#include "llama-model-saver.h"

#include "ggml.h"

#include "gguf.h"

#include "llama-arch.h"

#include "llama.h"

#include "llama-hparams.h"

#include "llama-model.h"

#include "llama-vocab.h"

#include <cstdint>

#include <string>

bool llama_model_saver_supports_arch(llm_arch arch) {

    switch (arch) {

        case LLM_ARCH_PLAMO3:

        case LLM_ARCH_GEMMA3:

        case LLM_ARCH_GEMMA3N:

        case LLM_ARCH_COHERE2:

        case LLM_ARCH_COHERE2MOE:

        case LLM_ARCH_OLMO2:

        case LLM_ARCH_BITNET:

        case LLM_ARCH_T5:

        case LLM_ARCH_EXAONE_MOE:

        case LLM_ARCH_AFMOE:

        case LLM_ARCH_APERTUS:

        case LLM_ARCH_MIMO2:

        case LLM_ARCH_STEP35:

        case LLM_ARCH_MELLUM:

            return false;

        default:

            return true;

    }

}

llama_model_saver::llama_model_saver(const struct llama_model * model) :

        gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {

    GGML_ASSERT(llama_model_saver_supports_arch(model->arch));

}

llama_model_saver::llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx) :

        gguf_ctx(gguf_ctx == nullptr ? gguf_init_empty() : gguf_ctx), gguf_ctx_owned(gguf_ctx == nullptr), model(nullptr), llm_kv(arch) {}

llama_model_saver::~llama_model_saver() {

    if (gguf_ctx_owned) {

        gguf_free(gguf_ctx);

    }

}

void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) {

    gguf_set_val_u32(gguf_ctx, llm_kv(key).c_str(), value);

}

void llama_model_saver::add_kv(const enum llm_kv key, const int32_t value) {

    gguf_set_val_i32(gguf_ctx, llm_kv(key).c_str(), value);

}

void llama_model_saver::add_kv(const enum llm_kv key, const float value) {

    gguf_set_val_f32(gguf_ctx, llm_kv(key).c_str(), value);

}

void llama_model_saver::add_kv(const enum llm_kv key, const bool value) {

    gguf_set_val_bool(gguf_ctx, llm_kv(key).c_str(), value);

}

void llama_model_saver::add_kv(const enum llm_kv key, const char * value) {

    gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), value);

}

[[noreturn]]

void llama_model_saver::add_kv(const enum llm_kv key, const char value) {

    GGML_UNUSED(key);

    GGML_UNUSED(value);

    GGML_ABORT("fatal error"); // this should never be called, only needed to make the template below compile

}

template <typename Container>

void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) {

    GGML_ASSERT(model != nullptr || !per_layer);

    const size_t n_values = per_layer ? size_t(model->hparams.n_layer()) : value.size();

    GGML_ASSERT(n_values <= value.size());

    if (n_values == 0) {

        return;

    }

    if (per_layer) {

        bool all_values_the_same = true;

        for (size_t i = 1; i < n_values; ++i) {

            if (value[i] != value[0]) {

                all_values_the_same = false;

                break;

            }

        }

        if (all_values_the_same) {

            add_kv(key, value[0]);

            return;

        }

    }

    if (std::is_same<typename Container::value_type, uint8_t>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT8, value.data(), n_values);

    } else if (std::is_same<typename Container::value_type, int8_t>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT8, value.data(), n_values);

    } else if (std::is_same<typename Container::value_type, uint32_t>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT32, value.data(), n_values);

    } else if (std::is_same<typename Container::value_type, bool>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_BOOL, value.data(), n_values);

    } else if (std::is_same<typename Container::value_type, int32_t>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT32, value.data(), n_values);

    } else if (std::is_same<typename Container::value_type, float>::value) {

        gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_FLOAT32, value.data(), n_values);

    } else if (std::is_same<Container, std::string>::value) {

        gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), reinterpret_cast<const char *>(value.data()));

    } else {

        GGML_ABORT("fatal error");

    }

}

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::vector<unsigned int, std::__1::allocator<unsigned int> > >(llm_kv, std::__1::vector<unsigned int, std::__1::allocator<unsigned int> > const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >(llm_kv, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::array<unsigned int, 512ul> >(llm_kv, std::__1::array<unsigned int, 512ul> const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::array<float, 512ul> >(llm_kv, std::__1::array<float, 512ul> const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::array<int, 512ul> >(llm_kv, std::__1::array<int, 512ul> const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::array<int, 4ul> >(llm_kv, std::__1::array<int, 4ul> const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::vector<int, std::__1::allocator<int> > >(llm_kv, std::__1::vector<int, std::__1::allocator<int> > const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::vector<float, std::__1::allocator<float> > >(llm_kv, std::__1::vector<float, std::__1::allocator<float> > const&, bool)

