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 <time.h>
#include <mutex>
#include "model_adapter.h"
#include "otherarch.h"
#include "grammar-parser.h"

//for easier compilation
//concat source files into one file for compilation purposes
#include "llama_v2.cpp"
#include "llama_v3.cpp"
#include "llama.cpp"
#include "utils.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"

//shared
std::string executable_path = "";
std::string lora_filename = "";
std::string lora_base = "";
bool generation_finished;
float last_process_time = 0;
float last_eval_time = 0;
int last_token_count = 0;
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
grammar_parser::parse_state 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 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 gpt_params params;
static int max_context_limit_at_load = 0;
static int n_past = 0;
static int n_threads = 4;
static int n_blasthreads = 4;
static int n_batch = 8;
static bool useSmartContext = false;
static bool useContextShift = false;
static int blasbatchsize = 512;
static int debugmode = 0; //-1 = hide all, 0 = normal, 1 = showall
static std::string modelname;
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<std::string> banned_tokens;
static std::vector<int> banned_token_ids;
static std::vector<llama_token_data> top_picks;
static int remaining_tokens = 0;
static int stopper_unused_tokens = 0;
static std::mutex concat_output_mtx;
static std::string concat_output = "";
static std::string concat_output_reader_copy = "";

const int extra_context_handle_fragmentation = 80;

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)
{
    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_LLAMA || file_format==FileFormat::GGUF_FALCON)
    {
        return std::string(llama_token_to_str(llama_ctx_v4, id));
    }
    else
    {
        return vocab.id_to_token[id];
    }
}

static void TokenizeString(const std::string & str_to_tokenize, std::vector<int> & output_tokens, FileFormat file_format)
{
    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_LLAMA || file_format==FileFormat::GGUF_FALCON)
    {
        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, true);
        }
        else if (file_format == FileFormat::GGML)
        {
            output_tokens = ::legacy_llama_v2_tokenize(llama_ctx_v2, str_to_tokenize, true);
        }
        else if (file_format == FileFormat::GGJT_3)
        {
            output_tokens = ::llama_v3_tokenize(llama_ctx_v3, str_to_tokenize, true);
        }
        else
        {
            output_tokens = ::llama_tokenize(llama_ctx_v4, str_to_tokenize, true, true);
        }
    }
    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_LLAMA || file_format==FileFormat::GGUF_FALCON)
    {
        if(file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON)
        {
            eosID = llama_token_eos(&(llama_ctx_v4->model));
        }
        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 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) + " (" + std::to_string(id) + ")', ";
    }
    ::utreplace(tmp, "\n", "\\n");
    return tmp;
}
static void print_tok_vec_str(std::vector<int> &vec)
{
    printf("\n%s", get_tok_vec_str(vec).c_str());
}


llama_token sample_token(llama_token_data_array * candidates, std::mt19937 & rng)
{
    llama_sample_softmax(nullptr, candidates);
    std::vector<float> probs;
    probs.reserve(candidates->size);
    top_picks.clear();
    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);

    if(debugmode==1)
    {
        top_picks.push_back(candidates->data[idx]);
        for (size_t i = 0; (i < candidates->size && i<4); ++i)
        {
            if(i!=idx)
            {
                top_picks.push_back(candidates->data[i]);
            }
        }
    }

    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);
    llama_sample_softmax(nullptr, 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
    llama_sample_top_k(nullptr, candidates, int(k),1);
    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)
{
    llama_sample_softmax(nullptr, 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
    llama_sample_softmax(nullptr, 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;
    }

    llama_sample_softmax(nullptr, 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_rep_pen(int n_ctx, int rep_pen_range, float rep_pen, 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;
    float penalty = rep_pen;

    if (last_tokens_size == 0 || penalty == 1.0f) {
        return;
    }

    const int64_t t_start_sample_us = ggml_time_us();

    for (size_t i = 0; i < candidates->size; ++i) {
        const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
        if (token_iter == last_tokens + last_tokens_size) {
            continue;
        }

        // 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->sorted = false;

}

void sample_temperature(llama_token_data_array * candidates_p, float temp)
{
    if (temp <= 0)
    {
        // Imitate greedy sampling
        temp = 0.00390625f; //cannot be zero else div0, this is 1/256
        llama_sample_temperature(nullptr, candidates_p, temp);
        llama_sample_top_k(nullptr, candidates_p, 1, 1); //only want first candidate
    }
    else
    {
        llama_sample_temperature(nullptr, candidates_p, temp);
    }
}

