patches/llama/llama-repeng.diff

diff --git a/Makefile b/Makefile
index c0f12503..af4d2533 100644
--- a/Makefile
+++ b/Makefile
@@ -1,6 +1,6 @@
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
-	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+	main repeng quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
 	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
 
@@ -744,6 +744,10 @@ server: examples/server/server.cpp examples/server/utils.hpp examples/server/htt
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
+repeng: examples/repeng/repeng.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 350bbdf7..400ca44d 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -32,17 +32,19 @@ endif()
 
 # Add a custom command to rebuild build-info.cpp when .git/index changes
 add_custom_command(
-    OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp"
+    OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp"
     COMMENT "Generating build details from Git"
     COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
             -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
+            -DOUTPUT_DIR=${CMAKE_CURRENT_BINARY_DIR}/..
+            -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
     WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
     DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
     VERBATIM
 )
 set(TARGET build_info)
-add_library(${TARGET} OBJECT build-info.cpp)
+add_library(${TARGET} OBJECT "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp")
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index e762cf8b..d46d9d17 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -46,4 +46,5 @@ else()
         add_subdirectory(server)
     endif()
     add_subdirectory(export-lora)
+    add_subdirectory(repeng)
 endif()
diff --git a/examples/repeng/CMakeLists.txt b/examples/repeng/CMakeLists.txt
new file mode 100644
index 00000000..9e20f806
--- /dev/null
+++ b/examples/repeng/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET repeng)
+add_executable(${TARGET} repeng.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/repeng/repeng.cpp b/examples/repeng/repeng.cpp
new file mode 100644
index 00000000..a1d5ed78
--- /dev/null
+++ b/examples/repeng/repeng.cpp
@@ -0,0 +1,892 @@
+#include "common.h"
+
+#include "console.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <vector>
+#include <deque>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
+static bool is_interacting = false;
+
+static bool file_exists(const std::string &path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string &path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (!is_interacting && g_params->interactive) {
+            is_interacting = true;
+        } else {
+            console::cleanup();
+            printf("\n");
+            llama_print_timings(*g_ctx);
+            //write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
+            _exit(130);
+        }
+    }
+}
+#endif
+
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval(
+    gpt_params & params,
+    ggml_backend_sched_eval_callback cb_eval,
+    void * cb_eval_user_data) {
+    auto mparams = llama_model_params_from_gpt_params(params);
+
+    llama_model * model  = llama_load_model_from_file(params.model.c_str(), mparams);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    auto cparams = llama_context_params_from_gpt_params(params);
+
+    cparams.cb_eval = cb_eval;
+    cparams.cb_eval_user_data = cb_eval_user_data;
+
+    llama_context * lctx = llama_new_context_with_model(model, cparams);
+    if (lctx == NULL) {
+        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
+        llama_free_model(model);
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
+        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
+        float lora_scale = std::get<1>(params.lora_adapter[i]);
+        int err = llama_model_apply_lora_from_file(model,
+                                             lora_adapter.c_str(),
+                                             lora_scale,
+                                             ((i > 0) || params.lora_base.empty())
+                                                ? NULL
+                                                : params.lora_base.c_str(),
+                                             params.n_threads);
+        if (err != 0) {
+            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    if (params.ignore_eos) {
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+    }
+
+    {
+        LOG("warming up the model with an empty run\n");
+
+        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
+        llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
+        llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
+        llama_reset_timings(lctx);
+    }
+
+    return std::make_tuple(model, lctx);
+}
+
+struct eval_callback_state {
+    std::vector<ggml_tensor *> tensors;
+    // For each hidden state tensor, how many tokens have we seen from the
+    // current batch?  When n_ubatch < n_batch, we don't see the hidden states
+    // for all tokens at once, but only n_ubatch-sized chunks of tokens.  This
+    // keeps track of our progress.
+    std::vector<int> tokens_seen;
+    int first_prompt_idx;
+    std::vector<int> extract_tokens;
+};
+
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) {
+    struct eval_callback_state * eval_state = (eval_callback_state *)user_data;
+    if (ask) {
+        // Report whether we want to observe this tensor.
+        if (strncmp(t->name, "l_out-", 6) == 0) {
+            return true;
+        } else {
+            return false;
+        }
+    } else {
+        // Actually observe the tensor data.
+
+        if (eval_state->first_prompt_idx >= 0) {
+            // Find the tensor collecting hidden states for the current layer.
+            int layer_idx = -1;
+            ggml_tensor * output_tensor = nullptr;
+            for (size_t i = 0; i < eval_state->tensors.size(); ++i) {
+                auto t2 = eval_state->tensors[i];
+                if (strcmp(t2->name, t->name) == 0) {
+                    output_tensor = t2;
+                    layer_idx = i;
+                    break;
+                }
+            }
+
+            if (output_tensor != nullptr) {
+                int ubatch_size = t->ne[1];
+                int ubatch_start = eval_state->tokens_seen[layer_idx];
+                int ubatch_end = ubatch_start + ubatch_size;
+                eval_state->tokens_seen[layer_idx] += ubatch_size;
+
+                int output_idx = eval_state->first_prompt_idx;
+                for (int token_idx : eval_state->extract_tokens) {
+                    if (token_idx < ubatch_start || token_idx >= ubatch_end) {
+                        ++output_idx;
+                        continue;
+                    }
+                    int input_idx = token_idx - ubatch_start;
+
+                    // Copy the hidden states for the current token into the
+                    // output buffer.
+                    size_t input_offset = t->nb[1] * input_idx;
+                    size_t output_offset = output_tensor->nb[1] * output_idx;
+                    assert(t->nb[0] == output_tensor->nb[0]);
+                    assert(t->ne[0] == output_tensor->ne[0]);
+                    ggml_backend_tensor_get(t,
+                            (char *)output_tensor->data + output_offset,
+                            input_offset,
+                            t->nb[0] * t->ne[0]);
+                    //memcpy((char *)output_tensor->data + output_offset,
+                    //        (char *)t->data + input_offset,
+                    //        t->nb[0] * t->ne[0]);
+                    //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data "
+                    //    << " for " << t->name << " in slot " << output_idx << "\n";
+
+                    //float * buf = (float *)((char *)t->data + input_offset);
+                    //float * buf = (float *)((char *)output_tensor->data + output_offset);
+                    //std::cerr << "prompt " << output_idx
+                    //    << " tensor contents for " << t->name << ": "
+                    //    << buf[0] << ", "
+                    //    << buf[1] << ", "
+                    //    << buf[2] << " ... "
+                    //    << buf[4093] << ", "
+                    //    << buf[4094] << ", "
+                    //    << buf[4095] << "\n";
+
+                    ++output_idx;
+                }
+            }
+        }
+
+        // Continue running
+        return true;
+    }
+}
+
+struct kv_trie_node {
+    int seq;
+    // Flag to indicate whether the current token (that is, the last token of
+    // the current sequence) has already been retained in the KV cache.  When a
+    // token from the old batch is reused for several different prompts in the
+    // new batch, we use this flag to ensure that it's only counted once
+    // against the batch's token limit.
+    bool retained;
+    // Map from token ID to the node representing that token appended to the
+    // current sequence.  For example, `root.children[3].children[4]` gives the
+    // node for the token `4` in the sequence `3, 4`; its `seq` value indicates
+    // a sequence in the KV cache that has `3, 4` as its prefix.
+    std::map<int, kv_trie_node> children;
+
+    kv_trie_node(int seq) : seq(seq), retained(false), children() {}
+};
+
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id);
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq);
+
+// Given a `node` representing some sequence, gets the `kv_trie_node`
+// representing that sequence extended with `token_id`.  Returns `nullptr` if
+// the extended sequence is not in the trie, or if `node` itself is `nullptr`.
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id) {
+    if (node == nullptr) {
+        return nullptr;
+    }
+    auto iter = node->children.find(token_id);
+    if (iter == node->children.end()) {
+        return nullptr;
+    } else {
+        return &iter->second;
+    }
+}
+
+// Given a `node` representing some sequence, insert a `kv_trie_node`
+// representing that sequence extended with `token_id`, with `seq` as the
+// sequence ID.  If the extended sequence is already present in the trie, this
+// does nothing.
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq) {
+    auto result = node->children.insert(std::make_pair(token_id, kv_trie_node(seq)));
+    return &result.first->second;
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    g_params = &params;
+
+    bool mf = false;
+    char *farg;
+    std::deque<char *> ff;
+    int findex = -1;
+    for (int i=0; i<argc; i++) {
+      if (strcmp(argv[i], "-f") == 0) {
+        if (++i >= argc) {
+          exit(1337);
+          break;
+        }
+        findex = i;
+        farg = argv[i];
+        for (int j=0; argv[i][j] != '\0'; j++) {
+          if (argv[i][j] == ',') {
+            argv[i][j] = '\0';
+            mf = true;
+            ff.push_back(&argv[i][j+1]);
+          }
+        }
+        break;
+      }
+    }
+
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+    llama_sampling_params & sparams = params.sparams;
+
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("main", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
+    // TODO: Dump params ?
