gpttype_adapter.cpp

//This is Concedo's shitty adapter for adding python bindings for llama

//Considerations:
//Don't want to use pybind11 due to dependencies on MSVCC
//ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations here!
//Leave main.cpp UNTOUCHED, We want to be able to update the repo and pull any changes automatically.
//No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
//Python will ALWAYS provide the memory, we just write to it.

#include <cmath>
#include <time.h>
#include <mutex>
#include <unordered_map>
#include "model_adapter.h"
#include "otherarch.h"
#include "llama.h"
#include <vector>
#include <map>
#include <cstdint>
#include <string>
#include <cctype>
#include <locale>

#include "utils.h"

//for easier compilation
//concat source files into one file for compilation purposes
#include "llama_v2.cpp"
#include "llama_v3.cpp"
#include "src/llama.cpp"
#include "gptj_v1.cpp"
#include "gptj_v2.cpp"
#include "gptj_v3.cpp"
#include "gpt2_v1.cpp"
#include "gpt2_v2.cpp"
#include "gpt2_v3.cpp"
#include "rwkv_v2.cpp"
#include "rwkv_v3.cpp"
#include "neox_v2.cpp"
#include "neox_v3.cpp"
#include "mpt_v3.cpp"
#include "examples/llava/clip.h"
#include "examples/llava/llava.h"
#include "common/common.h"

// #include "experimental/emphasis.h"

//const
const int extra_context_handle_fragmentation = 120;
const int LLAVA_TOKEN_IDENTIFIER_A = -998; //alternate between both, changing when image changes
const int LLAVA_TOKEN_IDENTIFIER_B = -999;

//shared
std::string executable_path = "";
std::string lora_filename = "";
std::string lora_base = "";
std::string mmproj_filename = "";
std::string draftmodel_filename = "";
int speculative_chunk_amt = 8; //do it in chunks of this many tokens
bool generation_finished;
float last_process_time = 0;
float last_eval_time = 0;
int last_token_count = 0;
int last_seed = -1;
int total_gens = 0;
stop_reason last_stop_reason = stop_reason::INVALID;
std::vector<std::string> generated_tokens;

llama_grammar *  grammar = nullptr; //currently used grammar
llama_grammar_parser parsed_grammar;
static std::string current_grammar = "";

//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
static FileFormat file_format = FileFormat::BADFORMAT;
static FileFormatExtraMeta file_format_meta;

static gpt_vocab vocab;
static int32_t n_vocab = 0;

static gptj_v1_model gptj_ctx_v1;
static gptj_v2_model gptj_ctx_v2;
static gptj_model gptj_ctx_v3;

static gpt2_v1_model gpt2_ctx_v1;
static gpt2_v2_model gpt2_ctx_v2;
static gpt2_model gpt2_ctx_v3;

static gpt_neox_v2_model neox_ctx_v2;
static gpt_neox_model neox_ctx_v3;

static mpt_model mpt_ctx_v3;

static rwkv_v2_context * rwkv_ctx_v2;
static rwkv_context * rwkv_ctx_v3;

static llama_v2_context * llama_ctx_v2;
static llama_v3_context * llama_ctx_v3;
static llama_context * llama_ctx_v4;
static llama_context * draft_ctx = nullptr; //will remain null if speculative is unused

static clip_ctx * clp_ctx = nullptr; //for llava
static clip_image_u8 * clp_img_data = nullptr; //most recent image
static std::vector<llava_image> llava_images;
static std::string llava_composite_image_signature = ""; //for identifying when the llava images change, we need to invalidate the cache
static int current_llava_identifier = LLAVA_TOKEN_IDENTIFIER_A;

static kcpp_params * kcpp_data = nullptr;
static int max_context_limit_at_load = 0;
static int n_past = 0;
static int debugmode = 0; //-1 = hide all, 0 = normal, 1 = showall
static std::vector<gpt_vocab::id> last_n_tokens;
static std::vector<gpt_vocab::id> current_context_tokens;
static size_t mem_per_token = 0;
static std::vector<float> logits;
static std::vector<int> smartcontext;
static std::vector<std::string> stop_sequence;
static std::vector<int> special_stop_sequence; //for stop sequences that don't have a string representation
static std::vector<std::string> banned_tokens;
static std::vector<int> banned_token_ids;
static std::vector<std::string> banned_phrases;
static std::unordered_multimap<gpt_vocab::id, std::vector<gpt_vocab::id>> dry_sequence_breakers; // Multi-mapping from first token of sequence to tail of sequence (tail is empty for a single token)
static std::vector<int> dry_repeat_count; // Indexed as last_n_tokens
static std::unordered_map<gpt_vocab::id, int> dry_max_token_repeat;
static std::vector<TopPicksData> top_picks_history;
static int remaining_tokens = 0;
static bool early_abort = false;
static std::mutex concat_output_mtx;
static std::string concat_output = "";
static std::string concat_output_reader_copy_poll = ""; //for streaming
static std::string concat_output_reader_copy_res = ""; //for gen response
static std::vector<logit_bias> logit_biases;

static int delayed_generated_tokens_limit = 0;
std::deque<std::string> delayed_generated_tokens; //for use with antislop sampling
static std::map<int,std::vector<int>> antislop_banned_token_ids; //first is the npast position, second is the array of banned ids at that index

inline int kcpp_cpu_has_blas(void) {
#if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
    return 1;
#else
    return 0;
#endif
}

inline bool IsNanCheck(float f)
{
    const unsigned int u = *(unsigned int*)&f;
    return (u&0x7F800000) == 0x7F800000 && (u&0x7FFFFF);    // Both NaN and qNan.
}

inline bool LogitsDuplicated(std::vector<float> & arr1, std::vector<float> & arr2)
{
    int compareQty = 5;
    if(arr1.size() < compareQty || arr2.size() < compareQty || arr1.size()!=arr2.size())
    {
        printf("\nError: Logit array sizes are bad!\n");
        return false;
    }
    for(int i=0;i<compareQty;++i)
    {
        if(arr1[i]!=arr2[i])
        {
            return false;
        }
    }
    return true;
}


static std::string FileFormatTokenizeID(int id, FileFormat file_format, bool return_special = false)
{
    if(id<0)
    {
        return ""; //placeholder IDs cannot be tokenized!
    }
    if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
    {
        return std::string(llama_v2_token_to_str(llama_ctx_v2, id));
    }
    else if (file_format == FileFormat::GGJT_3)
    {
        return std::string(llama_v3_token_to_str(llama_ctx_v3, id));
    }
    else if(file_format == FileFormat::GGUF_GENERIC)
    {
        return std::string(common_token_to_piece(llama_ctx_v4, id, return_special));
    }
    else
    {
        return vocab.id_to_token[id];
    }
}

static void TokenizeString(const std::string & str_to_tokenize, std::vector<int> & output_tokens, FileFormat file_format, bool add_bos=true)
{
    if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2  || file_format == FileFormat::GGJT_3 || file_format == FileFormat::GGUF_GENERIC)
    {
        if(file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 )
        {
            output_tokens = ::llama_v2_tokenize(llama_ctx_v2, str_to_tokenize, add_bos);
        }
        else if (file_format == FileFormat::GGML)
        {
            output_tokens = ::legacy_llama_v2_tokenize(llama_ctx_v2, str_to_tokenize, add_bos);
        }
        else if (file_format == FileFormat::GGJT_3)
        {
            output_tokens = ::llama_v3_tokenize(llama_ctx_v3, str_to_tokenize, add_bos);
        }
        else
        {
            output_tokens = ::common_tokenize(llama_ctx_v4, str_to_tokenize, add_bos, true);
            if(add_bos)
            {
                const llama_vocab * tmpvocab = llama_model_get_vocab(&(llama_ctx_v4->model));
                llama_token bostoadd = llama_vocab_bos(tmpvocab);
                if(bostoadd != LLAMA_TOKEN_NULL) //if bos does not exist, do not add it
                {
                    if(output_tokens.size()==0)
                    {
                        output_tokens.push_back(bostoadd);
                    }
                    else
                    {
                        if(output_tokens[0]!=bostoadd)
                        {
                            output_tokens.insert(output_tokens.begin(), 1, bostoadd);
                        }
                    }
                }
            }
        }
    }
    else
    {
        // tokenize the prompt
        output_tokens = ::gpt_tokenize(vocab, str_to_tokenize);
    }
}
static int GetEosID(FileFormat file_format, int32_t n_vocab)
{
    unsigned int eosID = 0;

    if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 || file_format == FileFormat::GGJT_3 || file_format == FileFormat::GGUF_GENERIC)
    {
        if(file_format == FileFormat::GGUF_GENERIC)
        {
            const llama_vocab * tmpvocab = llama_model_get_vocab(&(llama_ctx_v4->model));
            eosID = llama_vocab_eos(tmpvocab);
        }
        else if(file_format == FileFormat::GGJT_3)
        {
            eosID = llama_v3_token_eos();
        }
        else
        {
            eosID = llama_v3_token_eos();
        }
    }
    else
    {
        if (file_format == FileFormat::GPT2_1 ||
        file_format == FileFormat::GPT2_2 ||
        file_format == FileFormat::GPT2_3 ||
        file_format == FileFormat::GPT2_4 ||
        file_format == FileFormat::GPTJ_1 ||
        file_format == FileFormat::GPTJ_2 ||
        file_format == FileFormat::GPTJ_3 ||
        file_format == FileFormat::GPTJ_4 ||
        file_format == FileFormat::GPTJ_5)
        {
            eosID = 50256;
            if (n_vocab <= eosID)
            {
                //special case, starcoder models use ID 0 for EOS
                eosID = 0;
            }
        }

        if (file_format == FileFormat::RWKV_1 ||
            file_format == FileFormat::RWKV_2 ||
            file_format == FileFormat::NEOX_1 ||
            file_format == FileFormat::NEOX_2 ||
            file_format == FileFormat::NEOX_3 ||
            file_format == FileFormat::NEOX_4 ||
            file_format == FileFormat::NEOX_5 ||
            file_format == FileFormat::NEOX_6 ||
            file_format == FileFormat::NEOX_7 ||
            file_format == FileFormat::MPT_1)
        {
            eosID = 0;
        }
    }
    return eosID;
}
static int GetEotID(FileFormat file_format)
{
    if(file_format == FileFormat::GGUF_GENERIC)
    {
        const llama_vocab * tmpvocab = llama_model_get_vocab(&(llama_ctx_v4->model));
        return llama_vocab_eot(tmpvocab);
    }
    return -1;
}

static float LowestLogit(const std::vector<float> & logits)
{
    int topid = std::min_element(logits.begin(), logits.end()) - logits.begin();
    float v = logits[topid];
    return (v < 0 ? (v-8) : 0);
}
static float LowestLogit(const float *logits, size_t size)
{
    if (size == 0) {
        // Handle the case of an empty array
        return 0.0;
    }
    int topid = std::min_element(logits, logits + size) - logits;
    float v = logits[topid];
    return (v < 0 ? (v-8) : 0);
}

static std::string RemoveBell(const std::string & input) //removes the bell character
{
    std::string word2;
    std::remove_copy(input.begin(), input.end(), std::back_inserter(word2), '\a');
    return word2;
}

static std::string get_tok_vec_str(std::vector<int> &embd)
{
    std::string tmp = "";
    for (auto id : embd)
    {
        tmp += "'" + FileFormatTokenizeID(id, file_format, true) + " (" + std::to_string(id) + ")', ";
    }
    ::utreplace(tmp, "\n", "\\n");
    return tmp;
}
static void print_tok_vec_str(std::vector<int> &vec)
{
    printf("\n[%s]\n", get_tok_vec_str(vec).c_str());
}

bool allExtendedUnicode(const std::string& str) {
    if(str.size()==0)
    {
        return false;
    }
    for (unsigned char c : str) {
        if (c <= 127) {
            return false;
        }
    }
    return true;
}

// Find tokens that completely contain `str`, either as a single token, or as a sequence of tokens.
// It's important to use a hash map for head tokens because some models have many of them.
// For example, the Llama 3 tokenizer has 6570 tokens containing the period ('.') character.
// Single tokens are allowed to extend past `str` at the front and back. This is to allow, for
// instance, the token '.\n' to be a head for both '.' and '\n'. However if a head token
// begins a multi-token sequence, the head can only extend past `str` at the beginning. The
// tail tokens are generated by tokenizing the remainder.
// If max_tail_len is >= 0, the maximum token length of a tail sequence is clamped to this value.
static void GetOverlappingTokenSequences(const std::string& str, std::unordered_multimap<gpt_vocab::id, std::vector<gpt_vocab::id>>& token_sequences, int max_tail_len = -1) {
    bool isAllExtendedUnicode = allExtendedUnicode(str);
    for(int v=0;v<n_vocab;++v)
    {
        std::string word = FileFormatTokenizeID(v, file_format, true);
        if (word.find(str) != std::string::npos)
        {
            // The string is entirely contained within this single token.
            // Ensure that token_sequences only contains one key-value-pair with an empty value.
            auto its = token_sequences.equal_range(v);
            bool empty = false;
            for (auto it = its.first; it != its.second; ++it) {
                if (it->second.empty()) {
                    empty = true;
                    break;
                }
            }
            if (!empty) {
                token_sequences.emplace(v, std::vector<gpt_vocab::id>());
            }
        } else {
            // Check whether a prefix of the string overlaps with a suffix of the token.
            // Just do a naive O(N^2) search, since the worst case is limited by the
            // maximum character length of a token in the vocabulary.
            size_t word_len = word.size(), str_len = str.size();
            size_t pos = -1;
            while ((pos = word.find(str[0], pos + 1)) != std::string::npos) {
                bool match = true;
                size_t i;
                for (i = 1; i < str_len && i + pos < word_len; ++i) {
                    if (word[pos + i] != str[i]) {
                        match = false;
                        break;
                    }
                }
                if (match && !isAllExtendedUnicode) {
                    // We matched to the end of the string. Since `str` is not contained in `word`,
                    // there must be trailing letters in `str`.
                    std::vector<gpt_vocab::id> tokenization;
                    TokenizeString(str.substr(i), tokenization, file_format, false);
                    if (max_tail_len >= 0 && tokenization.size() > max_tail_len) {
                        tokenization.resize(max_tail_len);
                    }

                    // Ensure we don't already have a duplicate matching tokenization.
                    auto its = token_sequences.equal_range(v);
                    bool found = false;
                    for (auto it = its.first; it != its.second; ++it) {
                        if (tokenization == it->second) {
                            found = true;
                            break;
                        }
                    }
                    if (!found)
                    {
                        token_sequences.emplace(v, tokenization);
                    }
                }
            }
        }
    }
}

// Function to convert a UTF-8 encoded string to lowercase
static std::string toLowerCase(const std::string& str) {
    std::string result;
    std::locale loc;

    for (char ch : str) {
        result += std::tolower(ch, loc); // Use locale-aware tolower
    }

    return result;
}


void ContextRewind(std::vector<int> &embd, std::vector<int> &current_context_tokens, int &n_past, std::vector<int> &last_n_tokens, const int amount_rewind)
{
    if(amount_rewind<=0 || current_context_tokens.size()==0)
    {
        return; //do nothing
    }
    if(embd.size()>1)
    {
        printf("\nWARNING: Don't use context rewind when in batch processing phase!\n");
        return;
    }
    bool is_mamba = (file_format == FileFormat::GGUF_GENERIC && file_format_meta.model_architecture==GGUFArch::ARCH_MAMBA);
    bool is_rwkv_new = (file_format == FileFormat::GGUF_GENERIC && file_format_meta.model_architecture==GGUFArch::ARCH_RWKV);
    if(file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2 || is_mamba || is_rwkv_new)
    {
        printf("\nWARNING: RNN models do not support context rewind!\n");
        return;
    }

    if (amount_rewind >= last_n_tokens.size())
    {
        last_n_tokens.clear();
    }
    else
    {
        last_n_tokens.resize(last_n_tokens.size() - amount_rewind);
    }

    if(amount_rewind >= top_picks_history.size())
    {
        top_picks_history.clear();
    }
    else
    {
        top_picks_history.resize(top_picks_history.size() - amount_rewind);
    }

    if (amount_rewind >= current_context_tokens.size())
    {
        current_context_tokens.clear();
    }
    else
    {
        current_context_tokens.resize(current_context_tokens.size() - amount_rewind);
    }

    if (amount_rewind >= n_past)
    {
        n_past = 0;
    }
    else
    {
        n_past -= amount_rewind;
    }

    if (file_format == FileFormat::GGUF_GENERIC)
    {
        llama_kv_cache_seq_rm(llama_ctx_v4, 0, n_past, -1);
        if(draft_ctx)
        {
            llama_kv_cache_seq_rm(draft_ctx, 0, n_past, -1);
        }
    }

    embd.clear();
    if(current_context_tokens.size()>0)
    {
        embd.push_back(current_context_tokens[current_context_tokens.size()-1]);
    }
}

const char * kcpp_print_system_info(void) {
    ggml_cpu_init(); // some ARM features are detected at runtime

    static std::string s;

    s  = "";
    s += "AVX = "         + std::to_string(ggml_cpu_has_avx())         + " | ";
    s += "AVX_VNNI = "    + std::to_string(ggml_cpu_has_avx_vnni())    + " | ";
    s += "AVX2 = "        + std::to_string(ggml_cpu_has_avx2())        + " | ";
    s += "AVX512 = "      + std::to_string(ggml_cpu_has_avx512())      + " | ";
    s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
    s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
    s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | ";
    s += "AMX_INT8 = "    + std::to_string(ggml_cpu_has_amx_int8())    + " | ";
    s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
    s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
    s += "SVE = "         + std::to_string(ggml_cpu_has_sve())         + " | ";
    s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
    s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
    s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
    s += "RISCV_VECT = "  + std::to_string(ggml_cpu_has_riscv_v())     + " | ";
    s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
    s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
    s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
    s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
    s += "LLAMAFILE = "   + std::to_string(ggml_cpu_has_llamafile())   + " | ";

    return s.c_str();
}

//loads a model for speculative decoding.
static void speculative_decoding_setup(std::string spec_model_filename, const llama_model_params & base_model_params, const llama_context_params & base_ctx_params, int base_n_vocab, const float * draft_gpusplit, int draft_gpulayers)
{
    llama_model_params draft_model_params = llama_model_default_params();
    llama_context_params draft_ctx_params = llama_context_default_params();

    draft_model_params.use_mmap = base_model_params.use_mmap;
    draft_model_params.use_mlock = base_model_params.use_mlock;
    draft_model_params.n_gpu_layers = draft_gpulayers; //layers offload the speculative model.
    draft_ctx_params.n_ctx = base_ctx_params.n_ctx;
    draft_ctx_params.logits_all = false;
    draft_ctx_params.offload_kqv = base_ctx_params.offload_kqv;
    draft_model_params.main_gpu = base_model_params.main_gpu;
    draft_model_params.split_mode = llama_split_mode::LLAMA_SPLIT_MODE_LAYER;
    #if defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN)
    bool ts_all_zero = true;
    for (int i = 0; i < tensor_split_max; ++i) {
        if (draft_gpusplit[i] != 0.0f) {
            ts_all_zero = false;
            break;
        }
    }
    if(!ts_all_zero)
    {
        printf("\nApplying Draft GPU Split...\n");
        draft_model_params.tensor_split = draft_gpusplit;
    }
    #endif
    draft_ctx_params.n_batch = base_ctx_params.n_batch;
    draft_ctx_params.n_ubatch = base_ctx_params.n_ubatch;
    draft_ctx_params.n_threads = base_ctx_params.n_threads;
    draft_ctx_params.n_threads_batch =  base_ctx_params.n_threads_batch;
    draft_ctx_params.flash_attn = base_ctx_params.flash_attn;
    draft_ctx_params.type_k = base_ctx_params.type_k;
    draft_ctx_params.type_v = base_ctx_params.type_v;