Unexecuted instantiation: void llama_model_saver::add_kv<std::__1::vector<char, std::__1::allocator<char> > >(llm_kv, std::__1::vector<char, std::__1::allocator<char> > const&, bool)

// instantiate for external usage:

template void llama_model_saver::add_kv<std::vector<uint32_t>>(const enum llm_kv, const std::vector<uint32_t> &, const bool);

void llama_model_saver::add_kv(const enum llm_kv key, const std::vector<std::string> & value) {

    std::vector<const char *> tmp(value.size());

    for (size_t i = 0; i < value.size(); ++i) {

        tmp[i] = value[i].c_str();

    }

    gguf_set_arr_str(gguf_ctx, llm_kv(key).c_str(), tmp.data(), tmp.size());

}

void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {

    if (!tensor) {

        return;

    }

    if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) {

        const std::string tensor_name = tensor->name;

        GGML_ASSERT(

            tensor_name == "rope_freqs.weight" || tensor_name == "rope_factors_long.weight" ||

            tensor_name == "rope_factors_short.weight"); // FIXME

        return;

    }

    gguf_add_tensor(gguf_ctx, tensor);

}

void llama_model_saver::add_kv_from_model() {

    const llama_hparams & hparams = model->hparams;

    const llama_vocab   & vocab   = model->vocab;

    const int32_t n_vocab = vocab.n_tokens();

    std::vector<std::string> tokens(n_vocab);

    std::vector<float>       scores(n_vocab);

    std::vector<int32_t>     token_types(n_vocab);

    if (vocab.get_type() != LLAMA_VOCAB_TYPE_NONE) {

        for (int32_t id = 0; id < n_vocab; ++id) {

            const llama_vocab::token_data & token_data = vocab.get_token_data(id);

            tokens[id] = token_data.text;

            scores[id] = token_data.score;

            // FIXME should this be treated as flags?

            switch(token_data.attr) {

                case LLAMA_TOKEN_ATTR_UNKNOWN:      token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN;      break;

                case LLAMA_TOKEN_ATTR_UNUSED:       token_types[id] = LLAMA_TOKEN_TYPE_UNUSED;       break;

                case LLAMA_TOKEN_ATTR_NORMAL:       token_types[id] = LLAMA_TOKEN_TYPE_NORMAL;       break;

                case LLAMA_TOKEN_ATTR_CONTROL:      token_types[id] = LLAMA_TOKEN_TYPE_CONTROL;      break;

                case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;

                case LLAMA_TOKEN_ATTR_BYTE:         token_types[id] = LLAMA_TOKEN_TYPE_BYTE;         break;

                // case LLAMA_TOKEN_ATTR_NORMALIZED:   ???

                // case LLAMA_TOKEN_ATTR_LSTRIP:       ???

                // case LLAMA_TOKEN_ATTR_RSTRIP:       ???

                case LLAMA_TOKEN_ATTR_UNDEFINED:

                default:                            token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED;    break;

            }

        }

    }

    // add_kv(LLM_KV_GENERAL_TYPE,                      ???);

    add_kv(LLM_KV_GENERAL_ARCHITECTURE,              model->arch_name());

    // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION,      ???);

    // add_kv(LLM_KV_GENERAL_ALIGNMENT,                 ???);

    // add_kv(LLM_KV_GENERAL_FILE_TYPE,                 ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_SEQUENCE,         ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_TOP_K,            ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_TOP_P,            ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_MIN_P,            ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_XTC_PROBABILITY,  ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_XTC_THRESHOLD,    ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_TEMP,             ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_LAST_N,   ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_REPEAT,   ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT,         ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_TAU,     ???);

    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_ETA,     ???);

    add_kv(LLM_KV_GENERAL_NAME,                      model->name);

    // add_kv(LLM_KV_GENERAL_AUTHOR,                    ???);

    // add_kv(LLM_KV_GENERAL_VERSION,                   ???);

    // add_kv(LLM_KV_GENERAL_URL,                       ???);

    // add_kv(LLM_KV_GENERAL_DESCRIPTION,               ???);

    // add_kv(LLM_KV_GENERAL_LICENSE,                   ???);

    // add_kv(LLM_KV_GENERAL_SOURCE_URL,                ???);