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);

    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) {
            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 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, const std::vector<samplers> & sampler_order, llama_grammar * grammar)
{
    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});
    }

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

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

    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, &candidates_p);
        sample_temperature(&candidates_p, temp);
        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:
                    llama_sample_top_k(nullptr, &candidates_p, top_k,1);
                    break;
                case KCPP_SAMPLER_TOP_A:
                    sample_top_a(&candidates_p,top_a,1);
                    break;
                case KCPP_SAMPLER_TOP_P:
                    llama_sample_top_p(nullptr, &candidates_p, top_p,1);
                    llama_sample_min_p(nullptr, &candidates_p, min_p,1);
                    break;
                case KCPP_SAMPLER_TFS:
                    llama_sample_tail_free(nullptr, &candidates_p, tfs,1);
                    break;
                case KCPP_SAMPLER_TYP:
                    llama_sample_typical(nullptr, &candidates_p, typical_p,1);
                    break;
                case KCPP_SAMPLER_TEMP:
                    sample_temperature(&candidates_p, temp);
                    break;
                case KCPP_SAMPLER_REP_PEN:
                    sample_rep_pen(n_ctx, rep_pen_range, rep_pen, &candidates_p);
                    break;
                default:
                    printf("\nSampleLogits: Unknown Sampler : %d",sampler_order[i]);
                    break;
            }
        }
        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)) {
        for (const auto & stack : grammar->stacks) {
            if (stack.empty()) {
                return;
            }
        }
        GGML_ASSERT(false);
    }
    const std::string piece = FileFormatTokenizeID(token,file_format); //llama_token_to_str(ctx, token);

    // 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) {
        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *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(grammar);
        grammar = nullptr;
    }

    if (!gammarstr.empty()) {
        parsed_grammar = grammar_parser::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)
        {
            grammar_parser::print_grammar(stderr, parsed_grammar);
        }
        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
        grammar = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
    }
}

//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, 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 + (nctx/20); //dont trigger shifting if the distance between trimstart and currhead < this
    const int SlackAllowance = 50 + (nctx/60); //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*0.55)), 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(llama_ctx_v4, 0, trimstart, trimstart + diff);
            llama_kv_cache_seq_shift(llama_ctx_v4, 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);
        }
    }

}

ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format, FileFormatExtraMeta file_format_meta)
{
    ggml_time_init();

    file_format = in_file_format;
    n_threads = params.n_threads = inputs.threads;
    n_blasthreads = params.n_threads_batch = inputs.blasthreads;
    n_batch = params.n_batch = inputs.batch_size;
    modelname = params.model = inputs.model_filename;
    useSmartContext = inputs.use_smartcontext;
    useContextShift = inputs.use_contextshift;
    debugmode = inputs.debugmode;
    blasbatchsize = inputs.blasbatchsize;
    if(blasbatchsize<=0)
    {
        blasbatchsize = 8;
    }

    auto clamped_max_context_length = inputs.max_context_length;

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

    params.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 = params.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
    {
        rope_freq_scale = 1.0f;
        if (params.n_ctx <= 2048) //normie mode
        {
            rope_freq_base = 10000.0f;
        }
        else
        {
            //approximate NTK aware ctx
            auto effectivenctx = params.n_ctx;
            if((file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON) && file_format_meta.n_ctx_train > 2048)
            {
                float factor = file_format_meta.n_ctx_train/2048;
                effectivenctx = effectivenctx/factor;
            }
            rope_freq_base = (effectivenctx <= 2048 ? 10000.0f : (effectivenctx <= 3072 ? 26000.0f : (effectivenctx <= 4096 ? 32000.0f : (effectivenctx <= 6144 ? 54000.0f :
            (effectivenctx <= 8192 ? 82684.0f : (effectivenctx <= 12288 ? 140000.0f : (effectivenctx <= 16384 ? 200000.0f : (effectivenctx <= 24576 ? 320000.0f : 440000.0f))))))));

        }

        printf("Using automatic RoPE scaling. If the model has customized RoPE settings, they will be used directly instead!\n");
    }
    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;

    //handle custom token bans
    banned_tokens.clear();
    for(int x=0;x<ban_token_max;++x)
    {
        std::string word = inputs.banned_tokens[x];
        if(word!="")
        {
            banned_tokens.push_back(word);
        }
    }

    //this is used for the mem_per_token eval, openblas needs more RAM
    bool use_scratch = ggml_cpu_has_gpublas();