    llama_model * draftmodel = llama_model_load_from_file(spec_model_filename.c_str(), draft_model_params);
    draft_ctx = llama_new_context_with_model(draftmodel, draft_ctx_params);
    if(draft_ctx == NULL)
    {
        printf("Error: failed to load speculative decoding draft model '%s'\n", spec_model_filename.c_str());
        printf("Speculative Decoding will not be used!\n");
    }
    else
    {
        const llama_vocab * tmpvocab = llama_model_get_vocab(draftmodel);
        int draftvocab = llama_vocab_n_tokens(tmpvocab);
        if(llama_model_is_recurrent(draftmodel))
        {
            printf("Error: Speculative decoding cannot be used with Recurrent draft models!\n");
            llama_free(draft_ctx);
            draft_ctx = nullptr;
        }
        else if(draftvocab!=base_n_vocab)
        {
            if(debugmode==1)
            {
                printf("WARNING: Draft model vocab of (%d) does not match base vocab of (%d).\nIn debug mode, this restriction is bypassed. However, speculative decoding may malfunction!\n",draftvocab,base_n_vocab);
            }
            else if((draftvocab >= base_n_vocab-512) || (draftvocab <= base_n_vocab+512))
            {
                printf("WARNING: Draft model vocab of (%d) does not match base vocab of (%d).\nIn Croco.Cpp, a tolerance of +/- 512 tokens is allowed to account for some variations between the base models and their finetunes/updates and other self-merged frankenmodels + eventual finetunes of those.\nHowever, speculative decoding may malfuction in such cases if the difference between their vocab/tokenizers is too big!\n",draftvocab,base_n_vocab);
            }
            else
            {
                printf("Error: Draft model vocab of (%d) does not match base vocab of (%d), or is above 512 tokens of difference. Speculative decoding cannot be used!\n",draftvocab,base_n_vocab);
                printf("If you REALLY want to override this, run in --debugmode and this restriction will be completely disabled. However, you might encounter unwanted results!\n");
                llama_free(draft_ctx);
                draft_ctx = nullptr;
            }
        }
    }
}

static speculative_draft_result speculative_decoding_eval_chunk(llama_context * draft_ctx, llama_context * main_ctx, const llama_tokens & embd, const int n_vocab, const int & n_past)
{
    speculative_draft_result results;
    results.draft_success = false;
    if(embd.size()==0)
    {
        printf("\nERROR: Speculate on empty batch!\n");
        return results;
    }
    if(embd.size()>1)
    {
        printf("\nERROR: Speculative decoding applied on large batch!\n");
        return results;
    }
    int draft_npast = n_past;
    int actual_npast = n_past;
    std::vector<int> temp_embd;
    std::vector<int> drafted_ids;
    temp_embd.push_back(embd[0]);
    drafted_ids.push_back(embd[0]);
    for(int i=0;i<speculative_chunk_amt;++i)
    {
        kcpp_embd_batch batch1 = kcpp_embd_batch(temp_embd, draft_npast, false, false);
        auto draftok = (llama_decode(draft_ctx, batch1.batch)==0);
        if(!draftok)
        {
            printf("\nERROR: Speculative draft model 1 failed!\n");
            return results;
        }
        float * draftlogits = llama_get_logits(draft_ctx);
        //greedy sample the draft model
        int topid = std::max_element(draftlogits, draftlogits + n_vocab) - draftlogits;
        drafted_ids.push_back(topid);
        temp_embd.clear();
        temp_embd.push_back(topid);
        ++draft_npast;
    }
    //now that we have our drafted tokens, we form a batch and PP it

    std::vector<int> real_embd = drafted_ids;
    real_embd.pop_back();
    bool use_mrope = (file_format==FileFormat::GGUF_GENERIC && file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL);
    kcpp_embd_batch batch2 = kcpp_embd_batch(real_embd, actual_npast, use_mrope, true);
    auto draftok = (llama_decode(main_ctx, batch2.batch)==0); //actual eval for big model
    if(!draftok)
    {
        printf("\nERROR: Speculative draft model 2 failed!\n");
        return results;
    }
    results.drafted_amount = 0;
    for(int i=0;i<drafted_ids.size()-1;++i)
    {
         results.drafted_amount += 1;
        float * fulllogits = llama_get_logits_ith(main_ctx,i);
        results.draftids.push_back(drafted_ids[i+1]);
        results.actual_logits.push_back(fulllogits);
    }
    results.draft_success = true;
    return results;
}

// KCPP SAMPLING FUNCTIONS
void sample_softmax(llama_token_data_array * cur_p) {
    GGML_ASSERT(cur_p->size > 0);

    // Sort the logits in descending order
    if (!cur_p->sorted) {
        std::sort(cur_p->data, cur_p->data + cur_p->size, [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        });
        cur_p->sorted = true;
    }

    float max_l = cur_p->data[0].logit;
    float cum_sum = 0.0f;

    for (size_t i = 0; i < cur_p->size; ++i) {
        float p = expf(cur_p->data[i].logit - max_l);
        cur_p->data[i].p = p;
        cum_sum += p;
    }

    for (size_t i = 0; i < cur_p->size; ++i) {
        cur_p->data[i].p /= cum_sum;
    }
}

void sample_top_k(llama_token_data_array * cur_p, int32_t k) {
    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
    // if (k >= (int32_t)cur_p->size) {
    //     return;
    // }

    if (k <= 0) {
        k = cur_p->size;
    }

    k = std::max(k, (int) 1); //min keep of 1
    k = std::min(k, (int) cur_p->size);

    // Sort scores in descending order
    if (!cur_p->sorted) {
        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        };
        if (k <= 128) {
            std::partial_sort(cur_p->data, cur_p->data + k, cur_p->data + cur_p->size, comp);
        } else {
            constexpr int   nbuckets     = 128;
            constexpr float bucket_low   = -10.0f;
            constexpr float bucket_high  =  10.0f;
            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
            constexpr float bucket_inter = -bucket_low * bucket_scale;

            std::vector<int> bucket_idx(cur_p->size);
            std::vector<int> histo(nbuckets, 0);

            for (int i = 0; i < (int)cur_p->size; ++i) {
                const float val = cur_p->data[i].logit;
                int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
                ib = std::max(0, std::min(nbuckets-1, ib));
                bucket_idx[i] = ib;
                ++histo[ib];
            }
            int nhave = 0;
            int ib = nbuckets - 1;
            for ( ; ib >= 0; --ib) {
                nhave += histo[ib];
                if (nhave >= k) {
                    break;
                }
            }
            std::vector<llama_token_data> tmp_tokens(nhave);
            auto * ptr = tmp_tokens.data();
            std::vector<llama_token_data*> bucket_ptrs;
            bucket_ptrs.reserve(nbuckets - ib);
            for (int j = nbuckets - 1; j >= ib; --j) {
                bucket_ptrs.push_back(ptr);
                ptr += histo[j];
            }
            for (int i = 0; i < (int)cur_p->size; ++i) {
                int j = bucket_idx[i];
                if (j >= ib) {
                    *bucket_ptrs[nbuckets-1-j]++ = cur_p->data[i];
                }
            }

            ptr = tmp_tokens.data();
            int ndone = 0;
            for (int j = nbuckets-1; j > ib; --j) {
                std::sort(ptr, ptr + histo[j], comp);
                ptr += histo[j];
                ndone += histo[j];
            }
            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);

            std::memcpy(cur_p->data, tmp_tokens.data(), k*sizeof(llama_token_data));

        }
        cur_p->sorted = true;
    }
    cur_p->size = k;
}

llama_token sample_token(llama_token_data_array * candidates, std::mt19937 & rng)
{
    sample_softmax(candidates);
    std::vector<float> probs;
    probs.reserve(candidates->size);
    TopPicksData newpick;

    for (size_t i = 0; i < candidates->size; ++i) {
        probs.push_back(candidates->data[i].p);
    }

    std::discrete_distribution<> dist(probs.begin(), probs.end());
    int idx = dist(rng);

    newpick.selected_token = FileFormatTokenizeID(candidates->data[idx].id, file_format, true);
    float rp1 = (candidates->data[idx].p<=0.0001?0.0001f:candidates->data[idx].p);
    float sprob = logf(rp1);
    sprob = (sprob > 999.0f?999.0f:sprob);
    sprob = (sprob < -999.0f?-999.0f:sprob);
    newpick.selected_logprob = sprob;
    newpick.selected_probability = candidates->data[idx].p;
    newpick.selected_tokenid = candidates->data[idx].id;
    for (size_t i = 0; (i < candidates->size && i<logprobs_max); ++i)
    {
        newpick.tokens.push_back(FileFormatTokenizeID(candidates->data[i].id, file_format, true));
        float rp2 = (candidates->data[i].p<=0.0001?0.0001f:candidates->data[i].p);
        float prob = logf(rp2);
        prob = (prob > 999.0f?999.0f:prob);
        prob = (prob < -999.0f?-999.0f:prob);
        newpick.logprobs.push_back(prob);
        newpick.p.push_back(candidates->data[i].p);
        newpick.tokenid.push_back(candidates->data[i].id);
    }

    top_picks_history.push_back(newpick);

    llama_token result = candidates->data[idx].id;
    return result;
}

llama_token sample_token_mirostat(int n_vocab, llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, int m, float * mu)
{
    float N = float(n_vocab);
    sample_softmax(candidates);
    // Estimate s_hat using the most probable m tokens
    float s_hat = 0.0;
    float sum_ti_bi = 0.0;
    float sum_ti_sq = 0.0;
    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
        float t_i = logf(float(i + 2) / float(i + 1));
        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
        sum_ti_bi += t_i * b_i;
        sum_ti_sq += t_i * t_i;
    }
    s_hat = sum_ti_bi / sum_ti_sq;
    // Compute k from the estimated s_hat and target surprise value
    float epsilon_hat = s_hat - 1;
    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
    // Sample the next word X using top-k sampling
    sample_top_k(candidates, int(k));
    llama_token X = sample_token(candidates, rng);    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;
    // Update mu using the learning rate and error
    *mu = *mu - eta * e;
    return X;
}

llama_token sample_token_mirostat_v2(llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, float * mu)
{
    sample_softmax(candidates);
    // Truncate the words with surprise values greater than mu
    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return -log2f(candidate.p) > *mu;
    }));

    if (candidates->size == 0) {
        candidates->size = 1;
    }

    // Normalize the probabilities of the remaining words
    sample_softmax(candidates);
    // Sample the next word X from the remaining words
    llama_token X = sample_token(candidates,rng);

    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;
    // Update mu using the learning rate and error
    *mu = *mu - eta * e;
    return X;
}

// Top-a (remove all tokens that have softmax probability less than top_a*m^2 where m is the maximum softmax probability)
// top-a 0 is off (no effect)
void sample_top_a(llama_token_data_array * candidates, float a, size_t min_keep) {
    if (a <= 0.0f || candidates->size<=1) {
        return;
    }

    sample_softmax(candidates);

    // Compute the cumulative probabilities
    float maxprob = candidates->data[0].p;

    float threshold = a * maxprob * maxprob; //tokens with probs less than this are removed
    size_t last_idx = candidates->size;

    for (size_t i = 0; i < candidates->size; ++i) {
        // Go until we reach a value under the threshold
        float checkprob = candidates->data[i].p;
        if (checkprob < threshold && i >= min_keep) {
            last_idx = i;
            break;
        }
    }
    // printf("\n\nCandidates: %d, A:%f, MaxProb: %f, Threshold: %f, LastIdx: %d",candidates->size,a,maxprob,threshold,last_idx);
    // printf("\nCandidates: %f %f %f %f\n",candidates->data[0].p,candidates->data[1].p,candidates->data[2].p,candidates->data[3].p);

    // Resize the output vector to keep only the selected tokens
    candidates->size = last_idx;
}

void sample_xtc(llama_token_data_array * candidates, float xtc_threshold, float xtc_probability, std::mt19937 & rng)
{
    if (xtc_threshold > 0.5f || xtc_probability <= 0.0f || candidates->size <= 1) {
        return;
    }

    std::uniform_real_distribution<float> dist(0.0f, 1.0f);
    float roll = dist(rng);
    if(roll>=xtc_probability) //if dice roll fails, skip xtc
    {
        return;
    }

    sample_softmax(candidates);

    //calculate how many tokens cross the xtc threshold
    size_t last_idx = candidates->size;
    for (size_t i = 0; i < candidates->size; ++i) {
        // Go until we reach a value under the threshold
        float checkprob = candidates->data[i].p;
        if (checkprob < xtc_threshold) {
            last_idx = i;
            break;
        }
    }

    if(last_idx>1) //if there are 2 or more viable candidates
    {
        if (debugmode==1) {
            printf("XTC penalties [");
        }
        // then remove all other tokens above threshold EXCEPT the least likely one
        for (size_t i = 0; i < last_idx - 1; ++i) {
            if (debugmode==1)
            {
                gpt_vocab::id token = candidates->data[i].id;
                std::string tokenizedstr = FileFormatTokenizeID(token, file_format);
                ::utreplace(tokenizedstr, "\n", "\\n");
                printf("%s(%s %.02f%%)", i == 0 ? "" : " ", RemoveBell(tokenizedstr).c_str(), 100.f * candidates->data[i].p);
            }
            candidates->data[i].logit -= 999.0f; //infinity gets wonky results downstream, this hack works well enough
        }
        if (debugmode==1) {
            printf("]\n");
        }
        candidates->sorted = false;

    }  //otherwise xtc does not do anything

    // printf("\n\nCandidates: %d, Threshold: %f, LastIdx: %d",candidates->size,xtc_threshold,last_idx);
    // printf("\nCandidates: %f %f %f %f\n",candidates->data[0].p,candidates->data[1].p,candidates->data[2].p,candidates->data[3].p);

}

void sample_dry(int n_ctx, int penalty_range, float penalty_multiplier, float penalty_base, int allowed_length, const std::unordered_multimap<gpt_vocab::id, std::vector<gpt_vocab::id>>& restart_sequences, llama_token_data_array * candidates) {
    if (penalty_multiplier <= 0.0f || penalty_base <= 0.0f) {
        return;
    }
    if (penalty_range <= 0 || penalty_range>n_ctx) {
        penalty_range = n_ctx;
    }
    auto last_n_repeat = std::min(std::min((int)current_context_tokens.size(), penalty_range), n_ctx);
    if (last_n_repeat <= allowed_length) {
        return;
    }
    const llama_token * last_tokens = current_context_tokens.data() + current_context_tokens.size() - last_n_repeat;

    dry_repeat_count.assign(last_n_repeat, 0);
    dry_max_token_repeat.clear();

    // Step 1: Look for restart sequences to limit the maximum repetition length.
    // Work backwards through the context looking for any token that begins a restart sequence.
    //
    // The collection `restart_sequences` is a mapping from a "head" token to all "tail"
    // sequences that together comprise a restart sequence. This allows us to quickly check
    // whether each token is the head of a complete sequence. Most restart sequences are actually
    // a single token, and for these the "tail" is an empty vector.
    //
    // If the token is a "head", test all restart sequences that begin with this token
    // (there will often only be one sequence for each token, but if sequences like 'aaaq1' and
    // 'aaa1' are used as restart strings, both could start with 'aaa' when tokenized). The
    // longest matching sequence (if any) is used to limit the maximum repetition length.
    //
    // Note that in the case case of a short sequence contained in a longer one, this might fail to
    // find the smallest value for `rep_limit`. For example, if 'amniotic' and 'ni' are both used as
    // restart sequences, 'ni' will be found first, and since it's shorter it will fail to suppress
    // 'otic'. This is a minor issue since fully contained restart sequences are likely to be rare.
    //
    // This is theoretically worst-case O(N^2) for arbitrary restart sequences, which is why we
    // have already clamped the maximum tail sequence length when generating `restart_sequences`.
    // With clamping, this scan is O(N) in the context length.

    int rep_limit = last_n_repeat;
    for (size_t i = 0; i < last_n_repeat; ++i) {
        size_t ix = last_n_repeat - 1 - i;
        auto its = restart_sequences.equal_range(last_tokens[ix]);
        if (its.first == restart_sequences.end()) {
            continue;
        }
        int longest_match = -1;
        for (auto it = its.first; it != its.second; ++it) {
            // Note that (*it) does not contain the head character, so seq_len will be
            // the restart sequence length minus 1.
            // In the common case of a single-token restart sequence, (*it) will be empty
            // and we will trivially match.
            int seq_len = (int)it->second.size();
            if (seq_len > longest_match && seq_len <= i) {
                bool match = true;
                for (size_t offset = 0; offset < seq_len; ++offset) {
                    // The +1 when indexing `last_tokens` is because we already matched the head.
                    if (it->second[offset] != last_tokens[ix + 1 + offset]) {
                        match = false;
                        break;
                    }
                }
                if (match) {
                    longest_match = seq_len;
                }
            }
        }
        if (longest_match >= 0) {
            // We found a restart sequence starting `i` tokens from the end and continuing for
            // `longest_match` tokens.
            rep_limit = (int)i - longest_match;
            break;
        }
    }
    if (rep_limit <= allowed_length) {
        return;
    }

    // Step 2: Iterate in reverse over the last N tokens of the context, using the "Z-algorithm" (in
    // the reverse direction) to efficiently compute the positions and lengths of suffixes appearing
    // elsewhere in the context. We limit the suffix length to `rep_limit` to respect restart sequences.
    //
    // This algorithm is not currently documented on Wikipedia, but there is a clear description here:
    // https://ivanyu.me/blog/2014/10/15/z-algorithm/
    //
    // The code below is adapted from the public domain implementation by the same author here:
    // https://github.com/ivanyu/string-algorithms/blob/master/z_algorithm.py
    //
    // Example:
    // Last N tokens: a b c c b c y a b c
    // Repeat counts: 0 0 3 1 0 2 0 0 0 0
    //                    ^
    //   This `3` means that the last three tokens of the context (a b c) also appear here.
    //
    // This step is worst case O(N) since the Z-algorithm is linear, despite the appearance of nested
    // for/while loops. This can be seen by observing that the `lt` and `rt` bounds are set after each
    // repeated suffix is detected (i.e. after each while loop when n > 0). These bound variables
    // ensure that the inner while loops only examine each token in the context once as the outer
    // for loop iterates over the context.

    {
        const int last = last_n_repeat - 1;
        int rt = 0, lt = 0;

        for (int k = 1; k < last_n_repeat; ++k) {
            if (k > rt) {
                // If k is outside the current Z-box, do naive computation.
                int n = 0;
                while (n + k < last_n_repeat && last_tokens[last - n] == last_tokens[last - (n+k)]) {
                    ++n;
                }
                dry_repeat_count[last - k] = std::min(n, rep_limit);
                if (n > 0) {
                    lt = k;
                    rt = k+n-1;
                }
            } else {
                // If k is inside the current Z-box, consider two cases.

                int p = k - lt; // Pair index.
                int right_part_len = rt - k + 1;

                if (dry_repeat_count[last - p] < right_part_len) {
                    int n = std::min(dry_repeat_count[last - p], rep_limit);
                    dry_repeat_count[last - k] = n;
                } else {
                    int i = rt + 1;
                    while (i < last_n_repeat && last_tokens[last - i] == last_tokens[last - (i - k)]) {
                        i += 1;
                    }

                    int n = std::min(i - k, rep_limit);
                    dry_repeat_count[last - k] = n;

                    lt = k;
                    rt = i - 1;
                }
            }
        }
    }

    // Step 3: Iterate over dry_repeat_count and last_tokens, examining the maximum repeat length
    // that would be generated by emitting each new token that would extend a sequence.
    //
    // Following the same example as above:
    // Last N tokens: a b c c b c y a b c
    // Repeat counts: 0 0 3 1 0 2 0 0 0 0
    //
    // For each non-zero, look ahead one token. This token, if emitted, would extend the repetition.
    // c: 3 -> 4 (from `a b c` to `a b c c`)
    // b: 1 -> 2 (from `c` to `c b`)
    // y: 2 -> 3 (from `b c` to `b c y`)

    for (size_t i = 0; i < last_n_repeat - 1; ++i) {
        int repeat_len = dry_repeat_count[i];
        if (repeat_len >= allowed_length) {
            // This token ends a repeat, so the next token would continue one.
            // By convention, the value of `repeat_len` only includes the tokens currently
            // in the context, not the new token that would be added.
            gpt_vocab::id token = last_tokens[i + 1];
            // Track the maximum sequence ending in this token.
            const auto& it = dry_max_token_repeat.find(token);
            if (it == dry_max_token_repeat.end() || it->second < repeat_len) {
                dry_max_token_repeat[token] = repeat_len;
            }
        }
    }

    // Step 4: Apply logit penalties based on the maximum repeat length for relevant tokens.

