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llama : add simple-chat example (#10124)
* llama : add simple-chat example --------- Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
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‎Makefile

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@@ -34,6 +34,7 @@ BUILD_TARGETS = \
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llama-save-load-state \
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llama-server \
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llama-simple \
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llama-simple-chat \
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llama-speculative \
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llama-tokenize \
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llama-vdot \
@@ -1287,6 +1288,11 @@ llama-simple: examples/simple/simple.cpp \
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-simple-chat: examples/simple-chat/simple-chat.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
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llama-tokenize: examples/tokenize/tokenize.cpp \
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$(OBJ_ALL)
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

‎examples/CMakeLists.txt

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@@ -49,6 +49,7 @@ else()
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endif()
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add_subdirectory(save-load-state)
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add_subdirectory(simple)
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add_subdirectory(simple-chat)
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add_subdirectory(speculative)
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add_subdirectory(tokenize)
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endif()

‎examples/simple-chat/CMakeLists.txt

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set(TARGET llama-simple-chat)
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add_executable(${TARGET} simple-chat.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)

‎examples/simple-chat/README.md

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# llama.cpp/example/simple-chat
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The purpose of this example is to demonstrate a minimal usage of llama.cpp to create a simple chat program using the chat template from the GGUF file.
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```bash
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./llama-simple-chat -m Meta-Llama-3.1-8B-Instruct.gguf -c 2048
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...

‎examples/simple-chat/simple-chat.cpp

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#include "llama.h"
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#include <cstdio>
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#include <cstring>
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#include <iostream>
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#include <string>
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#include <vector>
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static void print_usage(int, char ** argv) {
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printf("\nexample usage:\n");
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printf("\n %s -m model.gguf [-c context_size] [-ngl n_gpu_layers]\n", argv[0]);
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printf("\n");
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}
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int main(int argc, char ** argv) {
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std::string model_path;
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int ngl = 99;
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int n_ctx = 2048;
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// parse command line arguments
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for (int i = 1; i < argc; i++) {
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try {
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if (strcmp(argv[i], "-m") == 0) {
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if (i + 1 < argc) {
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model_path = argv[++i];
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else if (strcmp(argv[i], "-c") == 0) {
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if (i + 1 < argc) {
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n_ctx = std::stoi(argv[++i]);
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else if (strcmp(argv[i], "-ngl") == 0) {
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if (i + 1 < argc) {
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ngl = std::stoi(argv[++i]);
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} catch (std::exception & e) {
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fprintf(stderr, "error: %s\n", e.what());
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print_usage(argc, argv);
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return 1;
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}
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}
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if (model_path.empty()) {
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print_usage(argc, argv);
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return 1;
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}
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// only print errors
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llama_log_set([](enum ggml_log_level level, const char * text, void * /* user_data */) {
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if (level >= GGML_LOG_LEVEL_ERROR) {
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fprintf(stderr, "%s", text);
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}
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}, nullptr);
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// initialize the model
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llama_model_params model_params = llama_model_default_params();
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model_params.n_gpu_layers = ngl;
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llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params);
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if (!model) {
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fprintf(stderr , "%s: error: unable to load model\n" , __func__);
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return 1;
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}
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// initialize the context
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.n_ctx = n_ctx;
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ctx_params.n_batch = n_ctx;
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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if (!ctx) {
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fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
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return 1;
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}
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// initialize the sampler
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llama_sampler * smpl = llama_sampler_chain_init(llama_sampler_chain_default_params());
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llama_sampler_chain_add(smpl, llama_sampler_init_min_p(0.05f, 1));
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llama_sampler_chain_add(smpl, llama_sampler_init_temp(0.8f));
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llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
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// helper function to evaluate a prompt and generate a response
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auto generate = [&](const std::string & prompt) {
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std::string response;
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// tokenize the prompt
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const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
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std::vector<llama_token> prompt_tokens(n_prompt_tokens);
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if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
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GGML_ABORT("failed to tokenize the prompt\n");
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}
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// prepare a batch for the prompt
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llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
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llama_token new_token_id;
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while (true) {
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// check if we have enough space in the context to evaluate this batch
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int n_ctx = llama_n_ctx(ctx);
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int n_ctx_used = llama_get_kv_cache_used_cells(ctx);
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if (n_ctx_used + batch.n_tokens > n_ctx) {
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printf("\033[0m\n");
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fprintf(stderr, "context size exceeded\n");
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exit(0);
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}
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if (llama_decode(ctx, batch)) {
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GGML_ABORT("failed to decode\n");
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}
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// sample the next token
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new_token_id = llama_sampler_sample(smpl, ctx, -1);
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// is it an end of generation?
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if (llama_token_is_eog(model, new_token_id)) {
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break;
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}
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// convert the token to a string, print it and add it to the response
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char buf[256];
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int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
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if (n < 0) {
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GGML_ABORT("failed to convert token to piece\n");
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}
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std::string piece(buf, n);
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printf("%s", piece.c_str());
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fflush(stdout);
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response += piece;
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// prepare the next batch with the sampled token
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batch = llama_batch_get_one(&new_token_id, 1);
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}
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return response;
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};
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std::vector<llama_chat_message> messages;
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std::vector<char> formatted(llama_n_ctx(ctx));
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int prev_len = 0;
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while (true) {
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// get user input
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printf("\033[32m> \033[0m");
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std::string user;
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std::getline(std::cin, user);
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if (user.empty()) {
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break;
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}
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// add the user input to the message list and format it
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messages.push_back({"user", strdup(user.c_str())});
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int new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
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if (new_len > (int)formatted.size()) {
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formatted.resize(new_len);
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new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size());
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}
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if (new_len < 0) {
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fprintf(stderr, "failed to apply the chat template\n");
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return 1;
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}
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// remove previous messages to obtain the prompt to generate the response
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std::string prompt(formatted.begin() + prev_len, formatted.begin() + new_len);
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// generate a response
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printf("\033[33m");
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std::string response = generate(prompt);
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printf("\n\033[0m");
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// add the response to the messages
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messages.push_back({"assistant", strdup(response.c_str())});
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prev_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), false, nullptr, 0);
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if (prev_len < 0) {
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fprintf(stderr, "failed to apply the chat template\n");
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return 1;
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}
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}
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// free resources
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for (auto & msg : messages) {
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free(const_cast<char *>(msg.content));
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}
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llama_sampler_free(smpl);
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llama_free(ctx);
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llama_free_model(model);
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return 0;
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}

‎ggml/include/ggml.h

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@@ -558,10 +558,10 @@ extern "C" {
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enum ggml_log_level {
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GGML_LOG_LEVEL_NONE = 0,
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GGML_LOG_LEVEL_INFO = 1,
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GGML_LOG_LEVEL_WARN = 2,
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GGML_LOG_LEVEL_ERROR = 3,
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GGML_LOG_LEVEL_DEBUG = 4,
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GGML_LOG_LEVEL_DEBUG = 1,
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GGML_LOG_LEVEL_INFO = 2,
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GGML_LOG_LEVEL_WARN = 3,
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GGML_LOG_LEVEL_ERROR = 4,
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GGML_LOG_LEVEL_CONT = 5, // continue previous log
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};
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