k
-        shape = v.shape
+def process_and_write_variables(fout, model, ftype):
 
-        # skip layers.X.attention.inner_attention.rope.freqs
-        if name[-5:] == "freqs":
+    for name, datao in model.items():
+
+        if name.endswith("freqs"):
             continue
 
-        print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
+        shape = datao.shape
 
-        #data = tf.train.load_variable(dir_model, name).squeeze()
-        data = v.numpy().squeeze()
-        n_dims = len(data.shape);
+        print(f"Processing variable: {name} with shape: {shape} and type: {datao.dtype}")
 
-        # for efficiency - transpose some matrices
-        # "model/h.*/attn/c_attn/w"
-        # "model/h.*/attn/c_proj/w"
-        # "model/h.*/mlp/c_fc/w"
-        # "model/h.*/mlp/c_proj/w"
-        #if name[-14:] == "/attn/c_attn/w" or \
-        #   name[-14:] == "/attn/c_proj/w" or \
-        #   name[-11:] == "/mlp/c_fc/w" or \
-        #   name[-13:] == "/mlp/c_proj/w":
-        #    print("  Transposing")
-        #    data = data.transpose()
-
-        dshape = data.shape
+        data = datao.numpy().squeeze()
+        n_dims = len(shape)
 
         # default type is fp16
         ftype_cur = 1
@@ -164,18 +111,40 @@ for p in range(n_parts):
 
         # header
         sname = name.encode('utf-8')
-        fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
-        for i in range(n_dims):
-            fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
-        fout.write(sname);
+        fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
+        for dim in reversed(data.shape):
+            fout.write(struct.pack("i", dim))
+        fout.write(sname)
 
-        # data
+        # data output to file
         data.tofile(fout)
 
-    # I hope this deallocates the memory ..
-    model = None
+def main():
 
-    fout.close()
+    args = parse_args()
+    dir_model = args.dir_model
+    ftype = args.ftype
+    ftype_str = ["f32", "f16"]
 
-    print("Done. Output file: " + fname_out + ", (part ", p, ")")
-    print("")
+    hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
+    n_parts = get_n_parts(hparams["dim"])
+
+    for p in range(n_parts):
+
+        print(f"Processing part {p}\n")
+
+        fname_model = f"{dir_model}/consolidated.0{p}.pth"
+        fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
+
+        model = torch.load(fname_model, map_location="cpu")
+
+        with open(fname_out, "wb") as fout:
+            write_header(fout, hparams, ftype)
+            write_tokens(fout, tokenizer)
+            process_and_write_variables(fout, model, ftype)
+
+        del model
+        print(f"Done. Output file: {fname_out}, (part {p})\n")
+
+if __name__ == "__main__":
+    main()

main.cpp

@@ -3,10 +3,12 @@
 #include "utils.h"
 
 #include <cassert>
+#include <cinttypes>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
 #include <fstream>
+#include <iostream>
 #include <map>
 #include <string>
 #include <vector>
@@ -27,6 +29,8 @@
 #define ANSI_COLOR_RESET   "\x1b[0m"
 #define ANSI_BOLD          "\x1b[1m"
 
+static const int EOS_TOKEN_ID = 2;
+
 // determine number of model parts based on the dimension
 static const std::map<int, int> LLAMA_N_PARTS = {
     { 4096, 1 },
@@ -86,7 +90,7 @@ struct llama_model {
 };
 
 // load the model's weights from a file
-bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
+bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
     fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
 
     std::vector<char> f_buf(1024*1024);
@@ -102,10 +106,24 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     {
         uint32_t magic;
         fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
+        if (magic == 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname.c_str());
+            return false;
+        }
+        if (magic != 0x67676d66) {
             fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
             return false;
         }
+
+        uint32_t format_version;
+        fin.read((char *) &format_version, sizeof(format_version));
+
+        if (format_version != 1) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
+                    __func__, fname.c_str(), format_version);
+            return false;
+        }
     }
 
     int n_ff = 0;
@@ -151,8 +169,12 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             word.resize(len);
             fin.read((char *) word.data(), len);
 