    // Prevent floating point overflow in `pow(penalty_base, exponent)` by clamping to `max_exponent`.
    // Compute it from `penalty_base` and the approximate log of `std::numeric_limits<float>::max()`
    const float FLOAT_MAX_LOG = 88.7228391f;
    int max_exponent = 0;
    if (penalty_base > 1.000001f) {
        max_exponent = FLOAT_MAX_LOG / std::log(penalty_base);
    }

    if (debugmode==1 && !dry_max_token_repeat.empty()) {
        printf("DRY penalties [");
    }
    size_t count = 0;
    for (const auto& kvp: dry_max_token_repeat) {
        gpt_vocab::id token = kvp.first;
        int repeat_exp = kvp.second - allowed_length;
        if (max_exponent > 0 && repeat_exp > max_exponent) {
            repeat_exp = max_exponent;
        }
        float penalty = penalty_multiplier * pow(penalty_base, repeat_exp);
        if (debugmode==1)
        {
            std::string tokenizedstr = FileFormatTokenizeID(token, file_format);
            ::utreplace(tokenizedstr, "\n", "\\n");
            printf("%s(%s %.02f)", count == 0 ? "" : " ", RemoveBell(tokenizedstr).c_str(), penalty);
        }
        candidates->data[token].logit -= penalty;
        ++count;
    }
    if(count>0)
    {
        candidates->sorted = false;
    }
    if (debugmode==1 && !dry_max_token_repeat.empty()) {
        printf("]\n");
    }
}

void sample_rep_pen(int n_ctx, int rep_pen_range, float rep_pen, float rep_pen_slope, float presence_penalty, llama_token_data_array * candidates_p)
{
    auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx);

    const llama_token * last_tokens =  last_n_tokens.data() + last_n_tokens.size() - last_n_repeat;
    size_t last_tokens_size = last_n_repeat;
    llama_token_data_array * candidates = candidates_p;

    if (last_tokens_size == 0 || (rep_pen == 1.0f && presence_penalty==0)) {
        return;
    }

    const int64_t t_start_sample_us = ggml_time_us();

    // Create a frequency map to count occurrences of each token in last_tokens
    std::unordered_map<llama_token, int> token_count_near;
    std::unordered_map<llama_token, int> token_count_far;
    for (size_t i = 0; i < last_n_repeat; ++i) {
        if((i*2) >= last_n_repeat)
        {
            token_count_near[last_tokens[i]]++;
        }
        else
        {
            token_count_far[last_tokens[i]]++;
        }
    }

    float rep_pen_reduced = rep_pen;
    if(rep_pen_reduced>1.0f)
    {
       rep_pen_reduced = 1.0f + ((rep_pen-1.0f)*rep_pen_slope);
    }
    for (size_t i = 0; i < candidates->size; ++i) {
        const auto token_in_near = token_count_near.find(candidates->data[i].id);
        const auto token_in_far = token_count_far.find(candidates->data[i].id);
        bool in_near = (token_in_near != token_count_near.end());
        bool in_far = (token_in_far != token_count_far.end());
        if (!in_near && !in_far) {
            continue;
        }

        float penalty = (in_near?rep_pen:rep_pen_reduced);

        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
        if (candidates->data[i].logit <= 0) {
            candidates->data[i].logit *= penalty;
        } else {
            candidates->data[i].logit /= penalty;
        }

        candidates->data[i].logit -= presence_penalty;
    }

    candidates->sorted = false;

}

void sample_top_p(llama_token_data_array * cur_p, float p, size_t min_keep) {
    if (p >= 1.0f) {
        return;
    }

    sample_softmax(cur_p);

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = cur_p->size;

    for (size_t i = 0; i < cur_p->size; ++i) {
        cum_sum += cur_p->data[i].p;

        // Check if the running sum is at least p or if we have kept at least min_keep tokens
        // we set the last index to i+1 to indicate that the current iterate should be included in the set
        if (cum_sum >= p && i + 1 >= min_keep) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the top-p tokens
    cur_p->size = last_idx;
}

void sample_min_p(llama_token_data_array * cur_p, float p, size_t min_keep) {
    if (p <= 0.0f || !cur_p->size) {
        return;
    }

    bool min_p_applied = false;

    // if the cur_p aren't sorted, try the unsorted implementation first
    if (!cur_p->sorted) {
        std::vector<llama_token_data> filtered_tokens;

        float max_logit = -FLT_MAX;
        for (size_t i = 0; i < cur_p->size; ++i) {
            max_logit = std::max(max_logit, cur_p->data[i].logit);
        }
        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max

        for (size_t i = 0; i < cur_p->size; ++i) {
            if (cur_p->data[i].logit >= min_logit) {
                filtered_tokens.push_back(cur_p->data[i]);
            }
        }

        // if we have enough values the operation was a success
        if (filtered_tokens.size() >= min_keep) {
            memcpy(cur_p->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
            cur_p->size = filtered_tokens.size();
            min_p_applied = true;
        }
    }

    // if the cur_p are sorted or the unsorted implementation failed, use this implementation
    if (!min_p_applied) {
        // Sort the logits in descending order
        if (!cur_p->sorted) {
            std::sort(cur_p->data, cur_p->data + cur_p->size, [](const llama_token_data & a, const llama_token_data & b) {
                return a.logit > b.logit;
            });
            cur_p->sorted = true;
        }

        const float min_logit = cur_p->data[0].logit + logf(p); // min logit for p_i >= p * p_max
        size_t i = 1; // first token always matches

        for (; i < cur_p->size; ++i) {
            if (cur_p->data[i].logit < min_logit && i >= min_keep) {
                break; // prob too small
            }
        }

        // Resize the output vector to keep only the matching tokens
        cur_p->size = i;
    }
}

void sample_tail_free(llama_token_data_array * cur_p, float z, size_t min_keep) {
    if (z >= 1.0f || cur_p->size <= 2) {
        return;
    }

    sample_softmax(cur_p);

    // Compute the first and second derivatives
    std::vector<float> first_derivatives(cur_p->size - 1);
    std::vector<float> second_derivatives(cur_p->size - 2);

    for (size_t i = 0; i < first_derivatives.size(); ++i) {
        first_derivatives[i] = cur_p->data[i].p - cur_p->data[i + 1].p;
    }
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
    }

    // Calculate absolute value of second derivatives
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = std::abs(second_derivatives[i]);
    }

    // Normalize the second derivatives
    {
        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);

        if (second_derivatives_sum > 1e-6f) {
            for (float & value : second_derivatives) {
                value /= second_derivatives_sum;
            }
        } else {
            for (float & value : second_derivatives) {
                value = 1.0f / second_derivatives.size();
            }
        }
    }

    float cum_sum = 0.0f;
    size_t last_idx = cur_p->size;
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        cum_sum += second_derivatives[i];

        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
        if (cum_sum > z && i >= min_keep) {
            last_idx = i;
            break;
        }
    }

    // Resize the output vector to keep only the tokens above the tail location
    cur_p->size = last_idx;
}

void sampler_typical(llama_token_data_array * cur_p, float p, size_t min_keep) {
    // Reference implementation:
    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
    if (p >= 1.0f) {
        return;
    }

    // Compute the softmax of logits and calculate entropy
    sample_softmax(cur_p);

    float entropy = 0.0f;
    for (size_t i = 0; i < cur_p->size; ++i) {
        if(cur_p->data[i].p>0)
        {
            entropy += -cur_p->data[i].p * logf(cur_p->data[i].p);
        }
    }

    // Compute the absolute difference between negative log probability and entropy for each candidate
    std::vector<float> shifted_scores;
    for (size_t i = 0; i < cur_p->size; ++i) {
        float shifted_score = fabsf(-logf(cur_p->data[i].p) - entropy);
        shifted_scores.push_back(shifted_score);
    }

    // Sort tokens based on the shifted_scores and their corresponding indices
    std::vector<size_t> indices(cur_p->size);
    std::iota(indices.begin(), indices.end(), 0);

    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
        return shifted_scores[a] < shifted_scores[b];
    });

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = indices.size();

    for (size_t i = 0; i < indices.size(); ++i) {
        size_t idx = indices[i];
        cum_sum += cur_p->data[idx].p;

        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
        if (cum_sum > p && i >= min_keep - 1) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the locally typical tokens
    std::vector<llama_token_data> cur_p_new;
    for (size_t i = 0; i < last_idx; ++i) {
        size_t idx = indices[i];
        cur_p_new.push_back(cur_p->data[idx]);
    }

    // Replace the data in cur_p with the cur_p_new data
    std::copy(cur_p_new.begin(), cur_p_new.end(), cur_p->data);
    cur_p->size = cur_p_new.size();
    cur_p->sorted = false;
}

void sample_entropy(llama_token_data_array * cur_p, float min_temp, float max_temp, float exponent_val, float smoothing_factor) {
    // no need to do anything if there is only one (or zero) candidates
    if (cur_p->size <= 1) {
        return;
    }

    // Calculate maximum possible entropy
    float max_entropy = -logf(1.0f / cur_p->size);

    sample_softmax(cur_p);

    // Calculate entropy of the softmax probabilities
    float entropy = 0.0f;
    for (size_t i = 0; i < cur_p->size; ++i) {
        float prob = cur_p->data[i].p;
        if (prob > 0.0f) { // Ensure no log(0)
            entropy -= prob * logf(prob);
        }
    }

    // Normalize the entropy (max_entropy cannot be 0 here because we checked cur_p->size != 1 above)
    float normalized_entropy = entropy / max_entropy;

    // Map the normalized entropy to the desired temperature range using the power function
    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);

    // Apply the dynamically calculated temperature scaling
    for (size_t i = 0; i < cur_p->size; ++i) {
        cur_p->data[i].logit /= dyn_temp;
    }

    // Re-compute softmax probabilities after scaling logits with dynamic temperature
    const double max_l_double = cur_p->data[0].logit;

    double cum_sum_double = 0.0;
    for (size_t i = 0; i < cur_p->size; ++i) {
        double p = exp(cur_p->data[i].logit - max_l_double);
        cur_p->data[i].p = p; // Store the scaled probability
        cum_sum_double += p;
    }

    for (size_t i = 0; i < cur_p->size; ++i) {
        cur_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
    }

    // Only apply smoothing if smoothing_factor is > 0. Do not change base implementation otherwise.
    if (smoothing_factor > 0 && cur_p->size > 1) {
        sample_softmax(cur_p);
        float h = cur_p->data[0].logit; // Find the maximum logit for h to be added after the transformation
        // Apply quadratic transformation using the smoothing_factor
        for (size_t i = 0; i < cur_p->size; ++i)
        {
            float logit_shifted = cur_p->data[i].logit - h;
            cur_p->data[i].logit = -smoothing_factor * logit_shifted * logit_shifted + h;
        }
        sample_softmax(cur_p);
    }

}

void sample_temperature(llama_token_data_array * candidates_p, float temp, float smoothing_factor)
{
    bool isgreedy = false;
    if (temp <= 0)
    {
        // Imitate greedy sampling
        temp = 0.00390625f; //cannot be zero else div0, this is 1/256
        smoothing_factor = 0;
        isgreedy = true;
    }

    for (size_t i = 0; i < candidates_p->size; ++i) {
        candidates_p->data[i].logit /= temp;
    }
    // Only apply smoothing if smoothing_factor is > 0. Do not change base implementation otherwise.
    if (smoothing_factor > 0 && candidates_p->size > 1) {
        sample_softmax(candidates_p);
        float h = candidates_p->data[0].logit; // Find the maximum logit for h to be added after the transformation
        // Apply quadratic transformation using the smoothing_factor
        for (size_t i = 0; i < candidates_p->size; ++i)
        {
            float logit_shifted = candidates_p->data[i].logit - h;
            candidates_p->data[i].logit = -smoothing_factor * logit_shifted * logit_shifted + h;
        }
        sample_softmax(candidates_p);
    }

    if(isgreedy)
    {
        sample_top_k(candidates_p, 1); //only want first candidate
    }
}

void sample_grammar(FileFormat file_format, int32_t n_vocab, llama_token_data_array * candidates, const struct llama_grammar * grammar) {

    const int64_t t_start_sample_us = ggml_time_us();

    bool allow_eos = false;
    for (const auto & stack : grammar->stacks) {
        if (stack.empty()) {
            allow_eos = true;
            break;
        }
    }

    const llama_token eos = GetEosID(file_format,n_vocab);
    const llama_token eot = GetEotID(file_format);

    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
    std::vector<llama_grammar_candidate>                              candidates_grammar;

    for (size_t i = 0; i < candidates->size; ++i) {
        const llama_token id    = candidates->data[i].id;
        const std::string piece = FileFormatTokenizeID(id,file_format);
        if (id == eos || (id==eot && id!=-1)) {
            if (!allow_eos) {
                candidates->data[i].logit = -INFINITY;
            }
        } else if (piece.empty() || piece[0] == 0) {
            candidates->data[i].logit = -INFINITY;
        } else {
            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
        }
    }

    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
    for (const auto & reject : rejects) {
        candidates->data[reject.index].logit = -INFINITY;
    }

}

int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float rep_pen_slope, float presence_penalty, float top_k, float top_a, float top_p, float min_p, float typical_p, float tfs, float temp, std::mt19937 & rng,
int mirostat, float mirostat_tau, float mirostat_eta, float dry_multiplier, float dry_base, int dry_allowed_length, int dry_penalty_last_n, float xtc_threshold, float xtc_probability,
const std::vector<samplers> & sampler_order, llama_grammar * grammar, float dynatemp_range, float dynatemp_exponent, float smoothing_factor)
{
    int id = 0;
    std::vector<llama_token_data> candidates;
    candidates.reserve(n_vocab);
    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
        candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
    }

    for(int i=0;i<logit_biases.size();++i)
    {
        auto & itm = logit_biases[i];
        candidates[itm.token_id].logit += itm.bias;
    }

    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };

    if (grammar != nullptr) {
        sample_grammar(file_format, n_vocab, &candidates_p, grammar);
    }

    //dry always first as logits cannot be resorted
    sample_dry(n_ctx, dry_penalty_last_n, dry_multiplier, dry_base, dry_allowed_length, dry_sequence_breakers, &candidates_p);

    //prefilter to top 3k tokens for improved speed
    sample_top_k(&candidates_p, 512);

    if (mirostat == 1 || mirostat == 2)
    {
        static float mirostat_mu = 2.0f * mirostat_tau;
        const int mirostat_m = 100;
        sample_rep_pen(n_ctx, rep_pen_range, rep_pen, rep_pen_slope, presence_penalty, &candidates_p);
        sample_temperature(&candidates_p, temp, smoothing_factor);
        if (mirostat == 1)
        {
            id = sample_token_mirostat(n_vocab, &candidates_p, rng, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
        }
        else
        {
            id = sample_token_mirostat_v2(&candidates_p, rng, mirostat_tau, mirostat_eta, &mirostat_mu);
        }
    }
    else
    {
        for (int i = 0; i < sampler_order.size(); i++)
        {
            switch (sampler_order[i])
            {
                case KCPP_SAMPLER_TOP_K:
                    sample_top_k(&candidates_p, top_k);
                    break;
                case KCPP_SAMPLER_TOP_A:
                    sample_top_a(&candidates_p, top_a, 1);
                    break;
                case KCPP_SAMPLER_TOP_P:
                    sample_top_p(&candidates_p, top_p, 1);
                    sample_min_p(&candidates_p, min_p, 1);
                    break;
                case KCPP_SAMPLER_TFS:
                    sample_tail_free(&candidates_p, tfs, 1);
                    break;
                case KCPP_SAMPLER_TYP:
                    sampler_typical(&candidates_p, typical_p, 1);
                    break;
                case KCPP_SAMPLER_TEMP:
                    if (dynatemp_range>0)
                    {
                        float dynatemp_min = temp - dynatemp_range;
                        float dynatemp_max = temp + dynatemp_range;
                        //do not allow negative values
                        dynatemp_min = dynatemp_min<0?0:dynatemp_min;
                        dynatemp_max = dynatemp_max<0?0:dynatemp_max;
                        dynatemp_exponent = dynatemp_exponent<0?0:dynatemp_exponent;
                        sample_entropy(&candidates_p, dynatemp_min, dynatemp_max, dynatemp_exponent, smoothing_factor);
                    }
                    else
                    {
                        sample_temperature(&candidates_p, temp, smoothing_factor);
                    }
                    break;
                case KCPP_SAMPLER_REP_PEN:
                    sample_rep_pen(n_ctx, rep_pen_range, rep_pen, rep_pen_slope, presence_penalty, &candidates_p);
                    break;
                default:
                    printf("\nSampleLogits: Unknown Sampler : %d",sampler_order[i]);
                    break;
            }
        }
        //xtc always last
        sample_xtc(&candidates_p, xtc_threshold, xtc_probability, rng);
        id = sample_token(&candidates_p, rng);
    }

    return id;
}


static void grammar_accept_token(FileFormat file_format, int32_t n_vocab, struct llama_grammar * grammar, llama_token token)
{
    if (token == GetEosID(file_format,n_vocab) || (token!=-1 && token == GetEotID(file_format))) {
        for (const auto & stack : grammar->stacks) {
            if (stack.empty()) {
                return;
            }
        }
        GGML_ASSERT(false);
    }
    const std::string piece = FileFormatTokenizeID(token,file_format);

    // Note terminating 0 in decoded string
    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
    const auto & code_points = decoded.first;
    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
        auto prev_stacks = grammar->stacks;
        llama_grammar_accept(grammar, *it);
    }
    grammar->partial_utf8 = decoded.second;
    GGML_ASSERT(!grammar->stacks.empty());
}

static void load_grammar(const std::string & gammarstr)
{
    if(grammar!=nullptr) //on demand free when next grammar is loaded
    {
        llama_grammar_free_impl(grammar);
        grammar = nullptr;
    }

    if (!gammarstr.empty()) {
        parsed_grammar = llama_grammar_parser();
        parsed_grammar.parse(gammarstr.c_str());
        // will be empty (default) if there are parse errors
        if (parsed_grammar.rules.empty()) {
            printf("\nIgnored invalid grammar sampler.");
            return;
        }
        if(debugmode==1)
        {
            parsed_grammar.print(stderr);
        }
        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
        grammar = llama_grammar_init_impl(nullptr,grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
    }
}

static bool kcpp_eval_image(llama_context * ctx_llama, float * img_embd, int num_img_tokens, int n_batch, int * n_past) {
    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
    bool use_mrope = (file_format==FileFormat::GGUF_GENERIC && file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL);

    for (int i = 0; i < num_img_tokens; i += n_batch) {
        int n_eval = num_img_tokens - i;
        if (n_eval > n_batch) {
            n_eval = n_batch;
        }
        float * embd = img_embd+i*n_embd;
        kcpp_embd_batch llava_batch = kcpp_embd_batch(embd, n_eval, *n_past, use_mrope);
        if (llama_decode(ctx_llama, llava_batch.batch)) {
            fprintf(stderr, "\n%s : failed to eval image\n", __func__);
            return false;
        }
        *n_past += n_eval;
    }
    return true;
}

//given an old GGUF context and a new context that has some middle portion removed,
//find and remove the middle portion from the old context from the KV. Does not fast forward after this destructive action
void PurgeMissingTokens(llama_context * ctx, llama_context * draft_ctx, std::vector<int> &current_context_tokens, std::vector<int> &new_context_tokens, const int genamt, const int nctx)
{
    //scan from start old and new ctx, until first mismatch found, save as p0
    //check remaining old and new ctx for longest common subseq, which needs to be at 256 tokens
    //test: longest common subseq (LCQ) MUST start within 0 tokens from end of memory, otherwise purge fails
    //if passed, save beginning of LCQ from old ctx as p1
    //remove all tokens from old ctx between p0 and p1, updating both arrays and kv, then continue as normal

    const int ShortfallThreshold = 200 + std::min((nctx/30),140); //dont trigger shifting if the distance between trimstart and currhead < this
    const int SlackAllowance = 60 + std::min((nctx/60),70); //in case the end text is slightly modified, be forgiving

    int trimstart = 0;
    int new_tokens_len = new_context_tokens.size();
    bool purgeneeded = true;

    for (int i = 0; i < current_context_tokens.size(); ++i)
    {
        if (current_context_tokens[i] == new_context_tokens[i])
        {
            trimstart += 1;
        }
        else
        {
            break;
        }
        if ((i + 2) >= new_tokens_len)
        {
            purgeneeded = false;
            break; //no surgery required
        }
    }

    if(!purgeneeded || new_tokens_len < 6 || current_context_tokens.size() < 6 || new_tokens_len - trimstart < ShortfallThreshold)
    {
        return; //no purge is needed
    }