+    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
+
+    // save choice to use color for later
+    // (note for later: this is a slightly awkward choice)
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    if (params.logits_all) {
+        printf("\n************\n");
+        printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.embedding) {
+        printf("\n************\n");
+        printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.n_ctx != 0 && params.n_ctx < 8) {
+        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        params.n_ctx = 8;
+    }
+
+    if (params.rope_freq_base != 0.0) {
+        LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 0.0) {
+        LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
+    }
+
+    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    LOG("%s: llama backend init\n", __func__);
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_model *preloaded = NULL;
+    llama_context * ctx;
+    llama_context * ctx_guidance = NULL;
+    g_model = &model;
+    g_ctx = &ctx;
+
+    ggml_context * eval_ctx = nullptr;
+    struct eval_callback_state eval_state;
+
+    // load the model and apply lora adapter, if any
+    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
+    std::tie(model, ctx) = llama_init_from_gpt_params_with_cb_eval(
+        params,
+        eval_callback,
+        (void *)&eval_state);
+    /*
+    if (sparams.cfg_scale > 1.f) {
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        ctx_guidance = llama_new_context_with_model(model, lparams);
+    }
+    */
+
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+    LOG_TEE("n_ctx: %d\n", n_ctx);
+
+    if (n_ctx > n_ctx_train) {
+        LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, n_ctx);
+    }
+//STARTYO
+    do {
+
+      // print system information
+      {
+          LOG_TEE("\n");
+          LOG_TEE("%s\n", get_system_info(params).c_str());
+      }
+
+
+      const bool add_bos = llama_should_add_bos_token(model);
+    //  LOG_TEE("add_bos: %d\n", add_bos);
+
+      std::vector<llama_token> embd_inp;
+
+      
+     /// std::cerr << "tokenize the prompt" << params.prompt << "\n";
+      if (params.chatml) {
+          params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+      }
+   //   std::cerr << "tokenize bitch\n";
+      embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+   //   std::cerr << "yodl";
+   //   LOG_TEE("prompt: \"%s\"\n", log_tostr(params.prompt));
+   //   LOG_TEE("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+
+      // Should not run without any tokens
+      if (embd_inp.empty()) {
+          embd_inp.push_back(llama_token_bos(model));
+          LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+      }
+
+      // Tokenize negative prompt
+      std::vector<llama_token> guidance_inp;
+      int guidance_offset = 0;
+      int original_prompt_len = 0;
+      /*
+      if (ctx_guidance) {
+          LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
+
+          guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
+          LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
+
+          std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+          LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
+
+          original_prompt_len = original_inp.size();
+          guidance_offset = (int)guidance_inp.size() - original_prompt_len;
+          LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
+          LOG("guidance_offset:     %s", log_tostr(guidance_offset));
+      }
+      */
+
+      /*
+      if ((int) embd_inp.size() > n_ctx - 4) {
+          LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
+          return 1;
+      }
+      */
+
+
+      // number of tokens to keep when resetting context
+      if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
+          params.n_keep = (int)embd_inp.size();
+      } else {
+          params.n_keep += add_bos; // always keep the BOS token
+      }
+
+      // prefix & suffix for instruct mode
+      const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
+      const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
+
+      LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
+      LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
+
+      // chatml prefix & suffix
+      const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
+      const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
+
+      LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
+      LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
+
+      // in instruct mode, we inject a prefix and a suffix to each input by the user
+      if (params.instruct) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("### Instruction:\n\n");
+      }
+      // similar for chatml mode
+      else if (params.chatml) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("<|im_start|>user\n");
+      }
+
+      // enable interactive mode if interactive start is specified
+      if (params.interactive_first) {
+          params.interactive = true;
+      }
+
+      if (params.verbose_prompt) {
+          LOG_TEE("\n");
+          LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+          LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+          for (int i = 0; i < (int) embd_inp.size(); i++) {
+              LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
+          }
+
+          if (params.n_keep > add_bos) {
+              LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+              for (int i = 0; i < params.n_keep; i++) {
+                  LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+              }
+              LOG_TEE("'\n");
+          }
+          LOG_TEE("\n");
+      }
+
+      // ctrl+C handling
+      {
+  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+          struct sigaction sigint_action;
+          sigint_action.sa_handler = sigint_handler;
+          sigemptyset (&sigint_action.sa_mask);
+          sigint_action.sa_flags = 0;
+          sigaction(SIGINT, &sigint_action, NULL);
+  #elif defined (_WIN32)
+          auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+              return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+          };
+          SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+  #endif
+      }
+
+      LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
+      LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
+      LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+      // group-attention state
+      // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+      int ga_i = 0;
+
+      const int ga_n = params.grp_attn_n;
+      const int ga_w = params.grp_attn_w;
+
+      if (ga_n != 1) {
+          GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+          GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+        //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+        //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+          LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+      }
+      LOG_TEE("\n\n");
+
+      bool is_antiprompt        = false;
+      bool input_echo           = true;
+      bool display              = true;
+
+      int n_past             = 0;
+      int n_remain           = params.n_predict;
+      unsigned n_consumed    = 0;
+      int n_past_guidance    = 0;
+
+      std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+      std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+      std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
+      // the first thing we will do is to output the prompt, so set color accordingly
+      console::set_display(console::prompt);
+      display = params.display_prompt;
+
+      std::vector<llama_token> embd;
+      std::vector<llama_token> embd_guidance;
+
+      // tokenized antiprompts
+      std::vector<std::vector<llama_token>> antiprompt_ids;
+
+      antiprompt_ids.reserve(params.antiprompt.size());
+      for (const std::string & antiprompt : params.antiprompt) {
+          antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+      }
+
+
+      // Tokenized prompt is in embd_inp
+
+
+      // Record prompt boundaries
+      const int PROMPT_DELIMITER_TOKEN = 13;
+
+      // Index of each delimiter token in `embd_inp`.  These mark the end of each
+      // prompt.
+      std::vector<size_t> delim_idxs;
+
+      for (size_t i = 0; i < embd_inp.size(); ++i) {
+        //std::cerr << "TOKEN " << embd_inp[i];
+          if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) {
+              delim_idxs.push_back(i);
+          }
+      }
+
+      // If the last prompt is missing an ending delimiter, add it.
+      if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) {
+          delim_idxs.push_back(embd_inp.size());
+          embd_inp.push_back(PROMPT_DELIMITER_TOKEN);
+      }
+
+      size_t num_prompts = delim_idxs.size();
+
+LOG_TEE("NIGGER");
+      // Set up eval_state
+      gguf_context * eval_gguf = gguf_init_empty();
+      {
+          int n_embd = llama_n_embd(model);
+          int n_layer = llama_n_layer(model);
+          std::cerr << "build eval state: " << num_prompts << " prompts, "
+              << n_embd << " embd, " << n_layer << " layers\n";
+
+          struct ggml_init_params params = {};
+          params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer;
+          eval_ctx = ggml_init(params);
+
+          for (int i = 0; i < n_layer; ++i) {
+              ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts);
+              snprintf(t->name, sizeof(t->name), "l_out-%d", i);
+              eval_state.tensors.push_back(t);
+              gguf_add_tensor(eval_gguf, t);
+          }
+          eval_state.first_prompt_idx = -1;
+      }
+
+
+
+      // Max tokens to include in a single batch.
+      int batch_max_tokens = llama_n_batch(ctx);
+      unsigned batch_max_seq = llama_n_seq_max(ctx);
+
+      std::cerr << "batch_max_tokens " << batch_max_tokens << "\n";
+      std::cerr << "batch_max_seq " << batch_max_seq << "\n";
+
+      struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq);
+
+      size_t prompt_idx = 0;
+      size_t batch_idx = 0;
+      // For each (token_id, pos) pair, these maps record the first sequence to
+      // contain that pair in the old and new states of the KV cache.
+      std::unique_ptr<kv_trie_node> old_kv_trie(new kv_trie_node(-1));
+      std::unique_ptr<kv_trie_node> new_kv_trie(new kv_trie_node(-1));
+          auto last = ggml_time_ms();
+      while (prompt_idx < num_prompts) {
+          std::cerr << "start batch at " << prompt_idx << "\n";
+          eval_state.first_prompt_idx = prompt_idx;
+          eval_state.extract_tokens.clear();
+          // Reset `tokens_seen` to zero for all layers.
+          eval_state.tokens_seen.clear();
+          eval_state.tokens_seen.resize(eval_state.tensors.size(), 0);
+
+          old_kv_trie = std::move(new_kv_trie);
+          new_kv_trie.reset(new kv_trie_node(-1));
+
+    //      std::cerr << "WHERE IS OUR OOM? " << prompt_idx << "\n";
+          // Clear the token batch.
+          batch.n_tokens = 0;
+          size_t context_used = 0;
+
+          llama_sampling_reset(ctx_sampling);
+
+          // Add prompts to the batch until it's full.
+          // We alternate between using sequence IDs [0, max/2) and [max/2, max).
+          // This ensures that old sequences and new sequences never collide.
+          unsigned first_seq = batch_idx % 2 == 0 ? 0 : batch_max_seq / 2;
+          unsigned last_seq = first_seq + batch_max_seq / 2;
+          unsigned cur_seq = first_seq;
+          unsigned kv_cache_space = n_ctx;
+          while (prompt_idx < num_prompts && cur_seq < last_seq) {
+  //            std::cerr << "WHERE IS OUR OOM now? " << prompt_idx << "\n";
+
+              size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1;
+              size_t end = delim_idxs[prompt_idx];
+              GGML_ASSERT(end > start && "empty prompts are not allowed");
+              std::cerr << "CHECKING BOUNDS\nstart: " << start << "\n" << "end: " << end << "\n" << "kv_cache_space: " << kv_cache_space << "\n";
+              std::cerr << "adding " << start << " .. " << end
+                  << " (" << (end - start) << " tokens); "
+                  << context_used << " / " << batch_max_tokens << " context used\n";
+
+              if (end - start > kv_cache_space) {
+
+                std::cerr << "biggernitch!\n";
+                  // Not enough space remaining in the batch, if we hit the worst
+                  // case where the prompt consists entirely of new tokens.
+                  break;
+              }
+
+            //    std::cerr << "gettheniggernodes!\n";
+              kv_trie_node * old_node = old_kv_trie.get();
+              kv_trie_node * new_node = new_kv_trie.get();
+            //    std::cerr << "g0ttheniggernodes!\n";
+
+              for (size_t j = start; j < end; ++j) {
+                  int id = embd_inp[j];
+                  int pos = j - start;
+
+            //    std::cerr << "lemmetrytofindthatnode!\n";
+                  old_node = kv_trie_find(old_node, id);
+                  // Regardless of how we handle the token, it will ultimately be
+                  // present at position `j` in sequence `cur_seq`.