+            float score;
+            fin.read((char *) &score, sizeof(score));
+
             vocab.token_to_id[word] = i;
             vocab.id_to_token[i] = word;
+            vocab.score[i] = score;
 
             //if (i < 30000) {
             //    fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
@@ -176,8 +198,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
                 }
     }
 
-    const ggml_type wtype2 = GGML_TYPE_F32;
-
     auto & ctx = model.ctx;
 
     size_t ctx_size = 0;
@@ -209,8 +229,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
         ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
 
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
 
         ctx_size += (5 + 10*n_layer)*256; // object overhead
 
@@ -237,7 +257,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
         const int n_embd  = hparams.n_embd;
         const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
         const int n_vocab = hparams.n_vocab;
 
         model.layers.resize(n_layer);
@@ -296,8 +315,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         const int n_mem      = n_layer*n_ctx;
         const int n_elements = n_embd*n_mem;
 
-        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
-        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+        model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
+        model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
 
         const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
 
@@ -539,9 +558,7 @@ bool llama_eval(
     const int n_vocab = hparams.n_vocab;
     const int n_rot   = hparams.n_embd/hparams.n_head;
 
-    const int d_key = n_embd/n_head;
-
-     // TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
+    // TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
     // static size_t buf_size = hparams.n_ctx*1024*1024;
     static size_t buf_size = 512u*1024*1024;
     static void * buf = malloc(buf_size);
@@ -752,6 +769,7 @@ static bool is_interacting = false;
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
 void sigint_handler(int signo) {
     printf(ANSI_COLOR_RESET);
+    printf("\n"); // this also force flush stdout.
     if (signo == SIGINT) {
         if (!is_interacting) {
             is_interacting=true;
@@ -792,7 +810,7 @@ int main(int argc, char ** argv) {
     if (gpt_params_parse(argc, argv, params) == false) {
         return 1;
     }
-    
+
     if (params.n_ctx > 2048) {
         fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
@@ -805,7 +823,7 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
-    if (params.prompt.empty()) {
+    if (params.random_prompt) {
         params.prompt = gpt_random_prompt(rng);
     }
 
@@ -819,8 +837,9 @@ int main(int argc, char ** argv) {
 
     // load the model
     {
+        const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
         const int64_t t_start_us = ggml_time_us();
-        if (!llama_model_load(params.model, model, vocab, params.n_ctx)) {  
+        if (!llama_model_load(params.model, model, vocab, params.n_ctx, memory_type)) {
             fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
             return 1;
         }
@@ -849,8 +868,27 @@ int main(int argc, char ** argv) {
 
     params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
 
+    // prefix & suffix for instruct mode
+    const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
+    const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
+
+    // in instruct mode, we inject a prefix and a suffix to each input by the user
+    if (params.instruct) {
+        params.interactive = true;
+        params.antiprompt.push_back("### Instruction:\n\n");
+    }
+
     // tokenize the reverse prompt
-    std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
+    std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
+    
+    for (auto antiprompt : params.antiprompt) {
+        antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
+    }
+
+    // enable interactive mode if reverse prompt is specified
+    if (antipromptv_inp.size() != 0) {
+        params.interactive = true;
+    }
 
     fprintf(stderr, "\n");
     fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
@@ -872,13 +910,16 @@ int main(int argc, char ** argv) {
 
         fprintf(stderr, "%s: interactive mode on.\n", __func__);
 
-        if(antiprompt_inp.size()) {
-            fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
-            fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
-            for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
-                fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
+        if(antipromptv_inp.size()) {
+            for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
+                auto antiprompt_inp = antipromptv_inp.at(apindex);
+                fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
+                fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
+                for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
+                    fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
+                }
+                fprintf(stderr, "\n");
             }
-            fprintf(stderr, "\n");
         }
     }
     fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
@@ -894,31 +935,27 @@ int main(int argc, char ** argv) {
     std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
     std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
 