    //at least this many tokens need to match, otherwise don't bother trimming
    const int LCSTokThreshold = std::max(std::min((new_tokens_len - trimstart) - (genamt+SlackAllowance), (int)(nctx*0.45)), ShortfallThreshold-SlackAllowance);

    auto curr_ctx_without_memory = std::vector<int>(current_context_tokens.begin() + trimstart, current_context_tokens.end());
    auto new_ctx_without_memory = std::vector<int>(new_context_tokens.begin() + trimstart, new_context_tokens.end());

    auto shared = LongestCommonSubseq(curr_ctx_without_memory, new_ctx_without_memory);

    if (shared.size() > LCSTokThreshold && ArrStartWith(new_ctx_without_memory, shared)) // enough tokens in common
    {
        int found = ArrFindIndexOf(current_context_tokens,shared);
        if(found>=0 && found > trimstart)
        {

            //extract the unwanted tokens out from context and KV
            int diff = found - trimstart;
            llama_kv_cache_seq_rm(ctx, 0, trimstart, trimstart + diff);
            llama_kv_cache_seq_add(ctx, 0, trimstart + diff, -1, -diff);
            if(draft_ctx)
            {
                llama_kv_cache_seq_rm(draft_ctx, 0, trimstart, trimstart + diff);
                llama_kv_cache_seq_add(draft_ctx, 0, trimstart + diff, -1, -diff);
            }

            for (size_t i = trimstart + diff; i < current_context_tokens.size() - 1; i++)
            {
                current_context_tokens[i - diff] = current_context_tokens[i];
            }

            printf("\n[Context Shifting: Erased %d tokens at position %d]", diff, trimstart + 1);

            current_context_tokens.resize(current_context_tokens.size() - diff);
        }
    }

}

static int GetBatchSize(int desiredBlasBatchSize,FileFormat in_file_format)
{
    //check if approved to use BLAS
    bool approved_format = !(file_format == FileFormat::BADFORMAT ||
                            file_format == FileFormat::GPT2_1 ||
                            file_format == FileFormat::GPTJ_1 ||
                            file_format == FileFormat::GPTJ_2 ||
                            file_format == FileFormat::RWKV_1 ||
                            file_format==FileFormat::RWKV_2);
    if(!approved_format || desiredBlasBatchSize<=0)
    {
        desiredBlasBatchSize = 32;
    }
    if (file_format != FileFormat::GGML && file_format != FileFormat::GGHF && file_format != FileFormat::GGJT && file_format != FileFormat::GGJT_2 && file_format != FileFormat::GGJT_3 && file_format != FileFormat::GGUF_GENERIC)
    {
        desiredBlasBatchSize = (desiredBlasBatchSize > 128 ? 128 : desiredBlasBatchSize);
    }
    if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
    {
        desiredBlasBatchSize = 1;
    }
    return desiredBlasBatchSize;
}

static int GetUBatchSize(int desiredBlasUBatchSize,FileFormat in_file_format)
{
    //check if approved to use BLAS
    bool approved_format = !(file_format == FileFormat::BADFORMAT ||
                            file_format == FileFormat::GPT2_1 ||
                            file_format == FileFormat::GPTJ_1 ||
                            file_format == FileFormat::GPTJ_2 ||
                            file_format == FileFormat::RWKV_1 ||
                            file_format==FileFormat::RWKV_2);
    if(!approved_format || desiredBlasUBatchSize<=0)
    {
        desiredBlasUBatchSize = 32;
    }
    if (file_format != FileFormat::GGML && file_format != FileFormat::GGHF && file_format != FileFormat::GGJT && file_format != FileFormat::GGJT_2 && file_format != FileFormat::GGJT_3 && file_format != FileFormat::GGUF_GENERIC)
    {
        desiredBlasUBatchSize = (desiredBlasUBatchSize > 128 ? 128 : desiredBlasUBatchSize);
    }
    if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
    {
        desiredBlasUBatchSize = 1;
    }
    return desiredBlasUBatchSize;
}

//this function applies automatic scaling to rope freq base when the desired context exceeds trained context
static float CalcGradientAIRopeFreqBase(float original_rope_base, int n_ctx_train, int n_ctx_desired, GGUFArch model_arch)
{
    if(n_ctx_desired <= n_ctx_train || n_ctx_desired <= 2048)
    {
        return original_rope_base;
    }
	else
	{
        float ctx_multiplier = (model_arch==GGUFArch::ARCH_SOLAR?8.0f:1.0f);
        float chi_ctx_train_value = (n_ctx_train * ctx_multiplier) / 6.28318;
        float chi_ctx_value = (n_ctx_desired * ctx_multiplier) / 6.28318;
        float gradient_ai_rope_freq_base_value = powf(original_rope_base, log10f(chi_ctx_value) / log10f(chi_ctx_train_value));

        printf("Trained max context length (value:%.d).\n", n_ctx_train);
        printf("Desired context length (value:%.d).\n", n_ctx_desired);

        if(debugmode==1)
        {
            printf("Solar context multiplier (value:%.3f).\n", ctx_multiplier);
            printf("Chi context train (value:%.3f).\n", chi_ctx_train_value);
            printf("Chi chosen context (value:%.3f).\n", chi_ctx_value);
            printf("Log Chi context train (value:%.3f).\n", log10f(chi_ctx_train_value));
            printf("Log Chi chosen context (value:%.3f).\n", log10f(chi_ctx_value));
        }

        printf("RoPE Frequency Base value (value:%.3f).\n", original_rope_base);
        printf("RoPE base calculated via Gradient AI formula. (value:%.1f).\n", gradient_ai_rope_freq_base_value);

	    if(model_arch==GGUFArch::ARCH_SOLAR)
        {
            float extended_rope_positive_offset_value = 1 + ((log10f(chi_ctx_value) - log10f(chi_ctx_train_value)) / ((log10f(chi_ctx_value) * log10f(chi_ctx_train_value)) - (log10f(chi_ctx_value) + log10f(chi_ctx_train_value))));
            float rope_freq_base_with_positive_offset = gradient_ai_rope_freq_base_value * extended_rope_positive_offset_value;
            // if(debugmode==1)
            // {
            printf("Extended RoPE Positive Offset (multiplicator) for Solar based models. (value:%.3f).\n", extended_rope_positive_offset_value);
            printf("RoPE base calculated via Gradient AI formula for Solar based models. (value:%.1f).\n", rope_freq_base_with_positive_offset);
            // }
            return rope_freq_base_with_positive_offset;
        }
/* 	    else if(model_arch==GGUFArch::ARCH_MISTRAL_LLAMA_1_AND_2)
        {
            float extended_rope_negative_offset_value = 1 + ((log10f(chi_ctx_value) - log10f(chi_ctx_train_value)) / (3.14159265358979323846 * 3.14159265358979323846));
            float rope_freq_base_with_negative_offset = gradient_ai_rope_freq_base_value / extended_rope_negative_offset_value;
            printf("Extended RoPE Negative Offset (divisor) for Llama 1 and 2 based models. (value:%.3f).\n", extended_rope_negative_offset_value);
            printf("RoPE base calculated via Gradient AI formula for Llama 1 and 2 based models. (value:%.1f).\n", rope_freq_base_with_negative_offset);
            return rope_freq_base_with_negative_offset;
        } */
        else
        {
	        return gradient_ai_rope_freq_base_value;
        }
    }
}

ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format, FileFormatExtraMeta in_file_format_meta)
{
    ggml_time_init();
    kcpp_data = new kcpp_params(); //allocate on heap to avoid linux segfault. yes this leaks memory.

    file_format = in_file_format;
    file_format_meta = in_file_format_meta;
    kcpp_data->n_threads = inputs.threads;
    kcpp_data->n_blasthreads = inputs.blasthreads;
    bool isGguf = (file_format == FileFormat::GGUF_GENERIC);
    kcpp_data->n_batch = GetBatchSize(inputs.blasbatchsize, in_file_format);
    kcpp_data->n_ubatch = GetUBatchSize(inputs.blasubatchsize, in_file_format);
    kcpp_data->flash_attn = inputs.flash_attention;
    kcpp_data->model_filename = inputs.model_filename;
    kcpp_data->use_smartcontext = inputs.use_smartcontext;
    kcpp_data->use_contextshift = inputs.use_contextshift;
    kcpp_data->use_fastforward = inputs.use_fastforward;
    debugmode = inputs.debugmode;
    draft_ctx = nullptr;

    auto clamped_max_context_length = inputs.max_context_length;

    if(clamped_max_context_length>16384 &&
    file_format != FileFormat::GGUF_GENERIC)
    {
        printf("Warning: Only GGUF models can use max context above 16k. Max context lowered to 16k.\n");
        clamped_max_context_length = 16384;
    }

    kcpp_data->n_ctx = clamped_max_context_length;
    max_context_limit_at_load = clamped_max_context_length;

    neox_ctx_v2.hparams.n_ctx  = neox_ctx_v3.hparams.n_ctx
    = gptj_ctx_v1.hparams.n_ctx = gptj_ctx_v2.hparams.n_ctx = gptj_ctx_v3.hparams.n_ctx
    = gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = gpt2_ctx_v3.hparams.n_ctx
    = mpt_ctx_v3.hparams.n_ctx = kcpp_data->n_ctx;

    //determine rope scaling params
    float rope_freq_scale = 1.0f;
    float rope_freq_base = 10000.0f;
    bool overwriteRope = false;
    if(inputs.rope_freq_scale>0.0f)
    {
        rope_freq_scale = inputs.rope_freq_scale;
        rope_freq_base = inputs.rope_freq_base;
        overwriteRope = true;
        printf("Using Custom RoPE scaling (scale:%.3f, base:%.1f).\n",rope_freq_scale,rope_freq_base);
    }
    else
    {
        //Set freq base for all, including non GGUF. If we are using GGUF, this will be overwritten with more accurate values later.
        rope_freq_base = CalcGradientAIRopeFreqBase(10000.0f,2048,kcpp_data->n_ctx, GGUFArch::ARCH_DEFAULT);
        if(file_format==FileFormat::GGUF_GENERIC)
        {
            printf("Using automatic RoPE scaling for GGUF. If the model has custom RoPE settings, they'll be used directly instead!\n");
            printf("It means that the RoPE values written above will be replaced by the RoPE values indicated after loading.\n");
        }
        else
        {
            //approximate NTK aware ctx
            auto effectivenctx = kcpp_data->n_ctx;
            if((file_format == FileFormat::GGUF_GENERIC) && file_format_meta.n_ctx_train > 2048)
            {
                float factor = file_format_meta.n_ctx_train/2048;
                effectivenctx = effectivenctx/factor;
            }
            float magic_multiplier = 8.0f;
            float base_multiplier = effectivenctx*magic_multiplier;
            float base_raw = 10000.0f;
            rope_freq_base = (effectivenctx <= 2048 ? base_raw : base_multiplier);
			//OLD : rope_freq_base = (effectivenctx <= 2048 ? 10000.0f : (effectivenctx <= 2176 ? 10000.0f : (effectivenctx <= 2304 ? 11000.0f : (effectivenctx <= 2432 ? 12000.0f : (effectivenctx <= 2560 ? 13000.0f : (effectivenctx <= 2688 ? 14000.0f : (effectivenctx <= 2816 ? 15000.0f : (effectivenctx <= 2944 ? 16000.0f : (effectivenctx <= 3072 ? 17000.0f : (effectivenctx <= 3200 ? 18000.0f : (effectivenctx <= 3328 ? 19000.0f : (effectivenctx <= 3456 ? 20000.0f : (effectivenctx <= 3584 ? 21000.0f : (effectivenctx <= 3712 ? 22000.0f : (effectivenctx <= 3840 ? 23000.0f : (effectivenctx <= 3968 ? 24000.0f : (effectivenctx <= 4096 ? 25000.0f : (effectivenctx <= 4224 ? 26000.0f : (effectivenctx <= 4352 ? 27000.0f : (effectivenctx <= 4480 ? 28500.0f : (effectivenctx <= 4608 ? 30000.0f : (effectivenctx <= 4736 ? 31500.0f : (effectivenctx <= 4864 ? 33000.0f : (effectivenctx <= 4992 ? 34500.0f : (effectivenctx <= 5120 ? 36000.0f : (effectivenctx <= 5248 ? 38000.0f : (effectivenctx <= 5376 ? 40000.0f : (effectivenctx <= 5504 ? 42000.0f : (effectivenctx <= 5632 ? 44000.0f : (effectivenctx <= 5760 ? 46000.0f : (effectivenctx <= 5888 ? 48000.0f : (effectivenctx <= 6016 ? 51000.0f : (effectivenctx <= 6144 ? 54000.0f : (effectivenctx <= 6288 ? 57000.0f : (effectivenctx <= 6400 ? 61000.0f : (effectivenctx <= 8192 ? 82684.0f : (effectivenctx <= 8192 ? 82684.0f : (effectivenctx <= 12288 ? 140000.0f : (effectivenctx <= 16384 ? 200000.0f : (effectivenctx <= 24576 ? 320000.0f : 440000.0f))))))))))))))))))))))))))))))))))))))));
            printf("Using Automatic RoPE scaling, Pre-GGUF (scale:%.3f, base:%.1f).\n",rope_freq_scale, rope_freq_base);
        }
    }
    gptj_ctx_v3.hparams.rope_freq_scale = neox_ctx_v3.hparams.rope_freq_scale = rope_freq_scale;
    gptj_ctx_v3.hparams.rope_freq_base = neox_ctx_v3.hparams.rope_freq_base = rope_freq_base;

    //this is used for the mem_per_token eval, blas needs more RAM
    bool v3_use_scratch = ggml_v3_cpu_has_gpublas();

    int cu_parseinfo_maindevice = inputs.cublas_info<=0?0:inputs.cublas_info;

    printf("System Info: %s\n", kcpp_print_system_info());
    #if defined(GGML_USE_CUDA)
    if(file_format!=FileFormat::GGUF_GENERIC)
    {
        if(ggml_v3_cpu_has_gpublas() && cu_parseinfo_maindevice>0)
        {
            printf("CUBLAS v3: Set main device to %d\n",cu_parseinfo_maindevice);
            ggml_v3_cuda_set_main_device(cu_parseinfo_maindevice);
        }
    }

    #endif
    SetQuantsUnshuffled(false);
    if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
    {
        //newer format has bit unshuffling
        SetQuantsUnshuffled(file_format == FileFormat::GGJT_2);
        llama_v2_context_params llama_ctx_params_v2 = llama_v2_context_default_params();
        llama_ctx_params_v2.n_ctx = clamped_max_context_length;
        llama_ctx_params_v2.seed = -1;
        llama_ctx_params_v2.f16_kv = true;
        llama_ctx_params_v2.logits_all = false;
        llama_ctx_params_v2.use_mmap = inputs.use_mmap;
        llama_ctx_params_v2.use_mlock = inputs.use_mlock;
        llama_ctx_params_v2.n_gpu_layers = inputs.gpulayers;

        llama_ctx_v2 = llama_v2_init_from_file(kcpp_data->model_filename.c_str(), llama_ctx_params_v2);

        if (llama_ctx_v2 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return ModelLoadResult::FAIL;
        }

        printf("\n---\nWarning: Your model may be an OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);

        if (lora_filename != "")
        {
            printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());

            const char * lora_base_arg = NULL;
            if (lora_base != "") {
                printf("Using LORA base model: %s\n", lora_base.c_str());
                lora_base_arg = lora_base.c_str();
            }

            int err = llama_v2_apply_lora_from_file(llama_ctx_v2,
                                                 lora_filename.c_str(),
                                                 lora_base_arg,
                                                 kcpp_data->n_threads);
            if (err != 0)
            {
                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
                return ModelLoadResult::FAIL;
            }
        }

        n_vocab = llama_v2_n_vocab(llama_ctx_v2);

        //determine mem per token
        const std::vector<int> tmp = {1, 2, 3, 4};
        llama_v2_eval(llama_ctx_v2, tmp.data(), tmp.size(), 0, kcpp_data->n_threads);
        return ModelLoadResult::SUCCESS;
    }
    else if(file_format == FileFormat::GGJT_3)
    {
        llama_v3_context_params llama_ctx_params = llama_v3_context_default_params();
        llama_ctx_params.n_ctx = clamped_max_context_length;
        llama_ctx_params.seed = -1;
        llama_ctx_params.f16_kv = true;
        llama_ctx_params.low_vram = inputs.low_vram;
        llama_ctx_params.mul_mat_q = inputs.use_mmq;

        if (inputs.use_mmq == true)
        {
            printf("\nUsing MMQ, less compute memory used\n");
        }
        else
        {
            printf("\nNot Using MMQ\n");
        }

        llama_ctx_params.logits_all = false;
        llama_ctx_params.use_mmap = inputs.use_mmap;
        llama_ctx_params.use_mlock = inputs.use_mlock;
        llama_ctx_params.n_gpu_layers = inputs.gpulayers;
        llama_ctx_params.main_gpu = cu_parseinfo_maindevice;
        llama_ctx_params.rope_freq_base = rope_freq_base;
        llama_ctx_params.rope_freq_scale = rope_freq_scale;
        llama_ctx_params.n_batch = kcpp_data->n_batch;
        llama_ctx_params.n_ubatch = kcpp_data->n_ubatch;

        #if defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN)
        bool ts_all_zero = true;
        for (int i = 0; i < tensor_split_max; ++i) {
            if (inputs.tensor_split[i] != 0.0f) {
                ts_all_zero = false;
                break;
            }
        }
        if(!ts_all_zero)
        {
            printf("\nApplying Tensor Split...\n");
            llama_ctx_params.tensor_split = inputs.tensor_split;
        }
        #endif

        llama_ctx_v3 = llama_v3_init_from_file(kcpp_data->model_filename.c_str(), llama_ctx_params);

        if (llama_ctx_v3 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return ModelLoadResult::FAIL;
        }
        if (lora_filename != "")
        {
            printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());

            const char * lora_base_arg = NULL;
            if (lora_base != "") {
                printf("Using LORA base model: %s\n", lora_base.c_str());
                lora_base_arg = lora_base.c_str();
            }

            int err = llama_v3_apply_lora_from_file(llama_ctx_v3,
                                                 lora_filename.c_str(),
                                                 lora_base_arg,
                                                 kcpp_data->n_threads);
            if (err != 0)
            {
                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
                return ModelLoadResult::FAIL;
            }
        }

        n_vocab = llama_v3_n_vocab(llama_ctx_v3);

        //determine mem per token
        const std::vector<int> tmp = {1, 2, 3, 4};
        auto er = llama_v3_eval(llama_ctx_v3, tmp.data(), tmp.size(), 0, kcpp_data->n_threads);
        if(er!=0)
        {
            printf("\nLLAMA EVAL returned nonzero!\n");
        }
        return ModelLoadResult::SUCCESS;
    }
    else if(file_format==FileFormat::GGUF_GENERIC)
    {
        llama_backend_init();

        llama_model_params model_params = llama_model_default_params();
        llama_context_params llama_ctx_params = llama_context_default_params();
        llama_ctx_params.n_ctx = clamped_max_context_length;
        if(kcpp_data->use_contextshift)
        {
           llama_ctx_params.n_ctx += extra_context_handle_fragmentation;
        }

        llama_ctx_params.offload_kqv = !inputs.low_vram;
        llama_ctx_params.logits_all = false;
        model_params.use_mmap = inputs.use_mmap;
        model_params.use_mlock = inputs.use_mlock;
        model_params.n_gpu_layers = inputs.gpulayers;

        #if defined(GGML_USE_CLBLAST)
        if(file_format==FileFormat::GGUF_GENERIC && model_params.n_gpu_layers>0)
        {
            if(file_format_meta.model_architecture == GGUFArch::ARCH_FALCON)
            {
                printf("\nOpenCL does not support GPU Layer offloading for this model architecture! GPU Offload has been disabled.\n");
                model_params.n_gpu_layers = 0;
            }
            else if(file_format_meta.n_expert_count>1)
            {
                printf("\nOpenCL cannot use regular GPU offloading for this model architecture. A fallback GPU offloader will be used with degraded performance.\n");

            }
        }
        #endif
        #if defined(GGML_USE_CUDA)
        if(cu_parseinfo_maindevice>0)
        {
            printf("CUBLAS: Set main device to %d\n",cu_parseinfo_maindevice);
        }
        ggml_cuda_set_mul_mat_q(inputs.use_mmq);

        if (inputs.use_mmq == true)
        {
            printf("\nUsing MMQ, less compute memory used\n");
        }
        else
        {
            printf("\nNot Using MMQ\n");
        }

        #endif
        if((file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2 || file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL) && !kcpp_data->flash_attn)
        {
            printf("Warning, you are running Qwen2 without Flash Attention. If you observe incoherent output, try enabling it.\n");
        }
        if(file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL)
        {
            printf("Qwen2VL detected! Mrope will be used!\n");
        }
        model_params.main_gpu = cu_parseinfo_maindevice;