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

    printf("System Info: %s\n", llama_print_system_info());
    #if defined(GGML_USE_CUBLAS)
    if(ggml_cpu_has_gpublas() && cu_parseinfo_maindevice>0)
    {
        printf("CUBLAS: Set main device to %d\n",cu_parseinfo_maindevice);
        ggml_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.n_parts = -1;
        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(modelname.c_str(), llama_ctx_params_v2);

        if (llama_ctx_v2 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.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,
                                                 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, params.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_paran_parts = -1;
        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;
        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 = blasbatchsize;

        #if defined(GGML_USE_CUBLAS)
        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)
        {
            llama_ctx_params.tensor_split = inputs.tensor_split;
        }
        #endif

        llama_ctx_v3 = llama_v3_init_from_file(modelname.c_str(), llama_ctx_params);

        if (llama_ctx_v3 == NULL)
        {
            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.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,
                                                 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, params.n_threads);
        if(er!=0)
        {
            printf("\nLLAMA EVAL returned nonzero!\n");
        }
        return ModelLoadResult::SUCCESS;
    }
    else if(file_format==FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON)
    {
        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(useContextShift)
        {
           llama_ctx_params.n_ctx += extra_context_handle_fragmentation;
        }

        //llama_ctx_paran_parts = -1;
        llama_ctx_params.seed = -1;
        //llama_ctx_params.f16_kv = true;
        llama_ctx_params.offload_kqv = !inputs.low_vram;
        llama_ctx_params.mul_mat_q = inputs.use_mmq;
        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_FALCON && model_params.n_gpu_layers>0)
        {
            printf("\nGPU layer offload for GGUF FALCON on OpenCL is known to have issues, it has been set to 0.\n");
            model_params.n_gpu_layers = 0;
        }
        #endif
        model_params.main_gpu = cu_parseinfo_maindevice;

        llama_ctx_params.n_batch = blasbatchsize;
        llama_ctx_params.n_threads = n_threads;
        llama_ctx_params.n_threads_batch = n_blasthreads;

        #if defined(GGML_USE_CUBLAS)
        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)
        {
            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(modelname.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, use the model values. Otherwise, use our automatic ones
            if(llamamodel->hparams.rope_freq_base_train!=10000.0f ||
            llamamodel->hparams.rope_freq_scale_train!=1.0f ||
            llamamodel->hparams.rope_scaling_type_train==2)
            {
                // float ropemultiplier = 1.0f;
                // if(llamamodel->hparams.rope_scaling_type_train!=2 &&
                // llamamodel->hparams.n_ctx_train > 2048 && clamped_max_context_length > llamamodel->hparams.n_ctx_train &&
                // llamamodel->hparams.rope_freq_scale_train==1.0f)
                // {
                //     ropemultiplier = (float)llamamodel->hparams.n_ctx_train / (float)clamped_max_context_length;
                //     llama_ctx_params.rope_freq_base = rope_freq_base = llamamodel->hparams.rope_freq_base_train;
                //     llama_ctx_params.rope_freq_scale = rope_freq_scale = ropemultiplier * llamamodel->hparams.rope_freq_scale_train;
                //     printf("Automatic RoPE Scaling: Using (scale:%.3f, base:%.1f).\n", rope_freq_scale, rope_freq_base);
                // }
                // else
                // {
                    printf("Automatic RoPE Scaling: Using model internal value.\n");
                // }
            }
            else
            {
                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);
            }
        }

        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__, modelname.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_apply_lora_from_file(llama_ctx_v4,
                                                 lora_filename.c_str(),
                                                 1.0f,
                                                 lora_base_arg,
                                                 n_threads);
            if (err != 0)
            {
                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
                return ModelLoadResult::FAIL;
            }
        }

        n_vocab = llama_n_vocab(llamamodel);

        //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(), 0, 0));
        if(er!=0)
        {
            printf("\nLLAMA EVAL returned nonzero!\n");
        }
        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(modelname.c_str(), n_threads);
        }
        else //rwkv_2
        {
            rwkv_ctx_v3 = rwkv_init_from_file(modelname.c_str(), 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)
        {
            n_batch = 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
        {
            n_batch = 1; //do not use sequence mode to speedup until it is fixed

            //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, params.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(params.model, gpt2_ctx_v1, vocab, file_format);
        if(res==ModelLoadResult::FAIL)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.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(params.model, gpt2_ctx_v3, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, use_scratch);
            return ModelLoadResult::SUCCESS;
        }
        else
        {
            //newer format has bit unshuffling
            SetQuantsUnshuffled(file_format == FileFormat::GPT2_3);