    // add_kv(LLM_KV_GENERAL_SOURCE_HF_REPO,            ???);

    add_kv(LLM_KV_VOCAB_SIZE,                        vocab.n_tokens());

    add_kv(LLM_KV_CONTEXT_LENGTH,                    hparams.n_ctx_train);

    add_kv(LLM_KV_EMBEDDING_LENGTH,                  hparams.n_embd);

    if (hparams.n_embd_out_impl > 0) {

        add_kv(LLM_KV_EMBEDDING_LENGTH_OUT,          hparams.n_embd_out_impl);

    }

    add_kv(LLM_KV_BLOCK_COUNT,                       hparams.n_layer_all);

    add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);

    add_kv(LLM_KV_FEED_FORWARD_LENGTH,               hparams.n_ff_arr, true);

    add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);

    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);

    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_chexp);

    add_kv(LLM_KV_SWIGLU_CLAMP_EXP,                  hparams.swiglu_clamp_exp);

    add_kv(LLM_KV_SWIGLU_CLAMP_SHEXP,                hparams.swiglu_clamp_shexp);

    add_kv(LLM_KV_USE_PARALLEL_RESIDUAL,             hparams.use_par_res);

    // add_kv(LLM_KV_TENSOR_DATA_LAYOUT,                ???);

    add_kv(LLM_KV_EXPERT_COUNT,                      hparams.n_expert);

    add_kv(LLM_KV_EXPERT_USED_COUNT,                 hparams.n_expert_used);

    add_kv(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);

    add_kv(LLM_KV_EXPERT_GROUP_COUNT,                hparams.n_expert_groups);

    add_kv(LLM_KV_EXPERT_GROUP_USED_COUNT,           hparams.n_group_used);

    add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale);

    add_kv(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm);

    add_kv(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);

    add_kv(LLM_KV_EXPERT_GROUP_SCALE,                hparams.expert_group_scale);

    add_kv(LLM_KV_EXPERTS_PER_GROUP,                 hparams.n_group_experts);

    add_kv(LLM_KV_MOE_EVERY_N_LAYERS,                hparams.moe_every_n_layers);

    add_kv(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.n_layer_nextn);

    add_kv(LLM_KV_NUM_DEEPSTACK_LAYERS,              hparams.n_deepstack_layers);

    add_kv(LLM_KV_DEEPSTACK_MAPPING,                 hparams.deepstack_mapping_arr);

    add_kv(LLM_KV_POOLING_TYPE,                      uint32_t(hparams.pooling_type));

    add_kv(LLM_KV_LOGIT_SCALE,                       hparams.f_logit_scale);

    add_kv(LLM_KV_DECODER_START_TOKEN_ID,            hparams.dec_start_token_id);

    add_kv(LLM_KV_DECODER_BLOCK_COUNT,               hparams.dec_n_layer);

    add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING,            hparams.f_attn_logit_softcapping);

    add_kv(LLM_KV_ROUTER_LOGIT_SOFTCAPPING,          hparams.f_router_logit_softcapping);

    add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING,           hparams.f_final_logit_softcapping);

    add_kv(LLM_KV_SWIN_NORM,                         hparams.swin_norm);

    add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS,            hparams.rescale_every_n_layers);

    add_kv(LLM_KV_TIME_MIX_EXTRA_DIM,                hparams.time_mix_extra_dim);

    add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM,              hparams.time_decay_extra_dim);

    add_kv(LLM_KV_RESIDUAL_SCALE,                    hparams.f_residual_scale);

    add_kv(LLM_KV_EMBEDDING_SCALE,                   hparams.f_embedding_scale);

    add_kv(LLM_KV_TOKEN_SHIFT_COUNT,                 hparams.token_shift_count);

    add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);

    // add_kv(LLM_KV_FULL_ATTENTION_INTERVAL,           ???); // saved as LLM_KV_ATTENTION_RECURRENT_LAYERS instead

    add_kv(LLM_KV_ATTENTION_HEAD_COUNT,              hparams.n_head_arr, true);

    add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV,           hparams.n_head_kv_arr, true);

    add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS,          hparams.f_max_alibi_bias);

    add_kv(LLM_KV_ATTENTION_CLAMP_KQV,               hparams.f_clamp_kqv);

    add_kv(LLM_KV_ATTENTION_KEY_LENGTH,              hparams.n_embd_head_k_full);