        #if defined(GGML_USE_CUDA)
        model_params.split_mode = (inputs.use_rowsplit?llama_split_mode::LLAMA_SPLIT_MODE_ROW:llama_split_mode::LLAMA_SPLIT_MODE_LAYER);
        #else
        model_params.split_mode = llama_split_mode::LLAMA_SPLIT_MODE_LAYER;
        #endif

        llama_ctx_params.n_batch = kcpp_data->n_batch;
        llama_ctx_params.n_ubatch = kcpp_data->n_ubatch;
        llama_ctx_params.n_threads = kcpp_data->n_threads;
        llama_ctx_params.n_threads_batch = kcpp_data->n_blasthreads;

        #if defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN)
        bool ts_all_zero = true;
        for (int i = 0; i < tensor_split_max; ++i) {
            if (inputs.tensor_split[i] != 0.0f) {
                ts_all_zero = false;
                break;
            }
        }
        if(!ts_all_zero)
        {
            printf("\nApplying Tensor Split...\n");
            model_params.tensor_split = inputs.tensor_split;
        }
        #endif

        //compat for old falcon
        if(file_format_meta.fileversion==1)
        {
            //apply compat fix
            printf("\nUsing older tokenizer for GGUFv1...");
            OldBPETokenizerMode = true;
        }

        std::vector<llama_model_kv_override> kvos; //ensure it keeps in scope until model is created
        if(inputs.moe_experts>0)
        {
            printf("\nOverriding number of experts to %d\n",inputs.moe_experts);
            llama_model_kv_override kvo;
            const char * moekey = "llama.expert_used_count";
            std::strncpy(kvo.key, moekey, sizeof(kvo.key) - 1);
            kvo.key[sizeof(kvo.key) - 1] = '\0'; // Ensure null termination
            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
            kvo.val_i64 = inputs.moe_experts;
            kvos.push_back(kvo);
            model_params.kv_overrides = kvos.data();
        }

        if(inputs.norm_rms_eps>0)
        {
            printf("\nOverriding norm rms epsilon to %f\n",inputs.norm_rms_eps);
            llama_model_kv_override kvo;
            const char * rmskey = "llama.attention.layer_norm_rms_epsilon";
            std::strncpy(kvo.key, rmskey, sizeof(kvo.key) - 1);
            kvo.key[sizeof(kvo.key) - 1] = '\0'; // Ensure null termination
            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
            kvo.val_f64 = inputs.norm_rms_eps;
            kvos.push_back(kvo);
            model_params.kv_overrides = kvos.data();
        }
        llama_model * llamamodel = llama_load_model_from_file(kcpp_data->model_filename.c_str(), model_params);

        if(overwriteRope)
        {
            llama_ctx_params.rope_freq_base = rope_freq_base;
            llama_ctx_params.rope_freq_scale = rope_freq_scale;
        }
        else
        {
            //if the model modifes rope in any way, or uses yarn, use the model values. Otherwise, use our automatic ones
            //special exception for llama, which uses auto scale
            if((llamamodel->hparams.rope_freq_base_train!=10000.0f && llamamodel->hparams.rope_freq_base_train!=500000.0f) ||
            llamamodel->hparams.rope_freq_scale_train!=1.0f ||
            llamamodel->hparams.rope_scaling_type_train==2)
            {
                printf("Automatic RoPE Scaling: Using model internal value.\n");
            }
            else
            {
				//Calculate rope_freq_base using the gradientAI formula, solar requires ctx *8 for correct scaling
                rope_freq_base = CalcGradientAIRopeFreqBase(llamamodel->hparams.rope_freq_base_train, file_format_meta.n_ctx_train, kcpp_data->n_ctx, file_format_meta.model_architecture);
                llama_ctx_params.rope_freq_base = rope_freq_base;
                llama_ctx_params.rope_freq_scale = rope_freq_scale;
                printf("Automatic RoPE Scaling: Using (scale:%.3f, base:%.1f).\n", rope_freq_scale, rope_freq_base);
            }
        }

        if(file_format_meta.model_architecture==GGUFArch::ARCH_RWKV)
        {
            printf("\nRWKV6 Overriding EOS and BOS IDs to 0\n");
            llamamodel->vocab.set_eos_bos(0,0);
        }

        llama_ctx_params.flash_attn = kcpp_data->flash_attn;
        llama_ctx_params.type_k =
		(inputs.quant_k==22?GGML_TYPE_IQ4_NL:
		(inputs.quant_k==21?GGML_TYPE_Q4_0:
		(inputs.quant_k==20?GGML_TYPE_Q4_1:
		(inputs.quant_k==19?GGML_TYPE_Q5_0:
		(inputs.quant_k==18?GGML_TYPE_Q5_1:
		(inputs.quant_k==17?GGML_TYPE_Q6_0:
		(inputs.quant_k==16?GGML_TYPE_Q8_0:
		(inputs.quant_k==15?GGML_TYPE_BF16:
		(inputs.quant_k==14?GGML_TYPE_IQ4_NL:
		(inputs.quant_k==13?GGML_TYPE_Q5_0:
		(inputs.quant_k==12?GGML_TYPE_Q5_1:
		(inputs.quant_k==11?GGML_TYPE_Q5_1:
		(inputs.quant_k==10?GGML_TYPE_Q6_0:
		(inputs.quant_k==9?GGML_TYPE_Q6_0:
		(inputs.quant_k==8?GGML_TYPE_Q6_0:
		(inputs.quant_k==7?GGML_TYPE_Q8_0:
		(inputs.quant_k==6?GGML_TYPE_Q8_0:
		(inputs.quant_k==5?GGML_TYPE_Q8_0:
		(inputs.quant_k==4?GGML_TYPE_F16:
		(inputs.quant_k==3?GGML_TYPE_F16:
		(inputs.quant_k==2?GGML_TYPE_Q4_0:
		(inputs.quant_k==1?GGML_TYPE_Q8_0:
		GGML_TYPE_F16))))))))))))))))))))));
        llama_ctx_params.type_v =
		(inputs.quant_v==22?GGML_TYPE_F16:
		(inputs.quant_v==21?GGML_TYPE_F16:
		(inputs.quant_v==20?GGML_TYPE_F16:
		(inputs.quant_v==19?GGML_TYPE_F16:
		(inputs.quant_v==18?GGML_TYPE_F16:
		(inputs.quant_v==17?GGML_TYPE_F16:
		(inputs.quant_v==16?GGML_TYPE_F16:
		(inputs.quant_v==15?GGML_TYPE_BF16:
		(inputs.quant_v==14?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==13?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==12?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==11?GGML_TYPE_Q5_0:
		(inputs.quant_v==10?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==9?GGML_TYPE_Q5_0:
		(inputs.quant_v==8?GGML_TYPE_Q6_0:
		(inputs.quant_v==7?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==6?GGML_TYPE_Q5_0:
		(inputs.quant_v==5?GGML_TYPE_Q6_0:
		(inputs.quant_v==4?GGML_TYPE_Q6_0:
		(inputs.quant_v==3?GGML_TYPE_Q8_0:
		(inputs.quant_v==2?GGML_TYPE_Q4_0:
		(inputs.quant_v==1?GGML_TYPE_Q8_0:
		GGML_TYPE_F16))))))))))))))))))))));
        llama_ctx_v4 = llama_new_context_with_model(llamamodel, llama_ctx_params);

        if (llama_ctx_v4 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return ModelLoadResult::FAIL;
        }
        if (lora_filename != "")
        {
            printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());

            const char * lora_base_arg = NULL;
            if (lora_base != "") {
                printf("Using LORA base model: %s\n", lora_base.c_str());
                lora_base_arg = lora_base.c_str();
            }

            auto adapter = llama_adapter_lora_init(llamamodel, lora_filename.c_str());
            if (adapter == nullptr) {
                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
                return ModelLoadResult::FAIL;
            }
            llama_set_adapter_lora(llama_ctx_v4, adapter, 1.0f);
        }

        if(mmproj_filename != "" && file_format==FileFormat::GGUF_GENERIC)
        {
            printf("\nAttempting to apply Multimodal Projector: %s\n", mmproj_filename.c_str());
            #if defined(GGML_USE_VULKAN) || defined(GGML_USE_METAL)
            if(file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL)
            {
                set_clip_uses_gpu(false);
                printf("Clip will use CPU for this model!\n");
            }
            #endif
            clp_ctx = clip_model_load(mmproj_filename.c_str(), /*verbosity=*/ 1);
            if(clp_ctx == nullptr) {
                fprintf(stderr, "%s: error: failed to load mmproj model!\n", __func__);
                return ModelLoadResult::FAIL;
            }
            const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
            const int n_embd_llm  = llama_n_embd(llamamodel);
            if (n_embd_clip != n_embd_llm) {
                fprintf(stderr, "%s: mmproj embedding mismatch (%d and %d)! Make sure you use the correct mmproj file!\n", __func__,n_embd_clip, n_embd_llm);
                return ModelLoadResult::FAIL;
            }
            clp_img_data = clip_image_u8_init();
        }

        const llama_vocab * tmpvocab = llama_model_get_vocab(llamamodel);
        n_vocab = llama_vocab_n_tokens(tmpvocab);

        if(draftmodel_filename !="" && file_format==FileFormat::GGUF_GENERIC)
        {
            if(llama_model_is_recurrent(llamamodel))
            {
                printf("Error: Speculative decoding cannot be used with Recurrent models!\n");
            }
            else if(clp_ctx!=nullptr)
            {
                printf("Error: Speculative decoding cannot be used with multimodal vision projectors!\n");
            }
            else
            {
                printf("\nAttempting to load draft model for speculative decoding. It will be fully offloaded if possible. Vocab must match the main model.\n");
                speculative_chunk_amt = inputs.draft_amount;
                speculative_decoding_setup(draftmodel_filename, model_params, llama_ctx_params, n_vocab, inputs.draft_gpusplit, inputs.draft_gpulayers);
            }
        }

        //determine mem per token
        std::vector<int> tmp = {1, 2, 3, 4};
        llama_kv_cache_clear(llama_ctx_v4);
        auto er = llama_decode(llama_ctx_v4, llama_batch_get_one(tmp.data(), tmp.size()));
        if(er!=0)
        {
            printf("\nLLAMA EVAL returned nonzero: %d\n",er);
        }
        return ModelLoadResult::SUCCESS;
    }
    else if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
    {
        //start loading the models first
        bool useWorldTokenizer = false;
        if (file_format == FileFormat::RWKV_1)
        {
            rwkv_ctx_v2 = rwkv_v2_init_from_file(kcpp_data->model_filename.c_str(), kcpp_data->n_threads);
        }
        else //rwkv_2
        {
            rwkv_ctx_v3 = rwkv_init_from_file(kcpp_data->model_filename.c_str(), kcpp_data->n_threads);

            if(inputs.gpulayers>0)
            {
                rwkv_gpu_offload_layers(rwkv_ctx_v3,inputs.gpulayers);
            }

            const struct rwkv_file_header & header = rwkv_ctx_v3->instance->model.header;
            const size_t n_vocab = header.n_vocab;
            printf("\nDetected Vocab: %zu",n_vocab);
            if(n_vocab>60000)
            {
                printf("\nUsing WORLD TOKENIZER");
                useWorldTokenizer = true;
            }
        }

        std::string word;
        if(useWorldTokenizer)
        {
            read_rwkv_world_vocab();
        }
        else
        {
            read_rwkv_vocab();
        }

        int vocabsiz = rwkv_vocab.size();
        for (int i = 0; i < vocabsiz; i++)
        {
            uint32_t len;
            word = rwkv_vocab[i];
            vocab.token_to_id[word] = i;
            vocab.id_to_token[i] = word;
        }
        printf("\nRWKV Vocab: %u\n", vocabsiz);
        logits.resize(vocabsiz);

        n_vocab = vocab.id_to_token.size(); //handled seperately

        if (file_format == FileFormat::RWKV_1)
        {

            //setup buffers for rwkv state
            auto padding = 512u;
            auto statebufsiz = rwkv_v2_get_state_buffer_element_count(rwkv_ctx_v2) * sizeof(float) + padding;
            auto logitbufsiz = rwkv_v2_get_logits_buffer_element_count(rwkv_ctx_v2) * sizeof(float) + padding;

            printf("\nRWKV old Init: State Buffer:%lu, Logit Buffer:%lu\n", statebufsiz, logitbufsiz);
            rwkv_ctx_v2->state_out = (float *)malloc(statebufsiz);
            rwkv_ctx_v2->logits_out = (float *)malloc(logitbufsiz);
            rwkv_ctx_v2->state_in = nullptr;

            bool testeval = rwkv_v2_eval(rwkv_ctx_v2, 0, rwkv_ctx_v2->state_in, rwkv_ctx_v2->state_out, rwkv_ctx_v2->logits_out);
            if (!testeval)
            {
                printf("\nError: RWKV old Init Eval Failed!\n");
            }

            memcpy(logits.data(), rwkv_ctx_v2->logits_out, sizeof(float) * vocabsiz);

            if (rwkv_ctx_v2 == NULL)
            {
                return ModelLoadResult::FAIL;
            }
            return ModelLoadResult::SUCCESS;
        }
        else
        {
            //setup buffers for rwkv state
            auto padding = 512u;
            auto statebufsiz = rwkv_get_state_buffer_element_count(rwkv_ctx_v3) * sizeof(float) + padding;
            auto logitbufsiz = rwkv_get_logits_buffer_element_count(rwkv_ctx_v3) * sizeof(float) + padding;

            printf("\nRWKV Init: State Buffer:%lu, Logit Buffer:%lu\n", statebufsiz, logitbufsiz);
            rwkv_ctx_v3->state_out = (float *)malloc(statebufsiz);
            rwkv_ctx_v3->logits_out = (float *)malloc(logitbufsiz);
            rwkv_ctx_v3->state_in = nullptr;

            bool testeval = rwkv_eval(rwkv_ctx_v3, kcpp_data->n_threads, 0, rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
            if (!testeval)
            {
                printf("\nError: RWKV Init Eval Failed!\n");
            }

            memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * vocabsiz);

            if (rwkv_ctx_v3 == NULL)
            {
                return ModelLoadResult::FAIL;
            }
            return ModelLoadResult::SUCCESS;
        }
    }
    else if (file_format == FileFormat::GPT2_1)
    {
        ModelLoadResult res = legacy_gpt2_model_load(kcpp_data->model_filename, gpt2_ctx_v1, vocab, file_format);
        if(res==ModelLoadResult::FAIL)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return res;
        }
        else if(res==ModelLoadResult::RETRY_LOAD)
        {
            printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
            return res;
        }

        n_vocab = gpt2_ctx_v1.hparams.n_vocab;

         // determine the required inference memory per token:
        legacy_gpt2_eval(gpt2_ctx_v1, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
        return ModelLoadResult::SUCCESS;
    }
    else if (file_format == FileFormat::GPT2_2 || file_format==FileFormat::GPT2_3 || file_format==FileFormat::GPT2_4)
    {
        if(file_format==FileFormat::GPT2_4)
        {
            ModelLoadResult res = gpt2_model_load(kcpp_data->model_filename, gpt2_ctx_v3, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return res;
            }
            else if(res==ModelLoadResult::RETRY_LOAD)
            {
                printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
                return res;
            }

            n_vocab = gpt2_ctx_v3.hparams.n_vocab;

            // determine the required inference memory per token:
            gpt2_eval(gpt2_ctx_v3, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, v3_use_scratch);
            return ModelLoadResult::SUCCESS;
        }
        else
        {
            //newer format has bit unshuffling
            SetQuantsUnshuffled(file_format == FileFormat::GPT2_3);

            ModelLoadResult res = gpt2_v2_model_load(kcpp_data->model_filename, gpt2_ctx_v2, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return res;
            }
            else if(res==ModelLoadResult::RETRY_LOAD)
            {
                printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
                return res;
            }

            n_vocab = gpt2_ctx_v2.hparams.n_vocab;

            // determine the required inference memory per token:
            gpt2_v2_eval(gpt2_ctx_v2, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
            return ModelLoadResult::SUCCESS;
        }
    }
    else if (file_format == FileFormat::GPTJ_1 || file_format == FileFormat::GPTJ_2)
    {
        ModelLoadResult res = legacy_gptj_model_load(kcpp_data->model_filename, gptj_ctx_v1, vocab, file_format);
        if(res==ModelLoadResult::FAIL)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return res;
        }
        else if(res==ModelLoadResult::RETRY_LOAD)
        {
            printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
            return res;
        }

        n_vocab = gptj_ctx_v1.hparams.n_vocab;

         // determine the required inference memory per token:
        legacy_gptj_eval(gptj_ctx_v1, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);

        //if the logits are NAN or duplicated, it means the model is incompatible
        if(logits.size()>0 && IsNanCheck(logits[0]))
        {
            printf("\nBad Logits detected! Retrying GPT-J model loading...");
            ggml_v1_free(gptj_ctx_v1.ctx);
            return ModelLoadResult::RETRY_LOAD;
        }

        return ModelLoadResult::SUCCESS;
    }
    else if(file_format == FileFormat::GPTJ_3 || file_format == FileFormat::GPTJ_4 || file_format == FileFormat::GPTJ_5)
    {
        if(file_format == FileFormat::GPTJ_5)
        {
            ModelLoadResult loadresult = gptj_model_load(kcpp_data->model_filename, gptj_ctx_v3, vocab, inputs.gpulayers);
            if (loadresult == ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return loadresult;
            }
            else if (loadresult == ModelLoadResult::RETRY_LOAD)
            {
                printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
                return loadresult;
            }

            n_vocab = gptj_ctx_v3.hparams.n_vocab;

            // determine the required inference memory per token:
            gptj_eval(gptj_ctx_v3, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, v3_use_scratch);

            //if the logits are NAN or duplicated, it means the model is incompatible
            std::vector<float> oldlogits(logits);

            //this is another hack because they change the library - we run the eval through the model
            //twice and compare logits. if they give the same logits for different inputs, model is broken
            gptj_eval(gptj_ctx_v3, kcpp_data->n_threads, 0, {4, 5, 6, 7}, logits, mem_per_token, v3_use_scratch);

            if(logits.size()>0 && (IsNanCheck(logits[0]) || LogitsDuplicated(oldlogits,logits)))
            {
                printf("\nBad Logits detected! Retrying GPT-J model loading...");
                ggml_v3_free(gptj_ctx_v3.ctx);
                return ModelLoadResult::RETRY_LOAD;
            }

            return ModelLoadResult::SUCCESS;
        }
        else
        {
            //newer format has bit unshuffling
            SetQuantsUnshuffled(file_format == FileFormat::GPTJ_4);

            ModelLoadResult loadresult = gptj_v2_model_load(kcpp_data->model_filename, gptj_ctx_v2, vocab, inputs.gpulayers);
            if (loadresult == ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return loadresult;
            }
            else if (loadresult == ModelLoadResult::RETRY_LOAD)
            {
                printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
                return loadresult;
            }

            n_vocab = gptj_ctx_v2.hparams.n_vocab;

            // determine the required inference memory per token:
            gptj_v2_eval(gptj_ctx_v2, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);

            //if the logits are NAN or duplicated, it means the model is incompatible
            std::vector<float> oldlogits(logits);

            //this is another hack because they change the library - we run the eval through the model
            //twice and compare logits. if they give the same logits for different inputs, model is broken
            gptj_v2_eval(gptj_ctx_v2, kcpp_data->n_threads, 0, {4, 5, 6, 7}, logits, mem_per_token);

            if(logits.size()>0 && (IsNanCheck(logits[0]) || LogitsDuplicated(oldlogits,logits)))
            {
                printf("\nBad Logits detected! Retrying GPT-J model loading...");
                ggml_v2_free(gptj_ctx_v2.ctx);
                return ModelLoadResult::RETRY_LOAD;
            }

            return ModelLoadResult::SUCCESS;
        }
    }
    else if(file_format==FileFormat::NEOX_1 || file_format==FileFormat::NEOX_2 || file_format==FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5|| file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
    {
        if(file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
        {
            ModelLoadResult res = gpt_neox_model_load(kcpp_data->model_filename, neox_ctx_v3, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return res;
            }
            else if(res==ModelLoadResult::RETRY_LOAD)
            {
                printf("\nIncorrect Tensor Size Detected! Retrying GPT-NeoX model loading...");
                return res;
            }

            n_vocab = neox_ctx_v3.hparams.n_vocab;