            ModelLoadResult res = gpt2_v2_model_load(params.model, gpt2_ctx_v2, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.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(params.model, gptj_ctx_v1, vocab, file_format);
        if(res==ModelLoadResult::FAIL)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.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(params.model, gptj_ctx_v3, vocab, inputs.gpulayers);
            if (loadresult == ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, 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, params.n_threads, 0, {4, 5, 6, 7}, logits, mem_per_token, use_scratch);

            if(logits.size()>0 && (IsNanCheck(logits[0]) || LogitsDuplicated(oldlogits,logits)))
            {
                printf("\nBad Logits detected! Retrying GPT-J model loading...");
                ggml_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(params.model, gptj_ctx_v2, vocab, inputs.gpulayers);
            if (loadresult == ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.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, params.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(params.model, neox_ctx_v3, vocab, file_format, inputs.gpulayers);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, 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(params.model, neox_ctx_v2, vocab, file_format);
            if(res==ModelLoadResult::FAIL)
            {
                fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.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, params.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(params.model, mpt_ctx_v3, vocab, inputs.gpulayers);
        if(res==false)
        {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.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, params.n_threads, 0, { 0, 1, 2, 3 }, logits, false, mem_per_token, use_scratch);
        return ModelLoadResult::SUCCESS;
    }
    else
    {
        printf("\nUnknown Model, cannot load.\n");
        return ModelLoadResult::FAIL;
    }

}

bool gpttype_generate_abort()
{
    stopper_unused_tokens = remaining_tokens;
    remaining_tokens = 0;
    return true;
}

std::vector<int> gpttype_get_token_arr(const std::string & input)
{
    if(debugmode==1)
    {
        printf("\nFileFormat: %d, Tokenizing: %s",file_format ,input.c_str());
    }
    std::vector<int> toks;
    TokenizeString(input, toks, file_format);
    int tokcount = toks.size();
    if(debugmode==1)
    {
        printf("\nTokens Counted: %d\n",tokcount);
    }
    return toks;
}

const std::string & gpttype_get_pending_output()
{
    concat_output_mtx.lock();
    concat_output_reader_copy = concat_output;
    concat_output_mtx.unlock();
    return concat_output_reader_copy;
}

generation_outputs gpttype_generate(const generation_inputs inputs, generation_outputs &output)
{
    concat_output_mtx.lock();
    concat_output = "";
    concat_output_reader_copy = "";
    concat_output_mtx.unlock();
    last_stop_reason = stop_reason::OUT_OF_TOKENS;
    stop_sequence.clear();
    for(int x=0;x<stop_token_max;++x)
    {
        std::string stopper = inputs.stop_sequence[x];
        if(stopper!="")
        {
            stop_sequence.push_back(stopper);
        }
    }

    std::string addedmemory = inputs.memory;
    params.prompt = inputs.prompt;
    params.seed = inputs.seed;
    params.n_predict = inputs.max_length;
    params.top_k = inputs.top_k;
    params.top_p = inputs.top_p;
    params.min_p = inputs.min_p;
    params.typical_p = inputs.typical_p;
    params.tfs_z = inputs.tfs;
    params.temp = inputs.temperature;
    params.repeat_last_n = inputs.rep_pen_range;
    params.repeat_penalty = inputs.rep_pen;
    params.mirostat = inputs.mirostat;
    params.mirostat_eta = inputs.mirostat_eta;
    params.mirostat_tau = inputs.mirostat_tau;
    params.n_ctx = inputs.max_context_length;
    params.n_batch = n_batch;
    params.n_threads = n_threads;
    params.n_threads_batch = n_blasthreads;
    bool stream_sse = inputs.stream_sse;

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

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

    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 (params.repeat_last_n < 1)
    {
        params.repeat_last_n = 1;
    }
    if (params.top_k < 1)
    {
        params.top_k = n_vocab; // all tokens in the vocabulary should be considered if top k is disabled
    }
    if (params.seed <= 0 || params.seed==0xFFFFFFFF)
    {
        params.seed = time(NULL);
    }

    // tokenize the prompt
    std::vector<int> embd_inp;
    std::vector<int> embd_inp_mem; //for storing added memory
    TokenizeString(params.prompt, embd_inp, file_format);
    if(addedmemory!="")
    {
        TokenizeString(addedmemory, embd_inp_mem, file_format);
    }