-
     if (params.interactive) {
         fprintf(stderr, "== Running in interactive mode. ==\n"
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
                " - Press Ctrl+C to interject at any time.\n"
 #endif
                " - Press Return to return control to LLaMa.\n"
-               " - If you want to submit another line, end your input in '\\'.\n");
+               " - If you want to submit another line, end your input in '\\'.\n\n");
+        is_interacting = true;
     }
 
-    int remaining_tokens = params.n_predict;
     int input_consumed = 0;
     bool input_noecho = false;
 
-    // prompt user immediately after the starting prompt has been loaded
-    if (params.interactive_start) {
-        is_interacting = true;
-    }
+    int remaining_tokens = params.n_predict;
 
     // set the color for the prompt which will be output initially
     if (params.use_color) {
         printf(ANSI_COLOR_YELLOW);
     }
 
-    while (remaining_tokens > 0) {
+    while (remaining_tokens > 0 || params.interactive) {
         // predict
         if (embd.size() > 0) {
             const int64_t t_start_us = ggml_time_us();
@@ -948,6 +985,11 @@ int main(int argc, char ** argv) {
             {
                 const int64_t t_start_sample_us = ggml_time_us();
 
+                if (params.ignore_eos) {
+                    // set the logit of the eos token to zero to avoid sampling it
+                    logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
+                }
+
                 id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
 
                 last_n_tokens.erase(last_n_tokens.begin());
@@ -971,15 +1013,10 @@ int main(int argc, char ** argv) {
                 last_n_tokens.erase(last_n_tokens.begin());
                 last_n_tokens.push_back(embd_inp[input_consumed]);
                 ++input_consumed;
-                if (embd.size() > params.n_batch) {
+                if ((int) embd.size() >= params.n_batch) {
                     break;
                 }
             }
-
-            // reset color to default if we there is no pending user input
-            if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) {
-                printf(ANSI_COLOR_RESET);
-            }
         }
 
         // display text
@@ -989,56 +1026,74 @@ int main(int argc, char ** argv) {
             }
             fflush(stdout);
         }
+        // reset color to default if we there is no pending user input
+        if (!input_noecho && params.use_color && (int)embd_inp.size() == input_consumed) {
+            printf(ANSI_COLOR_RESET);
+        }
 
         // in interactive mode, and not currently processing queued inputs;
         // check if we should prompt the user for more
         if (params.interactive && embd_inp.size() <= input_consumed) {
             // check for reverse prompt
-            if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
-                // reverse prompt found
-                is_interacting = true;
+            for (auto antiprompt_inp : antipromptv_inp) {
+                if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
+                    // reverse prompt found
+                    is_interacting = true;
+                    break;
+                }
             }
             if (is_interacting) {
-                // currently being interactive
-                bool another_line=true;
-                while (another_line) {
-                    fflush(stdout);
-                    char buf[256] = {0};
-                    int n_read;
-                    if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
-                    if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
-                        // presumable empty line, consume the newline
-                        std::ignore = scanf("%*c");
-                        n_read=0;
-                    }
-                    if(params.use_color) printf(ANSI_COLOR_RESET);
+                if (params.instruct) {
+                    input_consumed = embd_inp.size();
+                    embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
 
-                    if (n_read > 0 && buf[n_read-1]=='\\') {
-                        another_line = true;
-                        buf[n_read-1] = '\n';
-                        buf[n_read] = 0;
-                    } else {
-                        another_line = false;
-                        buf[n_read] = '\n';
-                        buf[n_read+1] = 0;
-                    }
-
-                    std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
-                    embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
-
-                    remaining_tokens -= line_inp.size();
-
-                    input_noecho = true; // do not echo this again
+                    printf("\n> ");
                 }
 
-                is_interacting = false;
+                // currently being interactive
+                if (params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
+                std::string buffer;
+                std::string line;
+                bool another_line = true;
+                do {
+                    std::getline(std::cin, line);
+                    if (line.empty() || line.back() != '\\') {
+                        another_line = false;
+                    } else {
+                        line.pop_back(); // Remove the continue character
+                    }
+                    buffer += line + '\n'; // Append the line to the result
+                } while (another_line);
+                if (params.use_color) printf(ANSI_COLOR_RESET);
+
+                std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
+                embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
+
+                if (params.instruct) {
+                    embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
+                }
+
+                remaining_tokens -= line_inp.size();
+
+                input_noecho = true; // do not echo this again
             }
+            is_interacting = false;
         }
 