            // determine the required inference memory per token:
            gpt_neox_eval(neox_ctx_v3, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, v3_use_scratch);

            return ModelLoadResult::SUCCESS;
        }
        else
        {
            //newer format has bit unshuffling
            SetQuantsUnshuffled(file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5);

            ModelLoadResult res = gpt_neox_v2_model_load(kcpp_data->model_filename, neox_ctx_v2, vocab, file_format);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
                return res;
            }
            else if(res==ModelLoadResult::RETRY_LOAD)
            {
                printf("\nIncorrect Tensor Size Detected! Retrying GPT-NeoX model loading...");
                return res;
            }

            n_vocab = neox_ctx_v2.hparams.n_vocab;

            // determine the required inference memory per token:
            gpt_neox_v2_eval(neox_ctx_v2, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);

            if(logits.size()>0 && file_format==FileFormat::NEOX_2 && !IsNanCheck(logits[0]))
            {
                //run the black magic eval to determine if it's redpajama. VERY UGLY HACK!
                std::vector<int> test_embd = ::gpt_tokenize(vocab, "1 2 3 4 5 6 7");
                auto orig_par_res = neox_ctx_v2.hparams.par_res;
                neox_ctx_v2.hparams.par_res = 0; //test with residual false
                gpt_neox_v2_eval(neox_ctx_v2, kcpp_data->n_threads, 0, test_embd, logits, mem_per_token);
                neox_ctx_v2.hparams.par_res = orig_par_res;
                int topid = std::max_element(logits.begin(),logits.end())-logits.begin();
                std::string predicted = vocab.id_to_token[topid].c_str();
                auto findresult = predicted.find("8");
                if(findresult != std::string::npos && findresult<2)
                {
                    printf("\n---\nOld RedPajama NeoX Detected! Switching to new format! (use_parallel_residual=False)\n");
                    ggml_v2_free(neox_ctx_v2.ctx);
                    return ModelLoadResult::RETRY_LOAD;
                }
            }

            return ModelLoadResult::SUCCESS;
        }

    }
    else if(file_format==FileFormat::MPT_1)
    {
        bool res = mpt_model_load(kcpp_data->model_filename, mpt_ctx_v3, vocab, inputs.gpulayers);
        if(res==false)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return ModelLoadResult::FAIL;
        }

        n_vocab = mpt_ctx_v3.hparams.n_vocab;

        // determine the required inference memory per token:
        mpt_eval(mpt_ctx_v3, kcpp_data->n_threads, 0, { 0, 1, 2, 3 }, logits, false, mem_per_token, v3_use_scratch);
        return ModelLoadResult::SUCCESS;
    }
    else
    {
        // printf("\nUnknown Model, cannot load.\n");
        // return ModelLoadResult::FAIL;
        llama_backend_init();

        llama_model_params model_params = llama_model_default_params();
        llama_context_params llama_ctx_params = llama_context_default_params();
        llama_ctx_params.n_ctx = clamped_max_context_length;
        if(kcpp_data->use_contextshift)
        {
           llama_ctx_params.n_ctx += extra_context_handle_fragmentation;
        }

        llama_ctx_params.offload_kqv = !inputs.low_vram;
        llama_ctx_params.logits_all = false;
        model_params.use_mmap = inputs.use_mmap;
        model_params.use_mlock = inputs.use_mlock;
        model_params.n_gpu_layers = inputs.gpulayers;

        #if defined(GGML_USE_CLBLAST)
        if(file_format==FileFormat::GGUF_GENERIC && model_params.n_gpu_layers>0)
        {
            if(file_format_meta.model_architecture == GGUFArch::ARCH_FALCON)
            {
                printf("\nOpenCL does not support GPU Layer offloading for this model architecture! GPU Offload has been disabled.\n");
                model_params.n_gpu_layers = 0;
            }
            else if(file_format_meta.n_expert_count>1)
            {
                printf("\nOpenCL cannot use regular GPU offloading for this model architecture. A fallback GPU offloader will be used with degraded performance.\n");

            }
        }
        #endif
        #if defined(GGML_USE_CUDA)
        if(cu_parseinfo_maindevice>0)
        {
            printf("CUBLAS: Set main device to %d\n",cu_parseinfo_maindevice);
        }
        ggml_cuda_set_mul_mat_q(inputs.use_mmq);

        if (inputs.use_mmq == true)
        {
            printf("\nUsing MMQ, less compute memory used\n");
        }
        else
        {
            printf("\nNot Using MMQ\n");
        }

        if(file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2 && !kcpp_data->flash_attn)
        {
            printf("CUBLAS: Warning, you are running Qwen2 without Flash Attention and may observe incoherent output.\n");
        }
        #endif
        model_params.main_gpu = cu_parseinfo_maindevice;

        #if defined(GGML_USE_CUDA)
        model_params.split_mode = (inputs.use_rowsplit?llama_split_mode::LLAMA_SPLIT_MODE_ROW:llama_split_mode::LLAMA_SPLIT_MODE_LAYER);
        #else
        model_params.split_mode = llama_split_mode::LLAMA_SPLIT_MODE_LAYER;
        #endif

        llama_ctx_params.n_batch = kcpp_data->n_batch;
        llama_ctx_params.n_ubatch = kcpp_data->n_ubatch;
        llama_ctx_params.n_threads = kcpp_data->n_threads;
        llama_ctx_params.n_threads_batch = kcpp_data->n_blasthreads;

        #if defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN)
        bool ts_all_zero = true;
        for (int i = 0; i < tensor_split_max; ++i) {
            if (inputs.tensor_split[i] != 0.0f) {
                ts_all_zero = false;
                break;
            }
        }
        if(!ts_all_zero)
        {
            printf("\nApplying Tensor Split...");
            model_params.tensor_split = inputs.tensor_split;
        }
        #endif

        //compat for old falcon
        if(file_format_meta.fileversion==1)
        {
            //apply compat fix
            printf("\nUsing older tokenizer for GGUFv1...");
            OldBPETokenizerMode = true;
        }

        llama_model * llamamodel = llama_load_model_from_file(kcpp_data->model_filename.c_str(), model_params);
        if(overwriteRope)
        {
            llama_ctx_params.rope_freq_base = rope_freq_base;
            llama_ctx_params.rope_freq_scale = rope_freq_scale;
        }
        else
        {
            //if the model modifes rope in any way, or uses yarn, use the model values. Otherwise, use our automatic ones
            //special exception for llama, which uses auto scale
            if((llamamodel->hparams.rope_freq_base_train!=10000.0f && llamamodel->hparams.rope_freq_base_train!=500000.0f) ||
            llamamodel->hparams.rope_freq_scale_train!=1.0f ||
            llamamodel->hparams.rope_scaling_type_train==2)
            {
                printf("Automatic RoPE Scaling: Using model internal value.\n");
            }
            else
            {
				//Calculate rope_freq_base using the gradientAI formula, solar requires ctx *8 for correct scaling
                rope_freq_base = CalcGradientAIRopeFreqBase(llamamodel->hparams.rope_freq_base_train, file_format_meta.n_ctx_train, kcpp_data->n_ctx, file_format_meta.model_architecture);
                llama_ctx_params.rope_freq_base = rope_freq_base;
                llama_ctx_params.rope_freq_scale = rope_freq_scale;
                printf("Automatic RoPE Scaling: Using (scale:%.3f, base:%.1f).\n", rope_freq_scale, rope_freq_base);
            }
        }

        if(file_format_meta.model_architecture==GGUFArch::ARCH_RWKV)
        {
            printf("\nRWKV6 Overriding EOS and BOS IDs to 0\n");
            llamamodel->vocab.set_eos_bos(0, 0);
        }

        llama_ctx_params.flash_attn = kcpp_data->flash_attn;
        llama_ctx_params.type_k =
		(inputs.quant_k==22?GGML_TYPE_IQ4_NL:
		(inputs.quant_k==21?GGML_TYPE_Q4_0:
		(inputs.quant_k==20?GGML_TYPE_Q4_1:
		(inputs.quant_k==19?GGML_TYPE_Q5_0:
		(inputs.quant_k==18?GGML_TYPE_Q5_1:
		(inputs.quant_k==17?GGML_TYPE_Q6_0:
		(inputs.quant_k==16?GGML_TYPE_Q8_0:
		(inputs.quant_k==15?GGML_TYPE_BF16:
		(inputs.quant_k==14?GGML_TYPE_IQ4_NL:
		(inputs.quant_k==13?GGML_TYPE_Q5_0:
		(inputs.quant_k==12?GGML_TYPE_Q5_1:
		(inputs.quant_k==11?GGML_TYPE_Q5_1:
		(inputs.quant_k==10?GGML_TYPE_Q6_0:
		(inputs.quant_k==9?GGML_TYPE_Q6_0:
		(inputs.quant_k==8?GGML_TYPE_Q6_0:
		(inputs.quant_k==7?GGML_TYPE_Q8_0:
		(inputs.quant_k==6?GGML_TYPE_Q8_0:
		(inputs.quant_k==5?GGML_TYPE_Q8_0:
		(inputs.quant_k==4?GGML_TYPE_F16:
		(inputs.quant_k==3?GGML_TYPE_F16:
		(inputs.quant_k==2?GGML_TYPE_Q4_0:
		(inputs.quant_k==1?GGML_TYPE_Q8_0:
		GGML_TYPE_F16))))))))))))))))))))));
        llama_ctx_params.type_v =
		(inputs.quant_v==22?GGML_TYPE_F16:
		(inputs.quant_v==21?GGML_TYPE_F16:
		(inputs.quant_v==20?GGML_TYPE_F16:
		(inputs.quant_v==19?GGML_TYPE_F16:
		(inputs.quant_v==18?GGML_TYPE_F16:
		(inputs.quant_v==17?GGML_TYPE_F16:
		(inputs.quant_v==16?GGML_TYPE_F16:
		(inputs.quant_v==15?GGML_TYPE_BF16:
		(inputs.quant_v==14?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==13?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==12?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==11?GGML_TYPE_Q5_0:
		(inputs.quant_v==10?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==9?GGML_TYPE_Q5_0:
		(inputs.quant_v==8?GGML_TYPE_Q6_0:
		(inputs.quant_v==7?GGML_TYPE_IQ4_NL:
		(inputs.quant_v==6?GGML_TYPE_Q5_0:
		(inputs.quant_v==5?GGML_TYPE_Q6_0:
		(inputs.quant_v==4?GGML_TYPE_Q6_0:
		(inputs.quant_v==3?GGML_TYPE_Q8_0:
		(inputs.quant_v==2?GGML_TYPE_Q4_0:
		(inputs.quant_v==1?GGML_TYPE_Q8_0:
		GGML_TYPE_F16))))))))))))))))))))));
        llama_ctx_v4 = llama_new_context_with_model(llamamodel, llama_ctx_params);

        if (llama_ctx_v4 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, kcpp_data->model_filename.c_str());
            return ModelLoadResult::FAIL;
        }
        if (lora_filename != "")
        {
            printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());

            const char * lora_base_arg = NULL;
            if (lora_base != "") {
                printf("Using LORA base model: %s\n", lora_base.c_str());
                lora_base_arg = lora_base.c_str();
            }

            auto adapter = llama_adapter_lora_init(llamamodel, lora_filename.c_str());
            if (adapter == nullptr) {
                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
                return ModelLoadResult::FAIL;
            }
            llama_set_adapter_lora(llama_ctx_v4, adapter, 1.0f);
        }

        if(mmproj_filename != "" && file_format==FileFormat::GGUF_GENERIC)
        {
            printf("\nAttempting to apply Multimodal Projector: %s\n", mmproj_filename.c_str());
            clp_ctx = clip_model_load(mmproj_filename.c_str(), /*verbosity=*/ 1);
            if(clp_ctx == nullptr) {
                fprintf(stderr, "%s: error: failed to load mmproj model!\n", __func__);
                return ModelLoadResult::FAIL;
            }
            const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
            const int n_embd_llm  = llama_n_embd(llamamodel);
            if (n_embd_clip != n_embd_llm) {
                fprintf(stderr, "%s: mmproj embedding mismatch (%d and %d)! Make sure you use the correct mmproj file!\n", __func__,n_embd_clip, n_embd_llm);
                return ModelLoadResult::FAIL;
            }
            clp_img_data = clip_image_u8_init();
        }

        const llama_vocab * tmpvocab = llama_model_get_vocab(llamamodel);
        n_vocab                      = llama_vocab_n_tokens(tmpvocab);

        //determine mem per token
        std::vector<int> tmp = {1, 2, 3, 4};
        llama_kv_cache_clear(llama_ctx_v4);
        auto er = llama_decode(llama_ctx_v4, llama_batch_get_one(tmp.data(), tmp.size()));
        if(er!=0)
        {
            printf("\nLLAMA EVAL returned nonzero: %d\n",er);
        }
        return ModelLoadResult::SUCCESS;
    }

}

bool gpttype_generate_abort()
{
    if(kcpp_data==nullptr)
    {
        printf("\nWarning: KCPP text generation not initialized!\n");
    }
    early_abort = true;
    return true;
}

std::string gpttype_get_chat_template()
{
    // copied from examples/server/utils.hpp::llama_get_chat_template
    std::string template_key = "tokenizer.chat_template";
    // call with NULL buffer to get the total size of the string
    int32_t res = llama_model_meta_val_str(&llama_ctx_v4->model, template_key.c_str(), NULL, 0);
    if (res < 0) {
        return "";
    }

    std::vector<char> model_template(res + 1, 0);
    llama_model_meta_val_str(&llama_ctx_v4->model, template_key.c_str(), model_template.data(), model_template.size());
    return std::string(model_template.data(), model_template.size() - 1);
}

std::vector<int> gpttype_get_token_arr(const std::string & input, bool addbos)
{
    std::vector<int> toks;
    if(kcpp_data==nullptr)
    {
        printf("\nWarning: KCPP text generation not initialized!\n");
        return toks;
    }
    if(debugmode==1)
    {
        printf("\nFileFormat: %d, Tokenizing: %s",file_format ,input.c_str());
    }
    TokenizeString(input, toks, file_format,addbos);
    int tokcount = toks.size();
    if(debugmode==1)
    {
        printf("\nTokens Counted: %d\n",tokcount);
    }
    return toks;
}

std::string gpttype_detokenize(const std::vector<int> & inputids, bool render_special)
{
    std::string output = "";
    for (auto eid : inputids)
    {
        if(eid<0 || eid>=n_vocab)
        {
            continue;
        }
        std::string tokenizedstr = FileFormatTokenizeID(eid, file_format, render_special);
        output += tokenizedstr;
    }
    return output;
}

const std::string & gpttype_get_pending_output()
{
    if(kcpp_data==nullptr)
    {
        printf("\nWarning: KCPP text generation not initialized!\n");
        return concat_output_reader_copy_poll;
    }
    concat_output_mtx.lock();
    concat_output_reader_copy_poll = concat_output;
    concat_output_mtx.unlock();
    return concat_output_reader_copy_poll;
}

const std::vector<TopPicksData> gpttype_get_top_picks_data()
{
    return top_picks_history;
}

bool VecContainsIntVal(const std::vector<int> & vec, const int val)
{
    for (const auto &matched : vec)
    {
        if (val == matched)
        {
            return true;
        }
    }
    return false;
}

int GetThreadsToUse(bool blasmode)
{
    if (blasmode)
    {
        #if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN)
            return kcpp_data->n_blasthreads;
        #else
            return std::min(kcpp_data->n_blasthreads, 4);
        #endif
    }
    return kcpp_data->n_threads;
}

generation_outputs gpttype_generate(const generation_inputs inputs)
{
    generation_outputs output;

    if(kcpp_data==nullptr)
    {
        printf("\nWarning: KCPP text generation not initialized!\n");
        output.text = nullptr;
        output.status = 0;
        output.prompt_tokens = output.completion_tokens = 0;
        output.stopreason = stop_reason::INVALID;
        generation_finished = true;
        return output;
    }

    if(debugmode==1 && file_format == FileFormat::GGUF_GENERIC)
    {
        llama_perf_context_reset(llama_ctx_v4);
    }

    generation_finished = false; // Set current generation status
    generated_tokens.clear(); // New Generation, new tokens
    delayed_generated_tokens.clear();

    concat_output_mtx.lock();
    concat_output = "";
    concat_output_reader_copy_poll = "";
    concat_output_reader_copy_res = "";
    concat_output_mtx.unlock();
    last_stop_reason = stop_reason::OUT_OF_TOKENS;
    stop_sequence.clear();
    special_stop_sequence.clear();
    dry_repeat_count.clear();
    dry_sequence_breakers.clear();
    dry_max_token_repeat.clear();
    top_picks_history.clear();
    early_abort = false;

    double time0 = 0, time1 = 0, time2 = 0;
    timer_start();

    for(int x=0;x<inputs.stop_sequence_len;++x)
    {
        std::string stopper = inputs.stop_sequence[x];
        if(stopper!="")
        {
            stop_sequence.push_back(stopper);

            //if it tokenizes to a single token, AND it's a single non-printable special token, use that
            std::vector<int> tmp;
            TokenizeString(stopper, tmp, file_format, false);

            if(tmp.size()==1) //tokenizes to exactly 1 special token
            {
                int specialid = tmp[0];
                std::string tokenizedstr = FileFormatTokenizeID(specialid, file_format);
                if(tokenizedstr=="") //must NOT have a text representation
                {
                    special_stop_sequence.push_back(specialid);
                }
            }
        }
    }

    //handle custom token bans and antislop phrase banning
    banned_phrases.clear();
    delayed_generated_tokens_limit = 0;
    antislop_banned_token_ids.clear();
    banned_tokens.clear();
    for(int x=0;x<inputs.banned_tokens_len;++x)
    {
        std::string word = inputs.banned_tokens[x];
        word = toLowerCase(word);
        if(word!="")
        {
            std::vector<int> toks;
            TokenizeString(word, toks, file_format, false);
            int tokcount = toks.size();
            if(tokcount==0)
            {
                continue;
            }
            if(tokcount==1 && word.length()<2) //only use banned tokens for single characters
            {
                banned_tokens.push_back(word);
            }
            else
            {
                tokcount += 3; //add some extra buffer
                delayed_generated_tokens_limit = (tokcount > delayed_generated_tokens_limit ? tokcount : delayed_generated_tokens_limit);
                banned_phrases.push_back(word);
            }
        }
    }

    banned_token_ids.clear();
    if(banned_tokens.size()>0)
    {
        // if(debugmode==1)
        // {
        printf("\nBanning %zu single character sequences...",banned_tokens.size());
        // }
        for(int v=0;v<n_vocab;++v)
        {
            std::string word = FileFormatTokenizeID(v,file_format, true);
            word = toLowerCase(word);
            for(int i=0;i<banned_tokens.size();++i)
            {
                if (word.find(banned_tokens[i]) != std::string::npos)
                {
                    banned_token_ids.push_back(v);
                    break;
                }
            }
        }
        // if(debugmode==1)
        // {
        printf("\nBanned a total of %zu individual tokens.\n",banned_token_ids.size());
        // }
    }

    // if(debugmode==1 && banned_phrases.size()>0)
    if(banned_phrases.size()>0)
    {
        printf("\nBanned a total of %zu phrases, with max token count of %d.\n",banned_phrases.size(),delayed_generated_tokens_limit);
    }

    logit_biases.clear();
    for(int x=0;x<inputs.logit_biases_len;++x)
    {
        int32_t t_id = inputs.logit_biases[x].token_id;
        float bias = inputs.logit_biases[x].bias;
        if(t_id >= 0 && t_id < n_vocab && bias!=0)
        {
           logit_biases.push_back(inputs.logit_biases[x]);
        }
    }

    std::string addedmemory = inputs.memory;