+            //    std::cerr << "lemmetrytoINSERTthatnode!\n";
+                  new_node = kv_trie_insert(new_node, id, cur_seq);
+            //    std::cerr << "node business going on!\n";
+                  if (old_node == nullptr) {
+                      // Add a new token.
+            //    std::cerr << "add that token to the batch y0!\n";
+                      llama_batch_add(batch, id, pos, {(int)cur_seq}, false);
+                      --kv_cache_space;
+                      //const std::string token_str = llama_token_to_piece(ctx, id);
+                      //LOG_TEE("add '%s' (%d), %d to new sequence %d (fresh)\n",
+                      //        token_str.c_str(), id, pos, cur_seq);
+                  } else {
+            //    std::cerr << "WE COPY SOME SHIT HERE.\n";
+                      llama_kv_cache_seq_cp(ctx, old_node->seq, cur_seq, pos, pos + 1);
+                      //const std::string token_str = llama_token_to_piece(ctx, id);
+                      //LOG_TEE("add '%s' (%d), %d to new sequence %d, from old sequence %d\n",
+                      //        token_str.c_str(), id, pos, cur_seq, old_seq);
+                      if (!old_node->retained) {
+                          // This token was previously going to be discarded.
+                          // Now that it's being kept, we have to count it
+                          // against `kv_cache_space`.
+                          old_node->retained = true;
+                          --kv_cache_space;
+                          //LOG_TEE("REUSED '%s' (%d), %d\n", token_str.c_str(), id, pos);
+                      }
+                  }
+              }
+
+        //        std::cerr << "Push it back, Jamal.\n";
+              eval_state.extract_tokens.push_back(batch.n_tokens - 1);
+
+              ++prompt_idx;
+              ++cur_seq;
+          }
+
+
+        //  std::cerr << "kv cache business going on.\n";
+          for (unsigned seq = 0; seq < batch_max_seq; ++seq) {
+              if (seq < first_seq || seq >= last_seq) {
+                  llama_kv_cache_seq_rm(ctx, seq, -1, -1);
+              }
+          }
+        //  std::cerr << "kv cache business went ok.\n";
+
+          // Force defragmentation of the KV cache.  `llama_decode` needs a
+          // contiguous block of `batch.n_tokens` cache slots, which it won't be
+          // able to find if the cache is too fragmented.  Since we build batches
+          // so as to maximize cache/context utilization, any fragmentation at
+          // all will usually cause it to fail.
+          //
+          // FIXME: This sometimes doesn't fully defragment the cache, as shown
+          // by `llama_kv_cache_view` debugging stats: if all free space was
+          // contiguous, then `max_contiguous` should equal the number of free
+          // cells (`n_cells - used_cells`), but often this is not the case.
+
+          //std::cerr << "defrag kv cache\n";
+          llama_kv_cache_defrag(ctx);
+          //std::cerr << "UPD0000T kv cache\n";
+          llama_kv_cache_update(ctx);
+          //std::cerr << "defragGGGED kv cache\n";
+
+          for (int i = 0; i < 10; ++i) {
+              // Debug prints to check cache usage and fragmentation:
+              auto view = llama_kv_cache_view_init(ctx, 1);
+              llama_kv_cache_view_update(ctx, &view);
+            //  std::cerr << "kv cache cells: " << view.n_cells << "\n";
+            //  std::cerr << "kv cache tokens: " << view.token_count << "\n";
+            //  std::cerr << "kv cache used: " << view.used_cells << "\n";
+            //  std::cerr << "kv cache contiguous free space: "
+            //      << view.max_contiguous << " / "
+            //      << (view.n_cells - view.used_cells) << "\n";
+              if (view.max_contiguous < view.n_cells - view.used_cells) {
+                  //std::cerr << "defrag didn't work! trying again...\n";
+                  llama_kv_cache_defrag(ctx);
+                  llama_kv_cache_update(ctx);
+              } else {
+                  break;
+              }
+          }
+
+          GGML_ASSERT(batch.n_tokens > 0);
+
+          std::cerr << "batch " << eval_state.first_prompt_idx << ": "
+              << (prompt_idx - eval_state.first_prompt_idx) << " prompts, "
+              << batch.n_tokens << " new tokens, "
+              << context_used << " total tokens\n";
+
+
+  //        fprintf(stderr, "Let us OOM now.\n");
+          if (llama_decode(ctx, batch)) {
+              LOG_TEE("%s : failed to eval\n", __func__);
+              return 1;
+          }
+    //      fprintf(stderr, "We OOMed not.\n");
+
+          auto now = ggml_time_ms();
+          auto timedelta = now - last;
+          last = now;
+          std::cerr << "time delta: " << timedelta << "ms\n";
+      //    fprintf(stderr, "We OOMed still not.\n");
+          ++batch_idx;
+      }
+      
+
+      //fprintf(stderr, "We DID NOT 00M.\n");
+      llama_print_timings(ctx);
+      //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
+      //if (ctx_guidance) { llama_free(ctx_guidance); }
+
+        char *fname = farg, *fe = NULL;
+        for (char *fn = fname; *fn != '\0'; fn++) {
+          if (*fn == '/')
+            fname = &fn[1];
+          if (*fn == '_')
+            fe = fn;
+        }
+        if (fe > fname) {
+          strcpy(fe, "_data.gguf");
+        }
+        fprintf(stderr, "Save the file, nigger.\n");
+        fprintf(stderr, "Save the file to %s nigger.\n", fname);
+          auto now = ggml_time_ms();
+          gguf_write_to_file(eval_gguf, fname, false);
+
+          auto nowa = ggml_time_ms();
+          auto timedeltaa = nowa - now;
+        fprintf(stderr, "Saving took: %dms.\n", timedeltaa);
+
+      llama_free(ctx);
+      ggml_free(eval_ctx);
+      llama_sampling_free(ctx_sampling);
+      gguf_free(eval_gguf);
+      if (ff.size()) {
+        farg = ff.front();
+        std::ifstream file(farg);
+        if (!file) {
+          fprintf(stderr, "error: failed to open file '%s'\n", farg);
+          exit(1337);
+        }
+
+        // store the external file name in params
+        params.prompt_file = farg;
+        params.prompt.clear();
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+        if (!params.prompt.empty() && params.prompt.back() == '\n') {
+          params.prompt.pop_back();
+        }
+
+        ff.pop_front();
+      } else break;
+    } while (true);
+//ENDYO
+
+    llama_free_model(model);
+
+    llama_backend_free();
+
+#ifndef LOG_DISABLE_LOGS
+    LOG_TEE("Log end\n");
+#endif // LOG_DISABLE_LOGS
+
+    return 0;
+}
diff --git a/examples/repeng/repengskowski.cpp b/examples/repeng/repengskowski.cpp
new file mode 100644
index 00000000..2798db3d
--- /dev/null
+++ b/examples/repeng/repengskowski.cpp
@@ -0,0 +1,956 @@
+#include "common.h"
+
+#include "console.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <vector>
+#include <deque>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
+static bool is_interacting = false;
+
+static bool file_exists(const std::string &path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string &path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (!is_interacting && g_params->interactive) {
+            is_interacting = true;
+        } else {
+            console::cleanup();
+            printf("\n");
+            llama_print_timings(*g_ctx);
+            //write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
+            _exit(130);
+        }
+    }
+}
+#endif
+
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval(
+    gpt_params & params,
+    ggml_backend_sched_eval_callback cb_eval,
+    void * cb_eval_user_data) {
+    auto mparams = llama_model_params_from_gpt_params(params);
+
+    llama_model * model  = llama_load_model_from_file(params.model.c_str(), mparams);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    auto cparams = llama_context_params_from_gpt_params(params);
+
+    cparams.cb_eval = cb_eval;
+    cparams.cb_eval_user_data = cb_eval_user_data;
+
+    llama_context * lctx = llama_new_context_with_model(model, cparams);
+    if (lctx == NULL) {
+        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
+        llama_free_model(model);
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
+        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
+        float lora_scale = std::get<1>(params.lora_adapter[i]);
+        int err = llama_model_apply_lora_from_file(model,
+                                             lora_adapter.c_str(),
+                                             lora_scale,
+                                             ((i > 0) || params.lora_base.empty())
+                                                ? NULL
+                                                : params.lora_base.c_str(),
+                                             params.n_threads);
+        if (err != 0) {
+            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    if (params.ignore_eos) {
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+    }
+
+    {
+        LOG("warming up the model with an empty run\n");
+
+        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
+        llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
+        llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
+        llama_reset_timings(lctx);
+    }
+
+    return std::make_tuple(model, lctx);
+}
+
+struct eval_callback_state {
+    std::vector<ggml_tensor *> tensors;
+    // For each hidden state tensor, how many tokens have we seen from the
+    // current batch?  When n_ubatch < n_batch, we don't see the hidden states
+    // for all tokens at once, but only n_ubatch-sized chunks of tokens.  This
+    // keeps track of our progress.
+    std::vector<int> tokens_seen;
+    int first_prompt_idx;
+    std::vector<int> extract_tokens;
+};
+
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) {
+    struct eval_callback_state * eval_state = (eval_callback_state *)user_data;
+    if (ask) {
+        // Report whether we want to observe this tensor.
+        if (strncmp(t->name, "l_out-", 6) == 0) {
+            return true;
+        } else {
+            return false;
+        }
+    } else {
+        // Actually observe the tensor data.
+
+        if (eval_state->first_prompt_idx >= 0) {
+            // Find the tensor collecting hidden states for the current layer.