         // end of text token
-        if (embd.back() == 2) {
-            fprintf(stderr, " [end of text]\n");
-            break;
+        if (embd.back() == EOS_TOKEN_ID) {
+            if (params.interactive) {
+                is_interacting = true;
+            } else {
+                fprintf(stderr, " [end of text]\n");
+                break;
+            }
+        }
+
+        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
+        if (params.interactive && remaining_tokens <= 0) {
+            remaining_tokens = params.n_predict;
+            is_interacting = true;
         }
     }
 

prompts/alpaca.txt

@@ -0,0 +1 @@
+Below is an instruction that describes a task. Write a response that appropriately completes the request.

prompts/chat-with-bob.txt

@@ -0,0 +1,7 @@
+Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
+
+User: Hello, Bob.
+Bob: Hello. How may I help you today?
+User: Please tell me the largest city in Europe.
+Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
+User:

quantize.cpp

@@ -3,6 +3,7 @@
 #include "utils.h"
 
 #include <cassert>
+#include <cinttypes>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
@@ -63,12 +64,28 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
     {
         uint32_t magic;
         finp.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
+        if (magic == 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
+                    __func__, fname_inp.c_str());
+            return false;
+        }
+        if (magic != 0x67676d66) {
             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 != 1) {
+            fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ")\n",
+                    __func__, fname_inp.c_str(), format_version);
+            return false;
+        }
+
+        fout.write((char *) &format_version, sizeof(format_version));
     }
 
     llama_hparams hparams;
@@ -122,8 +139,13 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
             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;
         }
     }
 

quantize.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+
+"""Script to execute the "quantize" script on a given set of models."""
+
+import subprocess
+import argparse
+import glob
+import sys
+import os
+
+
+def main():
+    """Update the quantize binary name depending on the platform and parse
+    the command line arguments and execute the script.
+    """
+
+    if "linux" in sys.platform or "darwin" in sys.platform:
+        quantize_script_binary = "quantize"
+
+    elif "win32" in sys.platform or "cygwin" in sys.platform:
+        quantize_script_binary = "quantize.exe"
+
+    else:
+        print("WARNING: Unknown platform. Assuming a UNIX-like OS.\n")
+        quantize_script_binary = "quantize"
+
+    parser = argparse.ArgumentParser(
+        prog='python3 quantize.py',
+        description='This script quantizes the given models by applying the '
+        f'"{quantize_script_binary}" script on them.'
+    )
+    parser.add_argument(
+        'models', nargs='+', choices=('7B', '13B', '30B', '65B'),
+        help='The models to quantize.'
+    )
+    parser.add_argument(
+        '-r', '--remove-16', action='store_true', dest='remove_f16',
+        help='Remove the f16 model after quantizing it.'
+    )
+    parser.add_argument(
+        '-m', '--models-path', dest='models_path',
+        default=os.path.join(os.getcwd(), "models"),
+        help='Specify the directory where the models are located.'
+    )
+    parser.add_argument(
+        '-q', '--quantize-script-path', dest='quantize_script_path',
+        default=os.path.join(os.getcwd(), quantize_script_binary),
+        help='Specify the path to the "quantize" script.'
+    )
+
+    # TODO: Revise this code
+    # parser.add_argument(
+    #     '-t', '--threads', dest='threads', type='int',
+    #     default=os.cpu_count(),
+    #     help='Specify the number of threads to use to quantize many models at '
+    #     'once. Defaults to os.cpu_count().'
+    # )
+
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.quantize_script_path):
+        print(
+            f'The "{quantize_script_binary}" script was not found in the '
+            "current location.\nIf you want to use it from another location, "
+            "set the --quantize-script-path argument from the command line."
+        )
+        sys.exit(1)
+
+    for model in args.models:
+        # The model is separated in various parts
+        # (ggml-model-f16.bin, ggml-model-f16.bin.0, ggml-model-f16.bin.1...)
+        f16_model_path_base = os.path.join(
+            args.models_path, model, "ggml-model-f16.bin"
+        )
+
+        f16_model_parts_paths = map(
+            lambda filename: os.path.join(f16_model_path_base, filename),
+            glob.glob(f"{f16_model_path_base}*")
+        )
+
+        for f16_model_part_path in f16_model_parts_paths:
+            if not os.path.isfile(f16_model_part_path):
+                print(
+                    f"The f16 model {os.path.basename(f16_model_part_path)} "
+                    f"was not found in {args.models_path}{os.path.sep}{model}"
+                    ". If you want to use it from another location, set the "
+                    "--models-path argument from the command line."
+                )
+                sys.exit(1)
+
+            __run_quantize_script(
+                args.quantize_script_path, f16_model_part_path
+            )
+
+            if args.remove_f16:
+                os.remove(f16_model_part_path)
+
+
+# This was extracted to a top-level function for parallelization, if
+# implemented. See https://github.com/ggerganov/llama.cpp/pull/222/commits/f8db3d6cd91bf1a1342db9d29e3092bc12dd783c#r1140496406
+
+def __run_quantize_script(script_path, f16_model_part_path):
+    """Run the quantize script specifying the path to it and the path to the
+    f16 model to quantize.
+    """
+
+    new_quantized_model_path = f16_model_part_path.replace("f16", "q4_0")
+    subprocess.run(
+        [script_path, f16_model_part_path, new_quantized_model_path, "2"],
+        check=True
+    )
+
+
+if __name__ == "__main__":
+    try:
+        main()
+
+    except subprocess.CalledProcessError:
+        print("\nAn error ocurred while trying to quantize the models.")
+        sys.exit(1)
+
+    except KeyboardInterrupt:
+        sys.exit(0)
+
+    else:
+        print("\nSuccesfully quantized all models.")