    //truncate to front of the prompt if its too long
    int32_t nctx = params.n_ctx;

    if (embd_inp.size() + params.n_predict > nctx)
    {
        //get bos token
        std::vector<int> bos;
        TokenizeString("", bos, file_format);
        int offset = embd_inp.size() - nctx + params.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];
        }
    }

    //added special memory, overwrite if needed
    if(addedmemory!="")
    {
        //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() + params.n_predict + 4 > nctx)
        {
            int offset = embd_inp_mem.size() - nctx + params.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() + params.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 = params.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 (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
    {
        ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, false, true);
    }
    else
    {
        bool triggersc = useSmartContext;
        if(useContextShift && (file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON))
        {
            PurgeMissingTokens(llama_ctx_v4, current_context_tokens, embd_inp, inputs.max_length, nctx);
            triggersc = false;
        }
        ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, triggersc, false);
        if(file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON)
        {
            llama_kv_cache_seq_rm(llama_ctx_v4, 0, n_past, -1);
        }
    }

    //if using BLAS and prompt is big enough, switch to single thread and use a huge batch
    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);
    bool blasmode = (approved_format && embd_inp.size() >= 32 && ggml_cpu_has_blas() && blasbatchsize!=-1);
    // bool blasmode = false;
    int original_batch = params.n_batch;
    int original_threads = params.n_threads;
    if (blasmode)
    {
        //for non llama, limit to 256
        int bbs = blasbatchsize;
        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_LLAMA && file_format!=FileFormat::GGUF_FALCON)
        {
            bbs = (blasbatchsize > 256 ? 256 : blasbatchsize);
        }

        params.n_batch = bbs; //received reports of 1024 and above crashing on some models
        if(!ggml_cpu_has_gpublas())
        {
            //does not limit here for gguf anymore. this is kept for older models.
            //new models will override threads inside decode fn.
            params.n_threads = 1;
            params.n_threads_batch = 1;
        }
        else
        {
            params.n_threads = n_blasthreads;
            params.n_threads_batch = n_blasthreads;
        }
    }

    current_context_tokens.resize(n_past);

    remaining_tokens = params.n_predict;
    stopper_unused_tokens = 0;
    int input_consumed = 0;
    std::mt19937 rng(params.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 use_scratch = true; //for normal inference always use scratch

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

    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!");
    }

    //prepare banned tokens
    if(banned_token_ids.size()==0 && banned_tokens.size()>0)
    {
        printf("\n[First Run] Banning %zu token sequences...",banned_tokens.size());
        for(int v=0;v<n_vocab;++v)
        {
            std::string word = FileFormatTokenizeID(v,file_format);
            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;
                }
            }
        }
        printf("\nBanned a total of %zu tokens.\n",banned_token_ids.size());
    }

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

    if (debugmode==1)
    {
        std::string outstr = "";
        printf("\n[Debug: Dump 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());
    }

    while (remaining_tokens > 0)
    {
        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, params.n_threads)==0);
            }
            else if(file_format == FileFormat::GGJT_3)
            {
                evalres = (llama_v3_eval(llama_ctx_v3, embd.data(), embdsize, n_past, params.n_threads)==0);
            }
            else if(file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON)
            {
                evalres = (llama_decode(llama_ctx_v4, llama_batch_get_one(embd.data(), embdsize, n_past, 0))==0);
            }
            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, params.n_threads, (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, params.n_threads, 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, params.n_threads, 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, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
            }
            else if(file_format==FileFormat::GPT2_4)
            {
                evalres = gpt2_eval(gpt2_ctx_v3, params.n_threads, n_past, embd, logits, mem_per_token, 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, params.n_threads, 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, params.n_threads, n_past, embd, logits, mem_per_token, use_scratch);
            }
            else if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2)
            {
                evalres = legacy_gptj_eval(gptj_ctx_v1, params.n_threads, 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, params.n_threads, n_past, embd, logits, mem_per_token);
            }
            else if(file_format==FileFormat::GPTJ_5)
            {
                evalres = gptj_eval(gptj_ctx_v3, params.n_threads, n_past, embd, logits, mem_per_token, use_scratch);
            }
            else if(file_format==FileFormat::MPT_1)
            {
                evalres = mpt_eval(mpt_ctx_v3, params.n_threads, n_past, embd, logits, false, mem_per_token, 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);
                snprintf(output.text, sizeof(output.text), "%s", "");
                output.status = 0;
                generation_finished = true;
                return output;
            }
        }