+            int layer_idx = -1;
+            ggml_tensor * output_tensor = nullptr;
+            for (size_t i = 0; i < eval_state->tensors.size(); ++i) {
+                auto t2 = eval_state->tensors[i];
+                if (strcmp(t2->name, t->name) == 0) {
+                    output_tensor = t2;
+                    layer_idx = i;
+                    break;
+                }
+            }
+
+            if (output_tensor != nullptr) {
+                int ubatch_size = t->ne[1];
+                int ubatch_start = eval_state->tokens_seen[layer_idx];
+                int ubatch_end = ubatch_start + ubatch_size;
+                eval_state->tokens_seen[layer_idx] += ubatch_size;
+
+                int output_idx = eval_state->first_prompt_idx;
+                for (int token_idx : eval_state->extract_tokens) {
+                    if (token_idx < ubatch_start || token_idx >= ubatch_end) {
+                        ++output_idx;
+                        continue;
+                    }
+                    int input_idx = token_idx - ubatch_start;
+
+                    // Copy the hidden states for the current token into the
+                    // output buffer.
+                    size_t input_offset = t->nb[1] * input_idx;
+                    size_t output_offset = output_tensor->nb[1] * output_idx;
+                    assert(t->nb[0] == output_tensor->nb[0]);
+                    assert(t->ne[0] == output_tensor->ne[0]);
+                    ggml_backend_tensor_get(t,
+                            (char *)output_tensor->data + output_offset,
+                            input_offset,
+                            t->nb[0] * t->ne[0]);
+                    //memcpy((char *)output_tensor->data + output_offset,
+                    //        (char *)t->data + input_offset,
+                    //        t->nb[0] * t->ne[0]);
+                    //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data "
+                    //    << " for " << t->name << " in slot " << output_idx << "\n";
+
+                    //float * buf = (float *)((char *)t->data + input_offset);
+                    //float * buf = (float *)((char *)output_tensor->data + output_offset);
+                    //std::cerr << "prompt " << output_idx
+                    //    << " tensor contents for " << t->name << ": "
+                    //    << buf[0] << ", "
+                    //    << buf[1] << ", "
+                    //    << buf[2] << " ... "
+                    //    << buf[4093] << ", "
+                    //    << buf[4094] << ", "
+                    //    << buf[4095] << "\n";
+
+                    ++output_idx;
+                }
+            }
+        }
+
+        // Continue running
+        return true;
+    }
+}
+
+struct kv_trie_node {
+    int seq;
+    // Flag to indicate whether the current token (that is, the last token of
+    // the current sequence) has already been retained in the KV cache.  When a
+    // token from the old batch is reused for several different prompts in the
+    // new batch, we use this flag to ensure that it's only counted once
+    // against the batch's token limit.
+    bool retained;
+    // Map from token ID to the node representing that token appended to the
+    // current sequence.  For example, `root.children[3].children[4]` gives the
+    // node for the token `4` in the sequence `3, 4`; its `seq` value indicates
+    // a sequence in the KV cache that has `3, 4` as its prefix.
+    std::map<int, kv_trie_node> children;
+
+    kv_trie_node(int seq) : seq(seq), retained(false), children() {}
+};
+
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id);
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq);
+
+// Given a `node` representing some sequence, gets the `kv_trie_node`
+// representing that sequence extended with `token_id`.  Returns `nullptr` if
+// the extended sequence is not in the trie, or if `node` itself is `nullptr`.
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id) {
+    if (node == nullptr) {
+        return nullptr;
+    }
+    auto iter = node->children.find(token_id);
+    if (iter == node->children.end()) {
+        return nullptr;
+    } else {
+        return &iter->second;
+    }
+}
+
+// Given a `node` representing some sequence, insert a `kv_trie_node`
+// representing that sequence extended with `token_id`, with `seq` as the
+// sequence ID.  If the extended sequence is already present in the trie, this
+// does nothing.
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq) {
+    auto result = node->children.insert(std::make_pair(token_id, kv_trie_node(seq)));
+    return &result.first->second;
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    g_params = &params;
+
+    bool mf = false;
+    char *farg;
+    std::deque<char *> ff;
+    int findex = -1;
+    for (int i=0; i<argc; i++) {
+      if (strcmp(argv[i], "-f") == 0) {
+        if (++i >= argc) {
+          exit(1337);
+          break;
+        }
+        findex = i;
+        farg = argv[i];
+        for (int j=0; argv[i][j] != '\0'; j++) {
+          if (argv[i][j] == ',') {
+            argv[i][j] = '\0';
+            mf = true;
+            ff.push_back(&argv[i][j+1]);
+          }
+        }
+        break;
+      }
+    }
+
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+    llama_sampling_params & sparams = params.sparams;
+
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("main", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
+    // TODO: Dump params ?
+    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
+
+    // save choice to use color for later
+    // (note for later: this is a slightly awkward choice)
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    if (params.logits_all) {
+        printf("\n************\n");
+        printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.embedding) {
+        printf("\n************\n");
+        printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.n_ctx != 0 && params.n_ctx < 8) {
+        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        params.n_ctx = 8;
+    }
+
+    if (params.rope_freq_base != 0.0) {
+        LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 0.0) {
+        LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
+    }
+
+    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    LOG("%s: llama backend init\n", __func__);
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_model *preloaded = NULL;
+    llama_context * ctx;
+    llama_context * ctx_guidance = NULL;
+    g_model = &model;
+    g_ctx = &ctx;
+
+    ggml_context * eval_ctx = nullptr;
+    struct eval_callback_state eval_state;
+
+    // load the model and apply lora adapter, if any
+    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
+    std::tie(model, ctx) = llama_init_from_gpt_params_with_cb_eval(
+        params,
+        eval_callback,
+        (void *)&eval_state);
+    /*
+    if (sparams.cfg_scale > 1.f) {
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        ctx_guidance = llama_new_context_with_model(model, lparams);
+    }
+    */
+
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+    LOG("n_ctx: %d\n", n_ctx);
+
+    if (n_ctx > n_ctx_train) {
+        LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, n_ctx);
+    }
+//STARTYO
+    do {
+
+      // print system information
+      {
+          LOG_TEE("\n");
+          LOG_TEE("%s\n", get_system_info(params).c_str());
+      }
+
+
+      const bool add_bos = llama_should_add_bos_token(model);
+      LOG_TEE("add_bos: %d\n", add_bos);
+
+      std::vector<llama_token> embd_inp;
+
+      
+      std::cerr << "tokenize the prompt" << params.prompt << "\n";
+      if (params.chatml) {
+          params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+      }
+      std::cerr << "tokenize bitch";
+      embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+      std::cerr << "yodl";
+      LOG_TEE("prompt: \"%s\"\n", log_tostr(params.prompt));
+      LOG_TEE("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+
+      // Should not run without any tokens
+      if (embd_inp.empty()) {
+          embd_inp.push_back(llama_token_bos(model));
+          LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+      }
+
+      // Tokenize negative prompt
+      std::vector<llama_token> guidance_inp;
+      int guidance_offset = 0;
+      int original_prompt_len = 0;
+      /*
+      if (ctx_guidance) {
+          LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
+
+          guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
+          LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
+
+          std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+          LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
+
+          original_prompt_len = original_inp.size();
+          guidance_offset = (int)guidance_inp.size() - original_prompt_len;
+          LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
+          LOG("guidance_offset:     %s", log_tostr(guidance_offset));
+      }
+      */
+
+      /*
+      if ((int) embd_inp.size() > n_ctx - 4) {
+          LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
+          return 1;
+      }
+      */
+
+
+      // number of tokens to keep when resetting context
+      if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
+          params.n_keep = (int)embd_inp.size();
+      } else {
+          params.n_keep += add_bos; // always keep the BOS token
+      }
+
+      // prefix & suffix for instruct mode
+      const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
+      const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
+
+      LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
+      LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
+
+      // chatml prefix & suffix
+      const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
+      const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
+
+      LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
+      LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
+
+      // in instruct mode, we inject a prefix and a suffix to each input by the user
+      if (params.instruct) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("### Instruction:\n\n");
+      }
+      // similar for chatml mode
+      else if (params.chatml) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("<|im_start|>user\n");
+      }
+
+      // enable interactive mode if interactive start is specified
+      if (params.interactive_first) {
+          params.interactive = true;
+      }
+
+      if (params.verbose_prompt) {
+          LOG_TEE("\n");
+          LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+          LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+          for (int i = 0; i < (int) embd_inp.size(); i++) {
+              LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
+          }
+
+          /*
+          if (ctx_guidance) {
+              LOG_TEE("\n");
+              LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
+              LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
+              for (int i = 0; i < (int) guidance_inp.size(); i++) {
+                  LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
+              }
+          }
+          */
+
+          if (params.n_keep > add_bos) {
+              LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+              for (int i = 0; i < params.n_keep; i++) {
+                  LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+              }
+              LOG_TEE("'\n");
+          }
+          LOG_TEE("\n");
+      }
+
+      // ctrl+C handling
+      {
+  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+          struct sigaction sigint_action;
+          sigint_action.sa_handler = sigint_handler;
+          sigemptyset (&sigint_action.sa_mask);
+          sigint_action.sa_flags = 0;
+          sigaction(SIGINT, &sigint_action, NULL);
+  #elif defined (_WIN32)
+          auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+              return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+          };
+          SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+  #endif
+      }
+
+      if (params.interactive) {
+          LOG_TEE("%s: interactive mode on.\n", __func__);
+
+          if (!params.antiprompt.empty()) {
+              for (const auto & antiprompt : params.antiprompt) {
+                  LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
+                  if (params.verbose_prompt) {
+                      auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
+                      for (int i = 0; i < (int) tmp.size(); i++) {
+                          LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                      }
+                  }
+              }
+          }
+
+          if (params.input_prefix_bos) {
+              LOG_TEE("Input prefix with BOS\n");
+          }
+
+          if (!params.input_prefix.empty()) {
+              LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
+              if (params.verbose_prompt) {
+                  auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
+                  for (int i = 0; i < (int) tmp.size(); i++) {
+                      LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                  }
+              }
+          }
+
+          if (!params.input_suffix.empty()) {
+              LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
+              if (params.verbose_prompt) {
+                  auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
+                  for (int i = 0; i < (int) tmp.size(); i++) {
+                      LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                  }
+              }
+          }
+      }
+      LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
+      LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
+      LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+      // group-attention state
+      // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+      int ga_i = 0;
+
+      const int ga_n = params.grp_attn_n;
+      const int ga_w = params.grp_attn_w;
+
+      if (ga_n != 1) {
+          GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+          GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+        //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+        //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+          LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+      }
+      LOG_TEE("\n\n");
+
+      if (params.interactive) {
+          const char *control_message;
+          if (params.multiline_input) {
+              control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+                                " - To return control without starting a new line, end your input with '/'.\n";
+          } else {
+              control_message = " - Press Return to return control to LLaMa.\n"
+                                " - To return control without starting a new line, end your input with '/'.\n"
+                                " - If you want to submit another line, end your input with '\\'.\n";
+          }
+          LOG_TEE("== Running in interactive mode. ==\n");
+  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+          LOG_TEE(       " - Press Ctrl+C to interject at any time.\n");
+  #endif
+          LOG_TEE(       "%s\n", control_message);
+
+          is_interacting = params.interactive_first;
+      }
+
+      bool is_antiprompt        = false;
+      bool input_echo           = true;
+      bool display              = true;
+
+      int n_past             = 0;
+      int n_remain           = params.n_predict;
+      unsigned n_consumed    = 0;
+      int n_past_guidance    = 0;
+
+      std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+      std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+      std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
+      // the first thing we will do is to output the prompt, so set color accordingly
+      console::set_display(console::prompt);
+      display = params.display_prompt;
+
+      std::vector<llama_token> embd;
+      std::vector<llama_token> embd_guidance;
+
+      // tokenized antiprompts
+      std::vector<std::vector<llama_token>> antiprompt_ids;
+
+      antiprompt_ids.reserve(params.antiprompt.size());
+      for (const std::string & antiprompt : params.antiprompt) {
+          antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+      }
+
+
+      // Tokenized prompt is in embd_inp
+
+
+      // Record prompt boundaries
+      const int PROMPT_DELIMITER_TOKEN = 13;
+
+      // Index of each delimiter token in `embd_inp`.  These mark the end of each
+      // prompt.