quantize.sh

@@ -1,15 +0,0 @@
-#!/usr/bin/env bash
-
-if ! [[ "$1" =~ ^[0-9]{1,2}B$ ]]; then
-    echo
-    echo "Usage: quantize.sh 7B|13B|30B|65B [--remove-f16]"
-    echo
-    exit 1
-fi
-
-for i in `ls models/$1/ggml-model-f16.bin*`; do
-    ./quantize "$i" "${i/f16/q4_0}" 2
-    if [[ "$2" == "--remove-f16" ]]; then
-        rm "$i"
-    fi
-done

utils.cpp

@@ -6,6 +6,7 @@
 #include <regex>
 #include <iostream>
 #include <iterator>
+#include <queue>
 #include <string>
 #include <math.h>
 
@@ -38,19 +39,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         } else if (arg == "-p" || arg == "--prompt") {
             params.prompt = argv[++i];
         } else if (arg == "-f" || arg == "--file") {
-
             std::ifstream file(argv[++i]);
-
-            std::copy(std::istreambuf_iterator<char>(file),
-                    std::istreambuf_iterator<char>(),
-                    back_inserter(params.prompt));
-                
+            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]);
         } else if (arg == "--top_k") {
             params.top_k = std::stoi(argv[++i]);
         } else if (arg == "-c" || arg == "--ctx_size") {
             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]);
         } else if (arg == "--temp") {
@@ -65,16 +66,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.model = argv[++i];
         } else if (arg == "-i" || arg == "--interactive") {
             params.interactive = true;
-        } else if (arg == "--interactive-start") {
-            params.interactive = true;
-            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 = argv[++i];
+            params.antiprompt.push_back(argv[++i]);
+        } else if (arg == "--ignore-eos") {
+            params.ignore_eos = true;
         } else if (arg == "-h" || arg == "--help") {
             gpt_print_usage(argc, argv, params);
             exit(0);
+        } else if (arg == "--random-prompt") {
+            params.random_prompt = true;
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             gpt_print_usage(argc, argv, params);
@@ -85,20 +89,22 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     return true;
 }
 