        n_past += embd.size();
        embd.clear();
        if ((int)embd_inp.size() <= input_consumed)
        {
            // out of user input, sample next token
            const float top_k = params.top_k;
            const float top_p = params.top_p;
            const float min_p = params.min_p;
            const float temp = params.temp;
            const float top_a = inputs.top_a;
            const float repeat_penalty = params.repeat_penalty;
            const float typical_p = params.typical_p;
            const float tfs_z = params.tfs_z;

            if (!startedsampling)
            {
                startedsampling = true;
                params.n_batch = original_batch;
                params.n_threads = original_threads;
                time1 = timer_check();
                timer_start();
                if(allow_regular_prints)
                {
                    printf("\n");
                }
            }

            unsigned int eosID = GetEosID(file_format, n_vocab);
            float * logitsPtr;
            float lowestLogit = 0;
            int btsize = banned_token_ids.size();
            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_LLAMA || file_format==FileFormat::GGUF_FALCON)
            {
                if(file_format == FileFormat::GGUF_LLAMA || file_format==FileFormat::GGUF_FALCON)
                {
                    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 = logits.data();
                lowestLogit = LowestLogit(logits);
            }

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

            id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty,
            top_k, top_a, top_p, min_p, typical_p, tfs_z, temp, rng,
            params.mirostat, params.mirostat_tau, params.mirostat_eta, sampler_order, grammar);

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

            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 id : embd)
            {
                std::string tokenizedstr = FileFormatTokenizeID(id, file_format);
                if(stream_sse)
                {
                    generated_tokens.push_back(tokenizedstr);
                }
                concat_output_mtx.lock();
                concat_output += tokenizedstr;
                concat_output_mtx.unlock();
            }

            if (startedsampling && allow_regular_prints)
            {
                printf("\rGenerating (%d / %d tokens)", (params.n_predict - remaining_tokens), params.n_predict);
            }
            if(debugmode==1 && top_picks.size()>0)
            {
                printf(" [");
                bool firstloop = true;
                for (auto & pick : top_picks)
                {
                    if (!firstloop)
                    {
                        printf(" ");
                    }
                    firstloop = false;
                    std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format);
                    ::utreplace(tokenizedstr, "\n", "\\n");
                    printf("(%s %.2f%%)", RemoveBell(tokenizedstr).c_str(), pick.p*100);
                }
                printf("]\n");
            }

            if(inputs.unban_tokens_rt && id==eosID)
            {
                stopper_unused_tokens = remaining_tokens;
                if(allow_regular_prints)
                {
                    printf("\n(EOS token triggered!)");
                }
                remaining_tokens = 0;
                last_stop_reason = stop_reason::EOS_TOKEN;
            }

            for (const auto &matched : stop_sequence)
            {
                if (concat_output.find(matched) != std::string::npos)
                {
                    stopper_unused_tokens = remaining_tokens;
                    remaining_tokens = 0;
                    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;
                }
            }
            fflush(stdout);
        }
        else
        {
            // some user input remains from prompt or interaction, forward it to processing
            while ((int)embd_inp.size() > input_consumed)
            {
                embd.push_back(embd_inp[input_consumed]);
                last_n_tokens.erase(last_n_tokens.begin());
                last_n_tokens.push_back(embd_inp[input_consumed]);
                current_context_tokens.push_back(embd_inp[input_consumed]);
                ++input_consumed;
                if ((int)embd.size() >= params.n_batch)
                {
                    break;
                }
            }
        }
    }
    time2 = timer_check();
    float pt1 = (time1*1000.0/(embd_inp.size()==0?1:embd_inp.size()));
    int realnpredict = params.n_predict-stopper_unused_tokens;
    float pt2 = (time2*1000.0/(realnpredict==0?1:realnpredict));
    float tokens_per_second = (realnpredict == 0 ? 0 : realnpredict / (time1 + time2));
    printf("\nContextLimit: %d/%d, Processing:%.2fs (%.1fms/T), Generation:%.2fs (%.1fms/T), Total:%.2fs (%.2fT/s)",current_context_tokens.size(),nctx, time1, pt1, time2, pt2, (time1 + time2), tokens_per_second);
    fflush(stdout);
    output.status = 1;
    generation_finished = true;
    last_eval_time = pt2;
    last_process_time = pt1;
    last_token_count = realnpredict;
    total_gens += 1;
    snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());

    return output;
}