+      std::vector<size_t> delim_idxs;
+
+      for (size_t i = 0; i < embd_inp.size(); ++i) {
+          if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) {
+              delim_idxs.push_back(i);
+          }
+      }
+
+      // If the last prompt is missing an ending delimiter, add it.
+      if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) {
+          delim_idxs.push_back(embd_inp.size());
+          embd_inp.push_back(PROMPT_DELIMITER_TOKEN);
+      }
+
+      size_t num_prompts = delim_idxs.size();
+
+LOG_TEE("NIGGER");
+      // Set up eval_state
+      gguf_context * eval_gguf = gguf_init_empty();
+      {
+          int n_embd = llama_n_embd(model);
+          int n_layer = llama_n_layer(model);
+          std::cerr << "build eval state: " << num_prompts << " prompts, "
+              << n_embd << " embd, " << n_layer << " layers\n";
+
+          struct ggml_init_params params = {};
+          params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer;
+          eval_ctx = ggml_init(params);
+
+          for (int i = 0; i < n_layer; ++i) {
+              ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts);
+              snprintf(t->name, sizeof(t->name), "l_out-%d", i);
+              eval_state.tensors.push_back(t);
+              gguf_add_tensor(eval_gguf, t);
+          }
+          eval_state.first_prompt_idx = -1;
+      }
+
+
+
+      // Max tokens to include in a single batch.
+      int batch_max_tokens = llama_n_batch(ctx);
+      unsigned batch_max_seq = llama_n_seq_max(ctx);
+
+      struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq);
+
+      size_t prompt_idx = 0;
+      size_t batch_idx = 0;
+      // For each (token_id, pos) pair, these maps record the first sequence to
+      // contain that pair in the old and new states of the KV cache.
+      std::unique_ptr<kv_trie_node> old_kv_trie(new kv_trie_node(-1));
+      std::unique_ptr<kv_trie_node> new_kv_trie(new kv_trie_node(-1));
+          auto last = ggml_time_ms();
+      while (prompt_idx < num_prompts) {
+          std::cerr << "start batch at " << prompt_idx << "\n";
+          eval_state.first_prompt_idx = prompt_idx;
+          eval_state.extract_tokens.clear();
+          // Reset `tokens_seen` to zero for all layers.
+          eval_state.tokens_seen.clear();
+          eval_state.tokens_seen.resize(eval_state.tensors.size(), 0);
+
+          old_kv_trie = std::move(new_kv_trie);
+          new_kv_trie.reset(new kv_trie_node(-1));
+
+    //      std::cerr << "WHERE IS OUR OOM? " << prompt_idx << "\n";
+          // Clear the token batch.
+          batch.n_tokens = 0;
+          size_t context_used = 0;
+
+          llama_sampling_reset(ctx_sampling);
+
+          // Add prompts to the batch until it's full.
+          // We alternate between using sequence IDs [0, max/2) and [max/2, max).
+          // This ensures that old sequences and new sequences never collide.
+          unsigned first_seq = batch_idx % 2 == 0 ? 0 : batch_max_seq / 2;
+          unsigned last_seq = first_seq + batch_max_seq / 2;
+          unsigned cur_seq = first_seq;
+          unsigned kv_cache_space = n_ctx;
+          while (prompt_idx < num_prompts && cur_seq < last_seq) {
+  //            std::cerr << "WHERE IS OUR OOM now? " << prompt_idx << "\n";
+
+              size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1;
+              size_t end = delim_idxs[prompt_idx];
+              GGML_ASSERT(end > start && "empty prompts are not allowed");
+
+              //std::cerr << "adding " << start << " .. " << end
+              //    << " (" << (end - start) << " tokens); "
+              //    << context_used << " / " << batch_max_tokens << " context used\n";
+
+              if (end - start > kv_cache_space) {
+
+                std::cerr << "biggernitch!\n";
+                  // Not enough space remaining in the batch, if we hit the worst
+                  // case where the prompt consists entirely of new tokens.
+                  break;
+              }
+
+            //    std::cerr << "gettheniggernodes!\n";
+              kv_trie_node * old_node = old_kv_trie.get();
+              kv_trie_node * new_node = new_kv_trie.get();
+            //    std::cerr << "g0ttheniggernodes!\n";
+
+              for (size_t j = start; j < end; ++j) {
+                  int id = embd_inp[j];
+                  int pos = j - start;
+
+            //    std::cerr << "lemmetrytofindthatnode!\n";
+                  old_node = kv_trie_find(old_node, id);
+                  // Regardless of how we handle the token, it will ultimately be
+                  // present at position `j` in sequence `cur_seq`.
+            //    std::cerr << "lemmetrytoINSERTthatnode!\n";
+                  new_node = kv_trie_insert(new_node, id, cur_seq);
+            //    std::cerr << "node business going on!\n";
+                  if (old_node == nullptr) {
+                      // Add a new token.
+            //    std::cerr << "add that token to the batch y0!\n";
+                      llama_batch_add(batch, id, pos, {(int)cur_seq}, false);
+                      --kv_cache_space;
+                      //const std::string token_str = llama_token_to_piece(ctx, id);
+                      //LOG_TEE("add '%s' (%d), %d to new sequence %d (fresh)\n",
+                      //        token_str.c_str(), id, pos, cur_seq);
+                  } else {
+            //    std::cerr << "WE COPY SOME SHIT HERE.\n";
+                      llama_kv_cache_seq_cp(ctx, old_node->seq, cur_seq, pos, pos + 1);
+                      //const std::string token_str = llama_token_to_piece(ctx, id);
+                      //LOG_TEE("add '%s' (%d), %d to new sequence %d, from old sequence %d\n",
+                      //        token_str.c_str(), id, pos, cur_seq, old_seq);
+                      if (!old_node->retained) {
+                          // This token was previously going to be discarded.
+                          // Now that it's being kept, we have to count it
+                          // against `kv_cache_space`.
+                          old_node->retained = true;
+                          --kv_cache_space;
+                          //LOG_TEE("REUSED '%s' (%d), %d\n", token_str.c_str(), id, pos);
+                      }
+                  }
+              }
+
+        //        std::cerr << "Push it back, Jamal.\n";
+              eval_state.extract_tokens.push_back(batch.n_tokens - 1);
+
+              ++prompt_idx;
+              ++cur_seq;
+          }
+
+
+        //  std::cerr << "kv cache business going on.\n";
+          for (unsigned seq = 0; seq < batch_max_seq; ++seq) {
+              if (seq < first_seq || seq >= last_seq) {
+                  llama_kv_cache_seq_rm(ctx, seq, -1, -1);
+              }
+          }
+        //  std::cerr << "kv cache business went ok.\n";
+
+          // Force defragmentation of the KV cache.  `llama_decode` needs a
+          // contiguous block of `batch.n_tokens` cache slots, which it won't be
+          // able to find if the cache is too fragmented.  Since we build batches
+          // so as to maximize cache/context utilization, any fragmentation at
+          // all will usually cause it to fail.
+          //
+          // FIXME: This sometimes doesn't fully defragment the cache, as shown
+          // by `llama_kv_cache_view` debugging stats: if all free space was
+          // contiguous, then `max_contiguous` should equal the number of free
+          // cells (`n_cells - used_cells`), but often this is not the case.