    //clear previous run llava embd memory, just-in-time free
    for(int i=0;i<llava_images.size();++i)
    {
        if(llava_images[i].b64data!="" && llava_images[i].clp_img_embd!=nullptr)
        {
            free(llava_images[i].clp_img_embd);
            llava_images[i].clp_img_embd = nullptr;
        }
    }
    llava_images.clear();
    std::string new_llava_composite = "";
    for(int x=0;x<images_max;++x)
    {
        std::string item = inputs.images[x];
        if(item!="")
        {
            llava_image lv;
            lv.b64data = item;
            llava_images.push_back(lv);
            new_llava_composite += item;
        }
    }
    if(llava_composite_image_signature!=new_llava_composite)
    {
        //images have changed. swap identifiers to force reprocessing
        current_llava_identifier = (current_llava_identifier==LLAVA_TOKEN_IDENTIFIER_A?LLAVA_TOKEN_IDENTIFIER_B:LLAVA_TOKEN_IDENTIFIER_A);
        llava_composite_image_signature = new_llava_composite;
        if(debugmode==1)
        {
            printf("\nLLAVA images changed, existing cache invalidated");
        }
    }

    kcpp_data->prompt = inputs.prompt;
    kcpp_data->seed = inputs.seed;
    kcpp_data->n_predict = inputs.max_length;
    kcpp_data->top_k = inputs.top_k;
    kcpp_data->top_p = inputs.top_p;
    kcpp_data->min_p = inputs.min_p;
    kcpp_data->typical_p = inputs.typical_p;
    kcpp_data->tfs_z = inputs.tfs;
    kcpp_data->temp = inputs.temperature;
    kcpp_data->repeat_last_n = inputs.rep_pen_range;
    kcpp_data->rep_pen_slope = inputs.rep_pen_slope;
    kcpp_data->repeat_penalty = inputs.rep_pen;
    kcpp_data->presence_penalty = inputs.presence_penalty;
    kcpp_data->mirostat = inputs.mirostat;
    kcpp_data->mirostat_eta = inputs.mirostat_eta;
    kcpp_data->mirostat_tau = inputs.mirostat_tau;
    kcpp_data->dry_multiplier = inputs.dry_multiplier;
    kcpp_data->dry_base = inputs.dry_base;
    kcpp_data->dry_allowed_length = inputs.dry_allowed_length;
    kcpp_data->dry_penalty_last_n = inputs.dry_penalty_last_n;
    kcpp_data->xtc_threshold = inputs.xtc_threshold;
    kcpp_data->xtc_probability = inputs.xtc_probability;
    kcpp_data->dynatemp_range = inputs.dynatemp_range;
    kcpp_data->dynatemp_exponent = inputs.dynatemp_exponent;
    kcpp_data->n_ctx = inputs.max_context_length;
    kcpp_data->smoothing_factor = inputs.smoothing_factor;

    // Parse dry sequence breakers / restart sequences
    kcpp_data->dry_sequence_breakers.clear();
    dry_sequence_breakers.clear();

    if (kcpp_data->dry_multiplier > 0)
    {
        for (int x = 0; x < inputs.dry_sequence_breakers_len; ++x)
        {
            std::string word = inputs.dry_sequence_breakers[x];
            if (word != "")
            {
                kcpp_data->dry_sequence_breakers.push_back(word);
            }
        }
        if (kcpp_data->dry_sequence_breakers.size() > 0)
        {
            // Restrict the maximum length of sequences used as sequence breakers. There are
            // very few use cases for a long sequence breaker, and limiting the max length
            // prevents a potential denial of service attack in which long repetitive sequence
            // breakers could result in slow DRY sampling with a suitably crafted context.
            const int MAX_CHAR_LEN = 40;
            const int MAX_SEQ_LEN = 20;

            // if (debugmode == 1)

            printf("\nProcessing %zu dry break strings...", kcpp_data->dry_sequence_breakers.size());

            for (auto sequence_break : kcpp_data->dry_sequence_breakers)
            {
                if (sequence_break.size() > MAX_CHAR_LEN)
                {
                    sequence_break.resize(MAX_CHAR_LEN);
                }
                GetOverlappingTokenSequences(sequence_break, dry_sequence_breakers, MAX_SEQ_LEN);
            }
            if (debugmode == 1)
            {
                int trivial = 0, non_trivial = 0;
                for (const auto &seq : dry_sequence_breakers)
                {
                    if (seq.second.empty())
                    {
                        ++trivial;
                    }
                    else
                    {
                        ++non_trivial;
                    }
                }
                printf("\nFound a total of %zu restart heads, %d trivial, %d non-trivial.\n", dry_sequence_breakers.size(), trivial, non_trivial);
            }
        }
    }

    bool stream_sse = inputs.stream_sse;

    bool allow_regular_prints = (debugmode!=-1 && !inputs.quiet) || debugmode >= 1;


    std::string grammarstr = inputs.grammar;
    bool grammar_retain_state = inputs.grammar_retain_state;
    if(grammar_retain_state)
    {
        if(grammarstr=="" || current_grammar!=grammarstr) //if grammar is identical, retain state
        {
            load_grammar(grammarstr);
        }
    }
    else
    {
        load_grammar(grammarstr);
    }
    current_grammar = grammarstr;


    if (kcpp_data->repeat_last_n < 1)
    {
        kcpp_data->repeat_last_n = 1;
    }
    if (kcpp_data->rep_pen_slope > 1 || kcpp_data->rep_pen_slope<=0)
    {
        kcpp_data->rep_pen_slope = 1;
    }
    if (kcpp_data->top_k < 1)
    {
        kcpp_data->top_k = n_vocab; // all tokens in the vocabulary should be considered if top k is disabled
    }
    if (kcpp_data->seed <= 0 || kcpp_data->seed==0xFFFFFFFF)
    {
        kcpp_data->seed = (((uint32_t)time(NULL)) % 1000000u);
        if(debugmode==1)
        {
            printf("\nUsing Seed: %d",kcpp_data->seed);
        }
    }

    // tokenize the prompt
    std::vector<int> embd_inp;
    std::vector<int> embd_inp_mem; //for storing added memory
    std::vector<int> llava_mem; //for storing dummy tokens that will be consumed by llava
    std::vector<int> llava_sep; //to separate between different llava images

    int32_t nctx = kcpp_data->n_ctx;

    TokenizeString(kcpp_data->prompt, embd_inp, file_format);

    if(clp_ctx!=nullptr && clp_img_data!=nullptr)
    {
        TokenizeString("\n\n", llava_sep, file_format,false);
        int sepsize = llava_sep.size();

        for(int i=0;i<llava_images.size();++i)
        {
            std::string llava_image = llava_images[i].b64data;
            const std::vector<uint8_t> image_buffer = kcpp_base64_decode(llava_image);
            if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), clp_img_data))
            {
                //failed to load image
                printf("\nError: Clip image %d failed to load!",i);
            }
            else
            {
                if(debugmode==1)
                {
                    printf("\nCreating clip image embed...");
                }
                llava_images[i].clp_image_tokens = 0;
                if (!llava_image_embed_make_with_clip_img(clp_ctx, kcpp_data->n_threads, clp_img_data, &llava_images[i].clp_img_embd, &llava_images[i].clp_image_tokens)) {
                    printf("\nError: Clip image %d failed to create embd!",i);
                }
                if(debugmode==1)
                {
                    printf("\nLLAVA Clip Embed %i used Tokens: %d",i,llava_images[i].clp_image_tokens);
                }
                if(llava_images[i].clp_image_tokens>0 && llava_images[i].clp_image_tokens < nctx)
                {
                    int tokcnt = (i==0?(llava_images[i].clp_image_tokens):(llava_images[i].clp_image_tokens+sepsize));
                    for(int n=0;n<tokcnt;++n)
                    {
                        llava_mem.push_back(current_llava_identifier);
                    }
                }
                else
                {
                    printf("\nWarning: LLAVA Image excluded - Context size too low or not enough clip tokens!\n");
                }
            }
        }
    }

    if(addedmemory!="")
    {
        TokenizeString(addedmemory, embd_inp_mem, file_format);
    }

    //truncate to front of the prompt if its too long
    if (embd_inp.size() + kcpp_data->n_predict > nctx)
    {
        //get bos token
        std::vector<int> bos;
        TokenizeString("", bos, file_format);
        int offset = embd_inp.size() - nctx + kcpp_data->n_predict;
        embd_inp = std::vector<int>(embd_inp.begin() + offset, embd_inp.end());
        //replace bos into front if exists
        if(bos.size()>0 && embd_inp.size()>0)
        {
            embd_inp[0] = bos[0];
        }
    }

    if(llava_mem.size()>0) //stick the llava mem before the added mem
    {
        if(llava_mem.size() + kcpp_data->n_predict + 4 > nctx)
        {
            printf("\nWarning: Too many LLaVA tokens, max context exceeded! They will be ignored!\n");
        }
        else
        {
            std::vector<int> bos;
            TokenizeString("", bos, file_format);
            if(embd_inp_mem.size()>0) //remove existing bos if exists
            {
                if (bos.size()>0 && !embd_inp_mem.empty() && bos[0]==embd_inp_mem[0]) {
                    embd_inp_mem.erase(embd_inp_mem.begin());
                }
            }

            //append llava dummy tokens
            embd_inp_mem.insert(embd_inp_mem.begin(), llava_mem.begin(), llava_mem.end());
            if (bos.size() > 0 && embd_inp_mem.size() > 0)
            {
                embd_inp_mem.insert(embd_inp_mem.begin(), bos[0]);  //insert bos at front
            }

             //shorten memory if needed
            if (embd_inp_mem.size() + kcpp_data->n_predict + 4 > nctx)
            {
                int limit = nctx - (kcpp_data->n_predict + 4);
                if (embd_inp_mem.size() > limit) {
                    embd_inp_mem.resize(limit);
                }
            }
        }
    }

    //added special memory, overwrite if needed
    if(embd_inp_mem.size()>0)
    {
        //remove bos token from prompt, it'll be taken from memory
        std::vector<int> bos;
        TokenizeString("", bos, file_format);
        if (bos.size()>0 && !embd_inp.empty() && bos[0]==embd_inp[0]) {
            embd_inp.erase(embd_inp.begin());
        }

        //shorten memory if needed
        if (embd_inp_mem.size() + kcpp_data->n_predict + 4 > nctx)
        {
            int offset = embd_inp_mem.size() - nctx + kcpp_data->n_predict + 4;
            embd_inp_mem = std::vector<int>(embd_inp_mem.begin() + offset, embd_inp_mem.end());
            //replace bos into front if exists
            if(bos.size()>0 && embd_inp_mem.size()>0)
            {
                embd_inp_mem[0] = bos[0];
            }
        }

        //shorten main prompt by trimming the front if needed
        int addmemtokens = embd_inp_mem.size();
        int totalsize = (addmemtokens + embd_inp.size() + kcpp_data->n_predict);
        if(totalsize > nctx)
        {
            int excess = totalsize - nctx;
            if (embd_inp.size() >= excess) {
                embd_inp.erase(embd_inp.begin(), embd_inp.begin() + excess);
            } else {
                embd_inp.clear();
            }
        }

        //stick memory to front of prompt
        embd_inp.insert(embd_inp.begin(), embd_inp_mem.begin(), embd_inp_mem.end());
    }

    //determine how much npast we have to rewind from the current state
    std::vector<gpt_vocab::id> embd;

    int last_n_size = kcpp_data->repeat_last_n;
    last_n_tokens.resize(last_n_size);

    std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
    n_past = 0;

    if (debugmode==1)
    {
        std::string outstr = "";
        printf("\n\n[Debug: Dump Raw Input Tokens, format: %d]\n", file_format);
        outstr += get_tok_vec_str(embd_inp);
        printf("%s\n", RemoveBell(outstr).c_str());
    }

    bool is_mamba = (file_format == FileFormat::GGUF_GENERIC && file_format_meta.model_architecture==GGUFArch::ARCH_MAMBA);
    bool is_rwkv_new = (file_format == FileFormat::GGUF_GENERIC && file_format_meta.model_architecture==GGUFArch::ARCH_RWKV);
    bool blank_prompt = (addedmemory=="" && kcpp_data->prompt=="");

    if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2 || is_mamba || is_rwkv_new)
    {
        if(!blank_prompt)
        {
            if(kcpp_data->use_fastforward)
            {
                ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, false, true);
            }
        }
        if(is_mamba || is_rwkv_new)
        {
            if(n_past==0)
            {
                llama_kv_cache_clear(llama_ctx_v4);
                if(draft_ctx)
                {
                    llama_kv_cache_clear(draft_ctx);
                }
            }
            else if(embd_inp.size()==0)
            {
                embd_inp.push_back(current_context_tokens[current_context_tokens.size()-1]);
                n_past -= 1;
            }
        }
    }
    else
    {
        bool triggersc = kcpp_data->use_smartcontext;
        if(!blank_prompt) //special case for blank prompts, no fast forward or shifts
        {
            if(kcpp_data->use_fastforward && kcpp_data->use_contextshift && (file_format == FileFormat::GGUF_GENERIC))
            {
                PurgeMissingTokens(llama_ctx_v4, draft_ctx, current_context_tokens, embd_inp, inputs.max_length, nctx);
                triggersc = false;
            }
            if(kcpp_data->use_fastforward)
            {
                ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, triggersc, false);
            }
        }
        if(file_format == FileFormat::GGUF_GENERIC)
        {
            llama_kv_cache_seq_rm(llama_ctx_v4, 0, n_past, -1);
            if(draft_ctx)
            {
                llama_kv_cache_seq_rm(draft_ctx, 0, n_past, -1);
            }
        }
    }

    bool blasmode = (embd_inp.size() >= 32 && kcpp_cpu_has_blas() && kcpp_data->n_batch>=32);

    current_context_tokens.resize(n_past);

    remaining_tokens = kcpp_data->n_predict;
    int input_consumed = 0;
    std::mt19937 rng(kcpp_data->seed);

    //prepare sampler order
    std::vector<samplers> sampler_order;
    if(inputs.sampler_len<=0) //list by value
    {
        sampler_order = {
            KCPP_SAMPLER_REP_PEN,
            KCPP_SAMPLER_TOP_K,
            KCPP_SAMPLER_TOP_A,
            KCPP_SAMPLER_TFS,
            KCPP_SAMPLER_TYP,
            KCPP_SAMPLER_TOP_P,
            KCPP_SAMPLER_TEMP
        };
    }
    else
    {
        for(int i=0;i<inputs.sampler_len;++i)
        {
            sampler_order.push_back(inputs.sampler_order[i]);
        }
    }

    bool startedsampling = false;
    bool v3_use_scratch = true; //for normal inference always use scratch

    speculative_draft_result draft_results; //only use if drafting was used
    bool draft_used = false;

    time0 = timer_check();
    timer_start();

    if(file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
    {
        if(n_past==0)
        {
            if(file_format == FileFormat::RWKV_1)
            {
                rwkv_ctx_v2->state_in = nullptr;
            }
            else
            {
                rwkv_ctx_v3->state_in = nullptr;
            }
        }
        else
        {
            if (file_format == FileFormat::RWKV_1)
            {
                rwkv_ctx_v2->state_in = rwkv_ctx_v2->state_out;
            }
            else
            {
                rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out;
            }

            //if it's empty, push in the final previous token
            if(embd_inp.size()==0 && current_context_tokens.size()>0)
            {
                embd_inp.push_back(current_context_tokens[current_context_tokens.size()-1]);
                current_context_tokens.pop_back();
            }
        }
    }

    if(n_vocab<=0)
    {
        printf("\nWarning! n_vocab is invalid, maybe bad format!");
    }

    if(allow_regular_prints)
    {
        printf("\n");
    }

    if (debugmode==1)
    {
        std::string outstr = "";
        printf("\n[Debug: Dump Forwarded Input Tokens, format: %d]\n", file_format);
        outstr += get_tok_vec_str(embd_inp);
        outstr += "\n\n[Debug: n_past="+std::to_string(n_past)+" Context Size = " + std::to_string(current_context_tokens.size()) + "]\n";
        outstr += get_tok_vec_str(current_context_tokens);
        printf("%s\n\n", RemoveBell(outstr).c_str());
    }

    // if (llama_ctx_v4) {
        // empcats_init(llama_ctx_v4, embd_inp, grammarstr);
    // }

    while (remaining_tokens > 0 && !early_abort)

    {
        gpt_vocab::id id = 0;
        // predict
        unsigned int embdsize = embd.size();
        //print progress
        if (!startedsampling && allow_regular_prints)
        {
            printf("\rProcessing Prompt%s (%d / %zu tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp.size());
        }
        fflush(stdout);

        if (embdsize > 0)
        {
            bool evalres = false;
            if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
            {
                evalres = (llama_v2_eval(llama_ctx_v2, embd.data(), embdsize, n_past, GetThreadsToUse(blasmode))==0);
            }
            else if(file_format == FileFormat::GGJT_3)
            {
                evalres = (llama_v3_eval(llama_ctx_v3, embd.data(), embdsize, n_past, GetThreadsToUse(blasmode))==0);
            }
            else if(file_format == FileFormat::GGUF_GENERIC)
            {
                if(embd.size()!=1 || draft_ctx==nullptr || remaining_tokens<=speculative_chunk_amt || grammar!=nullptr || startedsampling==false) //for large batch, or if no draft model, PP/TG as usual
                {
                    draft_used = false;
                    bool use_mrope = (file_format==FileFormat::GGUF_GENERIC && file_format_meta.model_architecture == GGUFArch::ARCH_QWEN2VL);
                    kcpp_embd_batch batch = kcpp_embd_batch(embd, n_past, use_mrope, false);
                    evalres = (llama_decode(llama_ctx_v4, batch.batch)==0);
                    if(draft_ctx)
                    {
                        evalres = (evalres && (llama_decode(draft_ctx, batch.batch)==0));
                    }
                } else { //individual tokens AND speculative is used (generation)
                    draft_used = true;
                    draft_results = speculative_decoding_eval_chunk(draft_ctx, llama_ctx_v4, embd, n_vocab, n_past);
                    evalres = draft_results.draft_success;
                    if(debugmode==1)
                    {
                        std::string draftedtoks = get_tok_vec_str(draft_results.draftids);
                        printf("\nDrafted %d Tokens: [%s]\n",speculative_chunk_amt,draftedtoks.c_str());
                    }
                }
            }
            else if(file_format==FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
            {
                if (file_format == FileFormat::RWKV_1)
                {
                    evalres = rwkv_v2_eval(rwkv_ctx_v2, embd[0], rwkv_ctx_v2->state_in, rwkv_ctx_v2->state_out, rwkv_ctx_v2->logits_out);
                    memcpy(logits.data(), rwkv_ctx_v2->logits_out, sizeof(float) * rwkv_vocab.size());
                    rwkv_ctx_v2->state_in = rwkv_ctx_v2->state_out;
                }
                else
                {
                    if(embd.size()>1)
                    {
                        evalres = rwkv_eval_sequence(rwkv_ctx_v3, GetThreadsToUse(blasmode), (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
                    }
                    else
                    {
                        bool ignoreLogits = (!startedsampling && ((int)embd_inp.size() > input_consumed + 2));
                        evalres = rwkv_eval(rwkv_ctx_v3, GetThreadsToUse(blasmode), embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, ignoreLogits?nullptr:rwkv_ctx_v3->logits_out);
                    }

                    memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * rwkv_vocab.size());
                    rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out;
                }
            }
            else if(file_format==FileFormat::GPT2_1)
            {
                evalres = legacy_gpt2_eval(gpt2_ctx_v1, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, file_format);
            }
            else if(file_format==FileFormat::GPT2_2 || file_format==FileFormat::GPT2_3)
            {
                evalres = gpt2_v2_eval(gpt2_ctx_v2, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, file_format);
            }
            else if(file_format==FileFormat::GPT2_4)
            {
                evalres = gpt2_eval(gpt2_ctx_v3, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, v3_use_scratch);
            }
            else if(file_format==FileFormat::NEOX_1 || file_format == FileFormat::NEOX_2 || file_format == FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5)
            {
                evalres = gpt_neox_v2_eval(neox_ctx_v2, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token);
            }
            else if(file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
            {
                evalres = gpt_neox_eval(neox_ctx_v3, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, v3_use_scratch);
            }
            else if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2)
            {
                evalres = legacy_gptj_eval(gptj_ctx_v1, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, file_format);
            }
            else if(file_format==FileFormat::GPTJ_3 || file_format==FileFormat::GPTJ_4)
            {
                evalres = gptj_v2_eval(gptj_ctx_v2, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token);
            }
            else if(file_format==FileFormat::GPTJ_5)
            {
                evalres = gptj_eval(gptj_ctx_v3, GetThreadsToUse(blasmode), n_past, embd, logits, mem_per_token, v3_use_scratch);
            }
            else if(file_format==FileFormat::MPT_1)
            {
                evalres = mpt_eval(mpt_ctx_v3, GetThreadsToUse(blasmode), n_past, embd, logits, false, mem_per_token, v3_use_scratch);
            }
            else
            {
                printf("\nCannot find eval function\n");
            }

            if (!evalres)
            {
                fprintf(stderr, "\nFailed to predict at %d! Check your context buffer sizes!\n",n_past);
                output.text = nullptr;
                output.status = 0;
                output.prompt_tokens = output.completion_tokens = 0;
                output.stopreason = stop_reason::INVALID;
                generation_finished = true;
                return output;
            }
        }

        n_past += embd.size();
        embd.clear();

        if (!early_abort && (int)embd_inp.size() <= input_consumed) //if decoding was aborted, DO NOT perform any sampling
        {
            // out of user input, sample next token
            const float top_k = kcpp_data->top_k;
            const float top_p = kcpp_data->top_p;
            const float min_p = kcpp_data->min_p;
            const float temp = kcpp_data->temp;
            const float top_a = inputs.top_a;
            const float repeat_penalty = kcpp_data->repeat_penalty;
            const float presence_penalty = kcpp_data->presence_penalty;
            const float typical_p = kcpp_data->typical_p;
            const float tfs_z = kcpp_data->tfs_z;
            const float dynatemp_range = kcpp_data->dynatemp_range;
            const float dynatemp_exponent = kcpp_data->dynatemp_exponent;
            const float smoothing_factor = kcpp_data->smoothing_factor;

            if (!startedsampling)
            {
                startedsampling = true;
                time1 = timer_check();
                timer_start();
                if(allow_regular_prints)
                {
                    printf("\n");
                }
            }

            unsigned int eosID = GetEosID(file_format, n_vocab);
            unsigned int eotID = GetEotID(file_format);
            float * logitsPtr;
            float lowestLogit = 0;
            int btsize = banned_token_ids.size();