-void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
+void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "usage: %s [options]\n", argv[0]);
     fprintf(stderr, "\n");
     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-start   run in interactive mode and poll user input at startup\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\n");
+    fprintf(stderr, "                        in interactive mode, 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, "  -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: random)\n");
+    fprintf(stderr, "                        prompt to start generation with (default: empty)\n");
+    fprintf(stderr, "  --random-prompt       start with a randomized prompt.\n");
     fprintf(stderr, "  -f FNAME, --file FNAME\n");
     fprintf(stderr, "                        prompt file to start generation.\n");
     fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
@@ -107,6 +113,8 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --repeat_last_n N     last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
     fprintf(stderr, "  --repeat_penalty N    penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
     fprintf(stderr, "  -c N, --ctx_size N    size of the prompt context (default: %d)\n", params.n_ctx);
+    fprintf(stderr, "  --ignore-eos          ignore end of stream token and continue generating\n");
+    fprintf(stderr, "  --memory_f16          use f16 instead of f32 for memory key+value\n");
     fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
     fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  -m FNAME, --model FNAME\n");
@@ -287,58 +295,146 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
     return tokens;
 }
 
-// TODO: Calculate this constant from the vocabulary
-#define MAX_TOKEN_LEN 18
-// SentencePiece implementation after https://guillaume-be.github.io/2020-05-30/sentence_piece
-std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
-    std::vector<gpt_vocab::id> res;
-    std::vector<int> score;
-    std::vector<gpt_vocab::id> prev;
-    int len = text.length();
+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];
+}
 
-    score.resize(len + 1);
-    prev.resize(len + 1);
+struct llama_sp_symbol {
+    using index = int;
+    index prev;
+    index next;
+    std::string_view text;
+};
 
-    // Forward pass
-    for (int i = 0; i < len; i++) {
-        int max_len = std::min(len - i, MAX_TOKEN_LEN);
-        for (int sub_len = 1; sub_len <= max_len; sub_len++) {
-            auto sub = text.substr(i, sub_len);
-            auto token = vocab.token_to_id.find(sub);
-            if (token != vocab.token_to_id.end()) {
-                int token_score = sub.length() * sub.length();
-                int local_score = score[i] + token_score;
-                int next = i + sub_len;
-                if (score[next] < local_score) {
-                    score[next] = local_score;
-                    prev[next] = (*token).second;
+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);
             }
         }
     }
 
-    // Backward pass
-    int i = len;
-    while (i > 0) {
-        gpt_vocab::id token_id = prev[i];
-        if (token_id == 0) {
-	    // TODO: Return error or something more meaningful
-            printf("failed to tokenize string!\n");
-	    break;
+private:
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
         }
-        res.push_back(token_id);
-        auto token = (*vocab.id_to_token.find(token_id)).second;
-        i -= token.length();
+
+        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) {
-        res.push_back(1); // TODO: replace with vocab.bos
+        output.push_back(1);
     }
 
-    // Pieces are in reverse order so correct that
-    std::reverse(res.begin(), res.end());
-
-    return res;
+    tokenizer.tokenize(text, output);
+    return output;
 }
 
 bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
@@ -398,7 +494,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
                     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));
             }

utils.h

@@ -18,6 +18,7 @@ struct gpt_params {
     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
 
     // sampling parameters
     int32_t top_k = 40;
@@ -27,14 +28,18 @@ struct gpt_params {
 
     int32_t n_batch = 8; // batch size for prompt processing
 
-    std::string model = "models/lamma-7B/ggml-model.bin"; // model path
-    std::string prompt;
+    std::string model      = "models/lamma-7B/ggml-model.bin"; // model path
+    std::string prompt     = "";
+
+    bool random_prompt = false;
 
     bool use_color = false; // use color to distinguish generations and inputs
 
     bool interactive = false; // interactive mode
     bool interactive_start = false; // reverse prompt immediately
-    std::string antiprompt = ""; // string upon seeing which more user input is prompted
+    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 gpt_params_parse(int argc, char ** argv, gpt_params & params);
@@ -53,6 +58,7 @@ struct gpt_vocab {
 
     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);
@@ -74,7 +80,7 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
 
 // 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, const std::string & text, bool bos);
+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);