+
+          //std::cerr << "defrag kv cache\n";
+          llama_kv_cache_defrag(ctx);
+          //std::cerr << "UPD0000T kv cache\n";
+          llama_kv_cache_update(ctx);
+          //std::cerr << "defragGGGED kv cache\n";
+
+          for (int i = 0; i < 10; ++i) {
+              // Debug prints to check cache usage and fragmentation:
+              auto view = llama_kv_cache_view_init(ctx, 1);
+              llama_kv_cache_view_update(ctx, &view);
+            //  std::cerr << "kv cache cells: " << view.n_cells << "\n";
+            //  std::cerr << "kv cache tokens: " << view.token_count << "\n";
+            //  std::cerr << "kv cache used: " << view.used_cells << "\n";
+            //  std::cerr << "kv cache contiguous free space: "
+            //      << view.max_contiguous << " / "
+            //      << (view.n_cells - view.used_cells) << "\n";
+              if (view.max_contiguous < view.n_cells - view.used_cells) {
+                  //std::cerr << "defrag didn't work! trying again...\n";
+                  llama_kv_cache_defrag(ctx);
+                  llama_kv_cache_update(ctx);
+              } else {
+                  break;
+              }
+          }
+
+          GGML_ASSERT(batch.n_tokens > 0);
+
+          std::cerr << "batch " << eval_state.first_prompt_idx << ": "
+              << (prompt_idx - eval_state.first_prompt_idx) << " prompts, "
+              << batch.n_tokens << " new tokens, "
+              << context_used << " total tokens\n";
+
+
+  //        fprintf(stderr, "Let us OOM now.\n");
+          if (llama_decode(ctx, batch)) {
+              LOG_TEE("%s : failed to eval\n", __func__);
+              return 1;
+          }
+    //      fprintf(stderr, "We OOMed not.\n");
+
+          auto now = ggml_time_ms();
+          auto timedelta = now - last;
+          last = now;
+          std::cerr << "time delta: " << timedelta << "ms\n";
+      //    fprintf(stderr, "We OOMed still not.\n");
+          ++batch_idx;
+      }
+      
+
+      //fprintf(stderr, "We DID NOT 00M.\n");
+      llama_print_timings(ctx);
+      //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
+      //if (ctx_guidance) { llama_free(ctx_guidance); }
+
+        char *fname = farg, *fe = NULL;
+        for (char *fn = fname; *fn != '\0'; fn++) {
+          if (*fn == '/')
+            fname = &fn[1];
+          if (*fn == '_')
+            fe = fn;
+        }
+        if (fe > fname) {
+          strcpy(fe, "_data.gguf");
+        }
+        fprintf(stderr, "Save the file, nigger.\n");
+        fprintf(stderr, "Save the file to %s nigger.\n", fname);
+          auto now = ggml_time_ms();
+          gguf_write_to_file(eval_gguf, fname, false);
+
+          auto nowa = ggml_time_ms();
+          auto timedeltaa = nowa - now;
+        fprintf(stderr, "Saving took: %dms.\n", timedeltaa);
+
+      llama_free(ctx);
+      ggml_free(eval_ctx);
+      llama_sampling_free(ctx_sampling);
+      if (ff.size()) {
+        farg = ff.front();
+        std::ifstream file(farg);
+        if (!file) {
+          fprintf(stderr, "error: failed to open file '%s'\n", farg);
+          exit(1337);
+        }
+
+        // store the external file name in params
+        params.prompt_file = farg;
+        params.prompt.clear();
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+        if (!params.prompt.empty() && params.prompt.back() == '\n') {
+          params.prompt.pop_back();
+        }
+
+        ff.pop_front();
+      } else break;
+    } while (true);
+//ENDYO
+
+    llama_free_model(model);
+
+    llama_backend_free();
+
+#ifndef LOG_DISABLE_LOGS
+    LOG_TEE("Log end\n");
+#endif // LOG_DISABLE_LOGS
+
+    return 0;
+}
diff --git a/examples/repeng/repong.cpp b/examples/repeng/repong.cpp
new file mode 100644
index 00000000..6a3c0456
--- /dev/null
+++ b/examples/repeng/repong.cpp
@@ -0,0 +1,915 @@
+
+#include "common.h"
+
+#include "console.h"
+#include "llama.h"
+#include <deque> 
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
+static bool is_interacting = false;
+
+static bool file_exists(const std::string &path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string &path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (!is_interacting && g_params->interactive) {
+            is_interacting = true;
+        } else {
+            console::cleanup();
+            printf("\n");
+            llama_print_timings(*g_ctx);
+            //write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
+            _exit(130);
+        }
+    }
+}
+#endif
+
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval(
+    gpt_params & params,
+    ggml_backend_sched_eval_callback cb_eval,
+    void * cb_eval_user_data,
+    struct llama_model *preloaded = NULL) {
+    auto mparams = llama_model_params_from_gpt_params(params);
+
+    llama_model * model  = preloaded ? preloaded : llama_load_model_from_file(params.model.c_str(), mparams);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    auto cparams = llama_context_params_from_gpt_params(params);
+
+    cparams.cb_eval = cb_eval;
+    cparams.cb_eval_user_data = cb_eval_user_data;
+
+    llama_context * lctx = llama_new_context_with_model(model, cparams);
+    if (lctx == NULL) {
+        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
+        llama_free_model(model);
+        return std::make_tuple(nullptr, nullptr);
+    }
+
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
+        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
+        float lora_scale = std::get<1>(params.lora_adapter[i]);
+        int err = llama_model_apply_lora_from_file(model,
+                                             lora_adapter.c_str(),
+                                             lora_scale,
+                                             ((i > 0) || params.lora_base.empty())
+                                                ? NULL
+                                                : params.lora_base.c_str(),
+                                             params.n_threads);
+        if (err != 0) {
+            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
+    if (params.ignore_eos) {
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+    }
+
+    {
+        LOG("warming up the model with an empty run\n");
+
+        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
+        llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
+        llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
+        llama_reset_timings(lctx);
+    }
+
+    return std::make_tuple(model, lctx);
+}
+
+struct eval_callback_state {
+    std::vector<ggml_tensor *> tensors;
+    // For each hidden state tensor, how many tokens have we seen from the
+    // current batch?  When n_ubatch < n_batch, we don't see the hidden states
+    // for all tokens at once, but only n_ubatch-sized chunks of tokens.  This
+    // keeps track of our progress.
+    std::vector<int> tokens_seen;
+    int first_prompt_idx;
+    std::vector<int> extract_tokens;
+};
+
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) {
+    struct eval_callback_state * eval_state = (eval_callback_state *)user_data;
+    if (ask) {
+        // Report whether we want to observe this tensor.
+        if (strncmp(t->name, "l_out-", 6) == 0) {
+            return true;
+        } else {
+            return false;
+        }
+    } else {
+        // Actually observe the tensor data.
+
+        if (eval_state->first_prompt_idx >= 0) {
+            // Find the tensor collecting hidden states for the current layer.
+            int layer_idx = -1;
+            ggml_tensor * output_tensor = nullptr;
+            for (size_t i = 0; i < eval_state->tensors.size(); ++i) {
+                auto t2 = eval_state->tensors[i];
+                if (strcmp(t2->name, t->name) == 0) {
+                    output_tensor = t2;
+                    layer_idx = i;
+                    break;
+                }
+            }
+
+            if (output_tensor != nullptr) {
+                int ubatch_size = t->ne[1];
+                int ubatch_start = eval_state->tokens_seen[layer_idx];
+                int ubatch_end = ubatch_start + ubatch_size;
+                eval_state->tokens_seen[layer_idx] += ubatch_size;
+
+                int output_idx = eval_state->first_prompt_idx;
+                for (int token_idx : eval_state->extract_tokens) {
+                    if (token_idx < ubatch_start || token_idx >= ubatch_end) {
+                        continue;
+                    }
+                    int input_idx = token_idx - ubatch_start;
+
+                    // Copy the hidden states for the current token into the
+                    // output buffer.
+                    size_t input_offset = t->nb[1] * input_idx;
+                    size_t output_offset = output_tensor->nb[1] * output_idx;
+                    assert(t->nb[0] == output_tensor->nb[0]);
+                    assert(t->ne[0] == output_tensor->ne[0]);
+                    ggml_backend_tensor_get(t,
+                            (char *)output_tensor->data + output_offset,
+                            input_offset,
+                            t->nb[0] * t->ne[0]);
+                    //memcpy((char *)output_tensor->data + output_offset,
+                    //        (char *)t->data + input_offset,
+                    //        t->nb[0] * t->ne[0]);
+                    //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data "
+                    //    << " for " << t->name << " in slot " << output_idx << "\n";
+
+                    //float * buf = (float *)((char *)t->data + input_offset);
+                    //float * buf = (float *)((char *)output_tensor->data + output_offset);
+                    //std::cerr << "prompt " << output_idx
+                    //    << " tensor contents for " << t->name << ": "
+                    //    << buf[0] << ", "
+                    //    << buf[1] << ", "
+                    //    << buf[2] << " ... "
+                    //    << buf[4093] << ", "
+                    //    << buf[4094] << ", "
+                    //    << buf[4095] << "\n";
+
+                    ++output_idx;
+                }
+            }
+        }
+
+        // Continue running
+        return true;
+    }
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    g_params = &params;
+
+    bool mf = false;
+    char *farg;
+    std::deque<char *> ff;
+    int findex = -1;
+    for (int i=0; i<argc; i++) {
+      if (strcmp(argv[i], "-f") == 0) {
+        if (++i >= argc) {
+          exit(1337);
+          break;
+        }
+        findex = i;
+        farg = argv[i];
+        for (int j=0; argv[i][j] != '\0'; j++) {
+          if (argv[i][j] == ',') {
+            argv[i][j] = '\0';
+            mf = true;
+            ff.push_back(&argv[i][j+1]);
+          }
+        }
+        break;
+      }
+    }
+    if (!gpt_params_parse(argc, argv, params)) {
+      printf("Fuck you");
+        return 1;
+    }
+    llama_sampling_params & sparams = params.sparams;
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("main", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
+    // TODO: Dump params ?