            //sample pending logits. usually only 1, unless speculative decoding
            int logits_to_sample = 1;
            int logits_sampled = 0;
            bool abort_draft = false;
            if(draft_used)
            {
                logits_to_sample = draft_results.drafted_amount;
            }
            while(logits_sampled<logits_to_sample && remaining_tokens>0 && !abort_draft && !early_abort)
            {
                if(logits_sampled>0)
                {
                    //this is not the first loop, so we need to increment some things
                    n_past += 1;
                }
                if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 || file_format == FileFormat::GGJT_3 || file_format == FileFormat::GGUF_GENERIC)
                {
                    if(file_format == FileFormat::GGUF_GENERIC)
                    {
                        if(draft_used)
                        {
                            logitsPtr = draft_results.actual_logits[logits_sampled];
                        }
                        else
                        {
                            logitsPtr = llama_get_logits(llama_ctx_v4);
                        }
                    }
                    else if(file_format == FileFormat::GGJT_3)
                    {
                        logitsPtr = llama_v3_get_logits(llama_ctx_v3);
                    }
                    else
                    {
                        logitsPtr = llama_v2_get_logits(llama_ctx_v2);
                    }
                    lowestLogit = LowestLogit(logitsPtr,n_vocab);
                }
                else
                {

/*                     logitsPtr = llama_v2_get_logits(llama_ctx_v2);
                }
                lowestLogit = LowestLogit(logitsPtr,n_vocab);
            }
            else
            {
                logitsPtr = logits.data();
                lowestLogit = LowestLogit(logits);
            }

            if (!inputs.allow_eos_token && !inputs.bypass_eos_token)
            {
                // set the logit of the eos token to very low to avoid sampling it
                if(eosID!=LLAMA_TOKEN_NULL)
                {
                    logitsPtr[eosID] = lowestLogit;
                }
                if(eotID!=-1)
                {
                    logitsPtr[eotID] = lowestLogit;
                }
            }
            if(btsize>0)
            {
                for(int t=0;t<btsize;++t)
                {
                    logitsPtr[banned_token_ids[t]]=lowestLogit;
                }
            }

            //handle temp bans from antislop
            if (antislop_banned_token_ids.find(n_past) != antislop_banned_token_ids.end()) {
                std::vector<int>& bans = antislop_banned_token_ids[n_past];

                //unwanted print, but.. ^^
                // print_tok_vec_str(bans);

                for(int t=0;t<bans.size();++t)
                {
                    logitsPtr[bans[t]]=lowestLogit;
                }
            }

            if (llama_ctx_v4) {
                empcats_step_pre(llama_ctx_v4, logitsPtr);
            }

            id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty, kcpp_data->rep_pen_slope, presence_penalty,
            top_k, top_a, top_p, min_p, typical_p, tfs_z, temp, rng,
            kcpp_data->mirostat, kcpp_data->mirostat_tau, kcpp_data->mirostat_eta,
            kcpp_data->dry_multiplier, kcpp_data->dry_base,
            kcpp_data->dry_allowed_length, kcpp_data->dry_penalty_last_n, kcpp_data->xtc_threshold, kcpp_data->xtc_probability,
            sampler_order, grammar, dynatemp_range, dynatemp_exponent, smoothing_factor);

            if (llama_ctx_v4) {
                empcats_step_post(llama_ctx_v4, id );
            }

            if (grammar != nullptr) {
                grammar_accept_token(file_format, n_vocab, grammar, id);
            }

            if (!last_n_tokens.empty())
            {
                last_n_tokens.erase(last_n_tokens.begin());
            }
            last_n_tokens.push_back(id);
            current_context_tokens.push_back(id);

            // add it to the context
            embd.push_back(id);

            // decrement remaining sampling budget
            --remaining_tokens;

            for (auto eid : embd)
            {
                std::string tokenizedstr = FileFormatTokenizeID(eid, file_format, inputs.render_special);
                if(!inputs.render_special && (eid==eosID || (eid==eotID && eid!=-1) || VecContainsIntVal(special_stop_sequence,id))) //extra filter to avoid unwanted special tokens
                {
                    tokenizedstr = ""; //prevent render */

                    logitsPtr = logits.data(); //legacy rwkv, neox, gptj etc
                    lowestLogit = LowestLogit(logits);

                }

                if (!inputs.allow_eos_token && !inputs.bypass_eos_token)
                {
                    // set the logit of the eos token to very low to avoid sampling it
                    if(eosID!=LLAMA_TOKEN_NULL)
                    {
                        logitsPtr[eosID] = lowestLogit;
                    }
                    if(eotID!=-1)
                    {
                        logitsPtr[eotID] = lowestLogit;
                    }
                }
                if(btsize>0)
                {
                    for(int t=0;t<btsize;++t)
                    {
                        logitsPtr[banned_token_ids[t]]=lowestLogit;
                    }
                }

                //handle temp bans from antislop
                if (antislop_banned_token_ids.find(n_past) != antislop_banned_token_ids.end()) {
                    std::vector<int>& bans = antislop_banned_token_ids[n_past];
                    for(int t=0;t<bans.size();++t)
                    {
                        logitsPtr[bans[t]]=lowestLogit;
                    }
                }

                // if (llama_ctx_v4) {
                    // empcats_step_pre(llama_ctx_v4, logitsPtr);
                // }

                id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty, kcpp_data->rep_pen_slope, presence_penalty,
                top_k, top_a, top_p, min_p, typical_p, tfs_z, temp, rng,
                kcpp_data->mirostat, kcpp_data->mirostat_tau, kcpp_data->mirostat_eta,
                kcpp_data->dry_multiplier, kcpp_data->dry_base,
                kcpp_data->dry_allowed_length, kcpp_data->dry_penalty_last_n, kcpp_data->xtc_threshold, kcpp_data->xtc_probability,
                sampler_order, grammar, dynatemp_range, dynatemp_exponent, smoothing_factor);

                // if (llama_ctx_v4) {
                    // empcats_step_post(llama_ctx_v4, id );
                // }

                if(draft_used)
                {
                    int32_t draftedid = draft_results.draftids[logits_sampled];
                    if(debugmode==1)
                    {
                        std::string drafttok = FileFormatTokenizeID(draftedid, file_format, true);
                        std::string realtok = FileFormatTokenizeID(id, file_format, true);
                        printf("(Draft %d/%d): Predicted=%d (%s), Actual=%d (%s) [%s]\n",(logits_sampled+1),logits_to_sample,draftedid,drafttok.c_str(),id,realtok.c_str(),(draftedid==id?"PASS":"FAIL"));
                    }
                    if(draftedid!=id) //draft mismatch, abort
                    {
                        abort_draft = true;
                    }
                }

                if (grammar != nullptr) {
                    grammar_accept_token(file_format, n_vocab, grammar, id);
                }

                if (!last_n_tokens.empty())
                {
                    last_n_tokens.erase(last_n_tokens.begin());
                }
                last_n_tokens.push_back(id);
                current_context_tokens.push_back(id);

                // add it to the context
                embd.clear();
                embd.push_back(id);

                // decrement remaining sampling budget
                --remaining_tokens;

                for (auto eid : embd)
                {
                    std::string tokenizedstr = FileFormatTokenizeID(eid, file_format, inputs.render_special);
                    if(!inputs.render_special && (eid==eosID || (eid==eotID && eid!=-1) || VecContainsIntVal(special_stop_sequence,id))) //extra filter to avoid unwanted special tokens
                    {
                        tokenizedstr = ""; //prevent render
                    }

                    delayed_generated_tokens.push_back(tokenizedstr);
                    while(delayed_generated_tokens.size() > delayed_generated_tokens_limit && delayed_generated_tokens.size() > 0)
                    {
                        generated_tokens.push_back(delayed_generated_tokens[0]);
                        concat_output_mtx.lock();
                        concat_output += delayed_generated_tokens[0];
                        concat_output_mtx.unlock();
                        delayed_generated_tokens.pop_front();
                    }
                }

                if (startedsampling && allow_regular_prints)
                {
                    printf("\rGenerating (%d / %d tokens)", (kcpp_data->n_predict - remaining_tokens), kcpp_data->n_predict);
                }
                if(debugmode==1 && top_picks_history.size()>0)
                {
                    printf(" [");
                    bool firstloop = true;
                    TopPicksData toppick = top_picks_history[top_picks_history.size()-1];
                    std::string topstr = toppick.selected_token;
                    ::utreplace(topstr, "\n", "\\n");
                    printf("(%s %.2f%%)", RemoveBell(topstr).c_str(), toppick.selected_probability*100);
                    int maxtoshow = (toppick.tokenid.size()>4?4:toppick.tokenid.size());
                    for (int i=0;i<maxtoshow;++i)
                    {
                        if(toppick.tokenid[i]==toppick.selected_tokenid)
                        {
                            continue;
                        }
                        printf(" ");
                        std::string tokenizedstr = toppick.tokens[i];
                        ::utreplace(tokenizedstr, "\n", "\\n");
                        printf("(%s %.2f%%)", RemoveBell(tokenizedstr).c_str(), toppick.p[i]*100);
                    }
                    printf("]\n");
                }

                //anti slop detection
                if (banned_phrases.size() > 0)
                {
                    std::string scanstr = "";
                    for (int i = 0; i < delayed_generated_tokens.size(); ++i)
                    {
                        scanstr += delayed_generated_tokens[i];
                    }
                    scanstr = toLowerCase(scanstr);
                    for (const auto &matched : banned_phrases)
                    {
                        std::string matched_lower = toLowerCase(matched);
                        if (scanstr.find(matched_lower) != std::string::npos)
                        {
                            //find the position in the string that contains all necessary tokens
                            std::string checkstr = "";
                            int rewind_amt = 0;
                            for (int i = delayed_generated_tokens.size() - 1; i >= 0; --i)
                            {
                                checkstr = delayed_generated_tokens[i] + checkstr;
                                ++rewind_amt;
                                if (toLowerCase(checkstr).find(matched_lower) != std::string::npos)
                                {
                                    break;
                                }
                            }
                            if (rewind_amt > 0 && (current_context_tokens.size() - rewind_amt) > 0)
                            {
                                int last_tok = current_context_tokens[current_context_tokens.size() - rewind_amt];
                                delayed_generated_tokens.resize(delayed_generated_tokens.size() - rewind_amt);
                                ContextRewind(embd, current_context_tokens, n_past, last_n_tokens, rewind_amt);

                                //immediately terminate drafting if used
                                abort_draft = true;

                                // Check if the key exists
                                int banindex = n_past+1;
                                if (antislop_banned_token_ids.find(banindex) == antislop_banned_token_ids.end()) {
                                    antislop_banned_token_ids[banindex] = std::vector<int>();
                                }
                                std::vector<int>& current_ids = antislop_banned_token_ids[banindex];
                                current_ids.push_back(last_tok);

                                // if (allow_regular_prints && debugmode == 1)
                                if (allow_regular_prints)
                                {
                                    auto match_clean = matched;
                                    replace_all(match_clean, "\n", "\\n");
                                    printf("\n(Banned Phrase Detected: %s - Add ID %d to banlist at index %d, and rewinding %d tokens)\n", match_clean.c_str(), last_tok, banindex, rewind_amt);
                                }

                                break;
                            }
                        }
                    }
                }

                if(!early_abort)
                {
                    if(!inputs.bypass_eos_token && inputs.allow_eos_token && (id==eosID || (id==eotID && id!=-1)))
                    {
                        if(allow_regular_prints)
                        {
                            printf("\n(EOS token triggered! ID:%d)",id);
                        }
                        early_abort = true;
                        last_stop_reason = stop_reason::EOS_TOKEN_HIT;
                    }
                }

/*                             // Check if the key exists
                            int banindex = n_past+1;
                            if (antislop_banned_token_ids.find(banindex) == antislop_banned_token_ids.end()) {
                                antislop_banned_token_ids[banindex] = std::vector<int>();
                            }
                            std::vector<int>& current_ids = antislop_banned_token_ids[banindex];
                            current_ids.push_back(last_tok);

                            // if (allow_regular_prints && debugmode == 1)
                            if (allow_regular_prints) */

                if(!early_abort)
                {
                    for (const auto &matched : special_stop_sequence)
                    {
                        if(id==matched)
                        {
                            if(allow_regular_prints)

                            {
                                printf("\n(Special Stop Token Triggered! ID:%d)",matched);
                            }
                            early_abort = true;
                            last_stop_reason = stop_reason::EOS_TOKEN_HIT;
                            break;
                        }
                    }
                }

                if(!early_abort)
                {
                    for (const auto &matched : stop_sequence)
                    {
                        if (concat_output.find(matched) != std::string::npos)
                        {
                            early_abort = true;
                            if(allow_regular_prints)
                            {
                                auto match_clean = matched;
                                replace_all(match_clean, "\n", "\\n");
                                printf("\n(Stop sequence triggered: %s)", match_clean.c_str());
                            }
                            last_stop_reason = stop_reason::CUSTOM_STOPPER;
                            break;
                        }
                    }
                }

                logits_sampled += 1;
            }

            //if we have somehow skipped ahead (e.g drafting), ensure that all tokens after npast are purged
            if (file_format == FileFormat::GGUF_GENERIC && draft_used)
            {
                llama_kv_cache_seq_rm(llama_ctx_v4, 0, n_past, -1);
                if (draft_ctx) {
                    llama_kv_cache_seq_rm(draft_ctx, 0, n_past, -1);
                }
            }

            fflush(stdout);
        }
        else if(!early_abort) //do not ingest prompt if aborted!
        {
            // some user input remains from prompt or interaction, forward it to processing
            while ((int)embd_inp.size() > input_consumed)
            {
                int currtoken = embd_inp[input_consumed];
                if(currtoken==LLAVA_TOKEN_IDENTIFIER_A || currtoken==LLAVA_TOKEN_IDENTIFIER_B) //special llava token hit
                {
                    //if partial batch, dispatch existing first
                    if(embd.size()>0)
                    {
                        break;
                    }
                    else
                    {
                        //batch is empty, do image processing
                        int llavatokenscounted = 0;
                        int llavatokensevaled = 0;
                        int sepsize = llava_sep.size();
                        while(input_consumed < embd_inp.size() && (embd_inp[input_consumed]==LLAVA_TOKEN_IDENTIFIER_A || embd_inp[input_consumed]==LLAVA_TOKEN_IDENTIFIER_B))
                        {
                            if (!last_n_tokens.empty())
                            {
                                last_n_tokens.erase(last_n_tokens.begin());
                            }
                            last_n_tokens.push_back(currtoken);
                            current_context_tokens.push_back(currtoken);
                            ++input_consumed;
                            ++llavatokenscounted;
                        }
                        for(int i=0;i<llava_images.size();++i)
                        {
                            //note: no handling for draft_ctx as we don't support vision for it
                            if(i>0 && sepsize>0)
                            {
                                //add a separator between each image
                                auto evr = llama_decode(llama_ctx_v4, llama_batch_get_one(llava_sep.data(), sepsize));
                                if(evr!=0)
                                {
                                    printf("\nError when appending llava separator: %d\n",evr);
                                }
                                else
                                {
                                    printf("\rProcessing LLaVa Separator (%d tokens)",sepsize);
                                }
                                n_past += sepsize;
                                llavatokensevaled += sepsize;
                            }

                            if(allow_regular_prints)
                            {
                                printf("\rProcessing LLaVa Embedding %d (%d tokens)",(i+1), llava_images[i].clp_image_tokens);
                            }
                            bool err = kcpp_eval_image(llama_ctx_v4,llava_images[i].clp_img_embd,llava_images[i].clp_image_tokens,kcpp_data->n_batch,&n_past);
                            llavatokensevaled += llava_images[i].clp_image_tokens;
                            if(!err)
                            {
                                llava_composite_image_signature = ""; //force invalidate
                                fprintf(stderr, "\nFailed to eval llava image at %d!\n",n_past);
                                output.text = nullptr;
                                output.status = 0;
                                output.prompt_tokens = output.completion_tokens = 0;
                                output.stopreason = stop_reason::INVALID;
                                generation_finished = true;
                                return output;
                            }
                        }
                        if(llavatokenscounted!=llavatokensevaled)
                        {
                            llava_composite_image_signature = ""; //force invalidate
                            fprintf(stderr, "\nLLAVA image tokens mismatch at %d! (%d vs %d tokens)\n",n_past,llavatokenscounted,llavatokensevaled);
                            output.text = nullptr;
                            output.status = 0;
                            output.prompt_tokens = output.completion_tokens = 0;
                            output.stopreason = stop_reason::INVALID;
                            generation_finished = true;
                            return output;
                        }
                    }
                }
                else
                {
                    embd.push_back(currtoken);
                    if (!last_n_tokens.empty())
                    {
                        last_n_tokens.erase(last_n_tokens.begin());
                    }
                    last_n_tokens.push_back(currtoken);
                    current_context_tokens.push_back(currtoken);
                    ++input_consumed;
                    if ((int)embd.size() >= kcpp_data->n_batch)
                    {
                        break;
                    }
                }

            }
        }
    }

    //flush any remaining delayed tokens
    while(delayed_generated_tokens.size() > 0)
    {
        generated_tokens.push_back(delayed_generated_tokens[0]);
        concat_output_mtx.lock();
        concat_output += delayed_generated_tokens[0];
        concat_output_mtx.unlock();
        delayed_generated_tokens.pop_front();
    }

    if(debugmode==1 && file_format == FileFormat::GGUF_GENERIC)
    {
        printf("\n");
        llama_perf_context_print(llama_ctx_v4);
    }

    time2 = timer_check();
    float pt1 = (time1*1000.0/(embd_inp.size()==0?1:embd_inp.size()));
    float ts1 = (1000.0/pt1);
    int realnpredict = kcpp_data->n_predict-remaining_tokens;
    float pt2 = (time2*1000.0/(realnpredict<=0?1:realnpredict));
    float ts2 = (1000.0/pt2);
    float tokens_per_second = (realnpredict <= 0 ? 0 : realnpredict / (time1 + time2));
    printf("\n[%s] CtxLimit:%d/%d, Amt:%d/%d, Init:%.2fs, Process:%.2fs (%.1fms/T = %.2fT/s), Generate:%.2fs (%.1fms/T = %.2fT/s), Total:%.2fs (%.2fT/s)",get_timestamp_str().c_str(),(int)current_context_tokens.size(),(int)nctx, realnpredict, kcpp_data->n_predict, time0, time1, pt1, ts1, time2, pt2, ts2, (time1 + time2), tokens_per_second);
    fflush(stdout);
    output.status = 1;
    int finaltokcount = (int)current_context_tokens.size()-realnpredict;
    output.prompt_tokens = (finaltokcount<0?0:finaltokcount);
    output.completion_tokens = realnpredict;
    output.stopreason = last_stop_reason;
    last_eval_time = pt2;
    last_process_time = pt1;
    last_token_count = realnpredict;
    last_seed = kcpp_data->seed;
    total_gens += 1;
    concat_output_mtx.lock();
    concat_output_reader_copy_res = concat_output;
    concat_output_mtx.unlock();
    output.text = concat_output_reader_copy_res.c_str();
    generation_finished = true;
    return output;
}