+    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
+
+    // save choice to use color for later
+    // (note for later: this is a slightly awkward choice)
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    if (params.logits_all) {
+        printf("\n************\n");
+        printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.embedding) {
+        printf("\n************\n");
+        printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.n_ctx != 0 && params.n_ctx < 8) {
+        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        params.n_ctx = 8;
+    }
+
+    if (params.rope_freq_base != 0.0) {
+        LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 0.0) {
+        LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
+    }
+
+    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    LOG("%s: llama backend init\n", __func__);
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_model *preloaded = NULL;
+    llama_context * ctx;
+    llama_context * ctx_guidance = NULL;
+    g_model = &model;
+    g_ctx = &ctx;
+
+    // load the model and apply lora adapter, if any
+    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
+//STARTYO
+    do
+    {
+      ggml_context * eval_ctx = nullptr;
+      struct eval_callback_state eval_state;
+
+      std::tie(model, ctx) = llama_init_from_gpt_params_with_cb_eval(
+          params,
+          eval_callback,
+          (void *)&eval_state,
+          preloaded);
+      preloaded = model;
+      /*
+      if (sparams.cfg_scale > 1.f) {
+          struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+          ctx_guidance = llama_new_context_with_model(model, lparams);
+      }
+      */
+
+      if (model == NULL) {
+          LOG_TEE("%s: error: unable to load model\n", __func__);
+          return 1;
+      }
+
+      const int n_ctx_train = llama_n_ctx_train(model);
+      const int n_ctx = llama_n_ctx(ctx);
+      LOG("n_ctx: %d\n", n_ctx);
+
+      if (n_ctx > n_ctx_train) {
+          LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                  __func__, n_ctx_train, n_ctx);
+      }
+
+      // print system information
+      {
+          LOG_TEE("\n");
+          LOG_TEE("%s\n", get_system_info(params).c_str());
+      }
+
+      std::string path_session = params.path_prompt_cache;
+      std::vector<llama_token> session_tokens;
+
+      if (!path_session.empty()) {
+          LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
+          if (!file_exists(path_session)) {
+              LOG_TEE("%s: session file does not exist, will create.\n", __func__);
+          } else if (file_is_empty(path_session)) {
+              LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__);
+          } else {
+              // The file exists and is not empty
+              session_tokens.resize(n_ctx);
+              size_t n_token_count_out = 0;
+              if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+                  LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
+                  return 1;
+              }
+              session_tokens.resize(n_token_count_out);
+              llama_set_rng_seed(ctx, params.seed);
+              LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
+          }
+      }
+
+      const bool add_bos = llama_should_add_bos_token(model);
+      LOG("add_bos: %d\n", add_bos);
+
+      std::vector<llama_token> embd_inp;
+
+      if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
+          LOG("tokenize the prompt\n");
+          if (params.chatml) {
+              params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+          }
+          embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+      } else {
+          LOG("use session tokens\n");
+          embd_inp = session_tokens;
+      }
+
+      LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
+      LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+
+      // Should not run without any tokens
+      if (embd_inp.empty()) {
+          embd_inp.push_back(llama_token_bos(model));
+          LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+      }
+
+      size_t n_matching_session_tokens = 0;
+      if (!session_tokens.empty()) {
+          for (llama_token id : session_tokens) {
+              if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
+                  break;
+              }
+              n_matching_session_tokens++;
+          }
+          if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
+              LOG_TEE("%s: using full prompt from session file\n", __func__);
+          } else if (n_matching_session_tokens >= embd_inp.size()) {
+              LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
+          } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
+              LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
+                  __func__, n_matching_session_tokens, embd_inp.size());
+          } else {
+              LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
+                  __func__, n_matching_session_tokens, embd_inp.size());
+          }
+
+          // remove any "future" tokens that we might have inherited from the previous session
+          llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
+      }
+
+      LOGLN(
+              "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
+              log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
+
+      // if we will use the cache for the full prompt without reaching the end of the cache, force
+      // reevaluation of the last token token to recalculate the cached logits
+      if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
+          LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);
+
+          session_tokens.resize(embd_inp.size() - 1);
+      }
+
+      // number of tokens to keep when resetting context
+      if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
+          params.n_keep = (int)embd_inp.size();
+      } else {
+          params.n_keep += add_bos; // always keep the BOS token
+      }
+
+      // prefix & suffix for instruct mode
+      const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
+      const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
+
+      LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
+      LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
+
+      // chatml prefix & suffix
+      const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
+      const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
+
+      LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
+      LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
+
+      // in instruct mode, we inject a prefix and a suffix to each input by the user
+      if (params.instruct) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("### Instruction:\n\n");
+      }
+      // similar for chatml mode
+      else if (params.chatml) {
+          params.interactive_first = true;
+          params.antiprompt.emplace_back("<|im_start|>user\n");
+      }
+
+      // enable interactive mode if interactive start is specified
+      if (params.interactive_first) {
+          params.interactive = true;
+      }
+
+      if (params.verbose_prompt) {
+          LOG_TEE("\n");
+          LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+          LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+          for (int i = 0; i < (int) embd_inp.size(); i++) {
+              LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
+          }
+
+          if (params.n_keep > add_bos) {
+              LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+              for (int i = 0; i < params.n_keep; i++) {
+                  LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+              }
+              LOG_TEE("'\n");
+          }
+          LOG_TEE("\n");
+      }
+
+      // ctrl+C handling
+      {
+  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+          struct sigaction sigint_action;
+          sigint_action.sa_handler = sigint_handler;
+          sigemptyset (&sigint_action.sa_mask);
+          sigint_action.sa_flags = 0;
+          sigaction(SIGINT, &sigint_action, NULL);
+  #elif defined (_WIN32)
+          auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+              return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+          };
+          SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+  #endif
+      }
+
+      if (params.interactive) {
+          LOG_TEE("%s: interactive mode on.\n", __func__);
+
+          if (!params.antiprompt.empty()) {
+              for (const auto & antiprompt : params.antiprompt) {
+                  LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
+                  if (params.verbose_prompt) {
+                      auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
+                      for (int i = 0; i < (int) tmp.size(); i++) {
+                          LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                      }
+                  }
+              }
+          }
+
+          if (params.input_prefix_bos) {
+              LOG_TEE("Input prefix with BOS\n");
+          }
+
+          if (!params.input_prefix.empty()) {
+              LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
+              if (params.verbose_prompt) {
+                  auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
+                  for (int i = 0; i < (int) tmp.size(); i++) {
+                      LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                  }
+              }
+          }
+
+          if (!params.input_suffix.empty()) {
+              LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
+              if (params.verbose_prompt) {
+                  auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
+                  for (int i = 0; i < (int) tmp.size(); i++) {
+                      LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                  }
+              }
+          }
+      }
+      LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
+      LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
+      LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+      // group-attention state
+      // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+      int ga_i = 0;
+
+      const int ga_n = params.grp_attn_n;
+      const int ga_w = params.grp_attn_w;
+
+      if (ga_n != 1) {
+          GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+          GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+        //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+        //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+          LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+      }
+      LOG_TEE("\n\n");
+
+      if (params.interactive) {
+          const char *control_message;
+          if (params.multiline_input) {
+              control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+                                " - To return control without starting a new line, end your input with '/'.\n";
+          } else {
+              control_message = " - Press Return to return control to LLaMa.\n"
+                                " - To return control without starting a new line, end your input with '/'.\n"
+                                " - If you want to submit another line, end your input with '\\'.\n";
+          }
+          LOG_TEE("== Running in interactive mode. ==\n");
+  #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+          LOG_TEE(       " - Press Ctrl+C to interject at any time.\n");
+  #endif
+          LOG_TEE(       "%s\n", control_message);
+
+          is_interacting = params.interactive_first;
+      }
+
+      bool is_antiprompt        = false;
+      bool input_echo           = true;
+      bool display              = true;
+      bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
+
+      int n_past             = 0;
+      int n_remain           = params.n_predict;
+      unsigned n_consumed    = 0;
+      int n_session_consumed = 0;
+      int n_past_guidance    = 0;
+
+      std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+      std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+      std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
+      // the first thing we will do is to output the prompt, so set color accordingly
+      console::set_display(console::prompt);
+      display = params.display_prompt;
+
+      std::vector<llama_token> embd;
+      std::vector<llama_token> embd_guidance;
+
+      // tokenized antiprompts
+      std::vector<std::vector<llama_token>> antiprompt_ids;
+
+      antiprompt_ids.reserve(params.antiprompt.size());
+      for (const std::string & antiprompt : params.antiprompt) {
+          antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+      }
+
+      struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
+
+
+
+      // Tokenized prompt is in embd_inp
+
+
+      printf("Start training from prompts %s ...\n", farg);
+      // Record prompt boundaries
+      const int PROMPT_DELIMITER_TOKEN = 13;
+
+      // Index of each delimiter token in `embd_inp`.  These mark the end of each
+      // prompt.
+      std::vector<size_t> delim_idxs;
+
+      for (size_t i = 0; i < embd_inp.size(); ++i) {
+          if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) {
+              delim_idxs.push_back(i);
+          }
+      }
+
+      // If the last prompt is missing an ending delimiter, add it.
+      if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) {
+          delim_idxs.push_back(embd_inp.size());
+          embd_inp.push_back(PROMPT_DELIMITER_TOKEN);
+      }
+
+      size_t num_prompts = delim_idxs.size();
+
+
+      // Set up eval_state
+      gguf_context * eval_gguf = gguf_init_empty();
+      {
+          int n_embd = llama_n_embd(model);
+          int n_layer = llama_n_layer(model);
+          std::cerr << "build eval state: " << num_prompts << " prompts, "
+              << n_embd << " embd, " << n_layer << " layers\n";
+
+          struct ggml_init_params params = {};
+          params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer;
+          eval_ctx = ggml_init(params);
+
+          for (int i = 0; i < n_layer; ++i) {
+              ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts);
+              snprintf(t->name, sizeof(t->name), "l_out-%d", i);
+              eval_state.tensors.push_back(t);
+              gguf_add_tensor(eval_gguf, t);
+          }
+          eval_state.first_prompt_idx = -1;
+      }
+
+
+      // Max tokens to include in a single batch.
+      int batch_max_tokens = llama_n_batch(ctx);
+      unsigned batch_max_seq = llama_n_seq_max(ctx);
+
+      struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq);
+
+      size_t prompt_idx = 0;
+      std::vector<size_t> prev_prompt_start_for_sequence;
+      auto last = ggml_time_ms();
+      while (prompt_idx < num_prompts) {
+          //std::cerr << "start batch at " << prompt_idx << "\n";
+          eval_state.first_prompt_idx = prompt_idx;
+          eval_state.extract_tokens.clear();
+          // Reset `tokens_seen` to zero for all layers.
+          eval_state.tokens_seen.clear();
+          eval_state.tokens_seen.resize(eval_state.tensors.size(), 0);
+
+          // Clear the token batch.
+          batch.n_tokens = 0;
+          size_t context_used = 0;
+
+          llama_sampling_reset(ctx_sampling);
+
+          // Add prompts to the batch until it's full.
+          unsigned next_seq = 0;
+          while (prompt_idx < num_prompts && next_seq < batch_max_seq) {
+              size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1;
+              size_t end = delim_idxs[prompt_idx];
+              GGML_ASSERT(end > start && "empty prompts are not allowed");
+
+              // Number of initial tokens in common between this prompt and the
+              // previous prompt to use this sequence ID.
+              size_t common = 0;
+
+              if (next_seq < prev_prompt_start_for_sequence.size()) {
+                  size_t prev_start = prev_prompt_start_for_sequence[next_seq];
+                  GGML_ASSERT(prev_start <= start);
+                  while (start + common < embd_inp.size()
+                          && embd_inp[prev_start + common] != PROMPT_DELIMITER_TOKEN
+                          && embd_inp[start + common] == embd_inp[prev_start + common]
+                          ) {
+                      ++common;
+                  }
+              }
+              // If the current prompt is a prefix of the previous one, then it's
+              // possible that all tokens are marked as common.  Ensure that the
+              // last token of the current prompt is never marked as common so
+              // that we get its hidden states.
+              if (common >= end - start) {
+                  common = end - start - 1;
+              }
+              GGML_ASSERT(start + common < end);
+
+              //std::cerr << "adding " << start << " .. " << end
+              //    << " (" << (end - start) << " tokens); "
+              //    << context_used << " / " << batch_max_tokens << " context used\n";
+
+              // FIXME: We artificially reduce the batch size limit here to
+              // account for `llama_kv_cache_defrag` not fully defragmenting the
+              // cache.  See the comment below.
+              if (end - start > (size_t)(batch_max_tokens * 8 / 10 - context_used)) {
+                  // Not enough space for this prompt in the batch.
+                  std::cerr << "check space for prompt: [" << end << "-" << start << "=" << end - start << "] | " << context_used << "\n";
+                  GGML_ASSERT(end - start <= (size_t)context_used);
+                  break;
+              }
+
+              // Clear the KV cache for this sequence, except for the common
+              // prefix.
+              llama_kv_cache_seq_rm(ctx, next_seq, common, -1);
+
+              //std::cerr << "still cooking\n";
+              for (size_t j = start + common; j < end; ++j) {
+                  int id = embd_inp[j];
+
+                  // push the prompt in the sampling context in order to apply
+                  // repetition penalties later for the prompt, we don't apply
+                  // grammar rules
+                  //llama_sampling_accept(ctx_sampling, ctx, id, false);
+
+                  // Add the token to the current batch.  Its position within the
+                  // context is relative to the start of the current prompt.
+                  llama_batch_add(batch, id, j - start, {(int)next_seq}, false);
+
+                  //const std::string token_str = llama_token_to_piece(ctx, id);
+                  //std::cerr << "pos " << (j - start) << ": token "
+                  //    << id << " \"" << token_str << "\"\n";
+              }
+
+              eval_state.extract_tokens.push_back(batch.n_tokens - 1);
+              if (next_seq >= prev_prompt_start_for_sequence.size()) {
+                  GGML_ASSERT(next_seq == prev_prompt_start_for_sequence.size());
+                  prev_prompt_start_for_sequence.push_back(start);
+              } else {
+                  prev_prompt_start_for_sequence[next_seq] = start;
+              }
+
+              ++prompt_idx;
+              ++next_seq;
+              context_used += end - start;
+          }
+
+          while (prev_prompt_start_for_sequence.size() > next_seq) {
+              llama_kv_cache_seq_rm(ctx, prev_prompt_start_for_sequence.size() - 1, -1, -1);
+              prev_prompt_start_for_sequence.pop_back();
+          }
+
+          // Force defragmentation of the KV cache.  `llama_decode` needs a
+          // contiguous block of `batch.n_tokens` cache slots, which it won't be
+          // able to find if the cache is too fragmented.  Since we build batches
+          // so as to maximize cache/context utilization, any fragmentation at
+          // all will usually cause it to fail.
+          //
+          // FIXME: This sometimes doesn't fully defragment the cache, as shown
+          // by `llama_kv_cache_view` debugging stats: if all free space was
+          // contiguous, then `max_contiguous` should equal the number of free
+          // cells (`n_cells - used_cells`), but often this is not the case.
+          std::cerr << "defrag boi\n";
+          llama_kv_cache_defrag(ctx);
+          llama_kv_cache_update(ctx);
+
+          /*
+          // Debug prints to check cache usage and fragmentation:
+          auto view = llama_kv_cache_view_init(ctx, 1);
+          llama_kv_cache_view_update(ctx, &view);
+          //std::cerr << "kv cache cells: " << view.n_cells << "\n";
+          //std::cerr << "kv cache tokens: " << view.token_count << "\n";
+          //std::cerr << "kv cache used: " << view.used_cells << "\n";
+          std::cerr << "kv cache max_contiguous: " << view.max_contiguous << "\n";
+          std::cerr << "kv cache free cells: " << (view.n_cells - view.used_cells) << "\n";
+          */
+
+          //GGML_ASSERT(batch.n_tokens > 0);
+
+
+          std::cerr << "batch " << eval_state.first_prompt_idx << ": "
+              << (prompt_idx - eval_state.first_prompt_idx) << " prompts, "
+              << batch.n_tokens << " new tokens, "
+              << context_used << " total tokens\n";
+
+          //std::cerr << "prompt " << eval_state.prompt_idx << ": " << batch.n_tokens << " tokens\n";
+
+          //batch.logits[batch.n_tokens - 1] = true;
+
+
+          if (llama_decode(ctx, batch)) {
+              LOG_TEE("%s : failed to eval\n", __func__);
+              return 1;
+          }
+
+          auto now = ggml_time_ms();
+          auto timedelta = now - last;
+          last = now;
+          std::cerr << "time delta: " << timedelta << "ms\n";
+
+          //const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr, batch.n_tokens - 1);
+          //const std::string token_str = llama_token_to_piece(ctx, id);
+          //LOG_TEE("sample token %d: \"%s\"\n", id, token_str.c_str());
+      }
+
+      char *fname = farg, *fe = NULL;
+      for (char *fn = fname; *fn != '\0'; fn++) {
+        if (*fn == '/')
+          fname = &fn[1];
+        if (*fn == '_')
+          fe = fn;
+      }
+      if (fe > fname) {
+        strcpy(fe, "_data.gguf");
+      }
+
+      gguf_write_to_file(eval_gguf, fname, false);
+
+      if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
+          LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
+          llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+      }
+
+      llama_print_timings(ctx);
+      //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
+      //if (ctx_guidance) { llama_free(ctx_guidance); }
+      llama_free(ctx);
+      ggml_free(eval_ctx);
+      llama_sampling_free(ctx_sampling);
+      if (ff.size()) {
+        farg = ff.front();
+        std::ifstream file(farg);
+        if (!file) {
+          fprintf(stderr, "error: failed to open file '%s'\n", farg);
+          exit(1337);
+        }
+        // store the external file name in params
+        params.prompt_file = farg;
+        params.prompt.clear();
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+        if (!params.prompt.empty() && params.prompt.back() == '\n') {
+          params.prompt.pop_back();
+        }
+        ff.pop_front();
+      } else break;
+    } while (true);
+//ENDYO
+    llama_free_model(model);
+
+    llama_backend_free();
+#ifndef LOG_DISABLE_LOGS
+    LOG_TEE("Log end\n");
+#endif // LOG_DISABLE_LOGS
+
+    return 0;
+}
diff --git a/scripts/gen-build-info-cpp.cmake b/scripts/gen-build-info-cpp.cmake
index d8933892..de12a311 100644
--- a/scripts/gen-build-info-cpp.cmake
+++ b/scripts/gen-build-info-cpp.cmake
@@ -1,7 +1,8 @@
 include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
 
 set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
-set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
+set(TEMP_FILE "${OUTPUT_DIR}/common/build-info.cpp.in")
+set(OUTPUT_FILE "${OUTPUT_DIR}/common/build-info.cpp")
 
 # Only write the build info if it changed
 if(EXISTS ${OUTPUT_FILE})
@@ -17,8 +18,12 @@ if(EXISTS ${OUTPUT_FILE})
         NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
         NOT OLD_TARGET   STREQUAL BUILD_TARGET
     )
-        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+        message(STATUS ${TEMPLATE_FILE} ${TEMP_FILE} ${OUTPUT_FILE})
+        configure_file(${TEMPLATE_FILE} ${TEMP_FILE} COPYONLY)
+        configure_file(${TEMP_FILE} ${OUTPUT_FILE})
     endif()
 else()
-    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
+    message(STATUS ${TEMPLATE_FILE} ${TEMP_FILE} ${OUTPUT_FILE})
+    configure_file(${TEMPLATE_FILE} ${TEMP_FILE} COPYONLY)
+    configure_file(${TEMP_FILE} ${OUTPUT_FILE})
 endif()