    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,            hparams.n_embd_head_v_full);

    add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS,           hparams.f_norm_eps);

    add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);

    add_kv(LLM_KV_ATTENTION_GROUPNORM_EPS,           hparams.f_norm_group_eps);

    add_kv(LLM_KV_ATTENTION_GROUPNORM_GROUPS,        hparams.n_norm_groups);

    add_kv(LLM_KV_ATTENTION_CAUSAL,                  hparams.causal_attn);

    add_kv(LLM_KV_ATTENTION_Q_LORA_RANK,             hparams.n_lora_q);

    add_kv(LLM_KV_ATTENTION_KV_LORA_RANK,            hparams.n_lora_kv);

    add_kv(LLM_KV_ATTENTION_DECAY_LORA_RANK,         hparams.n_lora_decay);

    add_kv(LLM_KV_ATTENTION_ICLR_LORA_RANK,          hparams.n_lora_iclr);

    add_kv(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);

    add_kv(LLM_KV_ATTENTION_GATE_LORA_RANK,          hparams.n_lora_gate);

    add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,  hparams.n_rel_attn_bkts);

    add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);

    // add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,  ???);

    add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);

    add_kv(LLM_KV_ATTENTION_OUTPUT_SCALE,            hparams.f_attn_out_scale);

    add_kv(LLM_KV_ATTENTION_VALUE_SCALE,             hparams.f_attn_value_scale);

    add_kv(LLM_KV_ATTENTION_TEMPERATURE_LENGTH,      hparams.attn_temp_length);

    add_kv(LLM_KV_ATTENTION_TEMPERATURE_SCALE,       hparams.f_attn_temp_scale);

    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA,          hparams.n_embd_head_k_mla_impl);

    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,        hparams.n_embd_head_v_mla_impl);

    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,          hparams.n_embd_head_k_swa);

    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,        hparams.n_embd_head_v_swa);

    add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,      hparams.indexer_n_head);

    add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,      hparams.indexer_head_size);

    add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K,           hparams.indexer_top_k);

    add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS,        hparams.is_recr_impl, true);

    const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;

    add_kv(LLM_KV_ROPE_DIMENSION_COUNT,              hparams.n_rot_full);

    add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA,          hparams.n_rot_swa);

    add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS,           hparams.rope_sections);

    add_kv(LLM_KV_ROPE_FREQ_BASE,                    hparams.rope_freq_base_train);

    add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,                hparams.rope_freq_base_train_swa);

    // add_kv(LLM_KV_ROPE_SCALE_LINEAR,                 rope_scaling_factor); // old name

    add_kv(LLM_KV_ROPE_SCALING_TYPE,                 llama_rope_scaling_type_name(hparams.rope_scaling_type_train));

    add_kv(LLM_KV_ROPE_SCALING_FACTOR,               rope_scaling_factor);

    add_kv(LLM_KV_ROPE_SCALING_ATTN_FACTOR,          hparams.rope_attn_factor);

    add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,         hparams.n_ctx_orig_yarn);

    add_kv(LLM_KV_ROPE_SCALING_FINETUNED,            hparams.rope_finetuned);

    add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,         hparams.rope_yarn_log_mul);

    add_kv(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,      hparams.yarn_ext_factor);

    add_kv(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,     hparams.yarn_attn_factor);

    add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,       hparams.yarn_beta_fast);

    add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,       hparams.yarn_beta_slow);

    // TODO: implement split file support

    // add_kv(LLM_KV_SPLIT_NO,                          ???);

    // add_kv(LLM_KV_SPLIT_COUNT,                       ???);

    // add_kv(LLM_KV_SPLIT_TENSORS_COUNT,               ???);

    add_kv(LLM_KV_SSM_INNER_SIZE,                    hparams.ssm_d_inner);

    add_kv(LLM_KV_SSM_CONV_KERNEL,                   hparams.ssm_d_conv);

    add_kv(LLM_KV_SSM_STATE_SIZE,                    hparams.ssm_d_state);

    add_kv(LLM_KV_SSM_TIME_STEP_RANK,                hparams.ssm_dt_rank);

    add_kv(LLM_KV_SSM_GROUP_COUNT,                   hparams.ssm_n_group);

    add_kv(LLM_KV_SSM_DT_B_C_RMS,                    hparams.ssm_dt_b_c_rms);

    add_kv(LLM_KV_KDA_HEAD_DIM,                      hparams.n_embd_head_kda);

    add_kv(LLM_KV_WKV_HEAD_SIZE,                     hparams.wkv_head_size);

    add_kv(LLM_KV_TOKENIZER_MODEL,                   vocab.get_tokenizer_model());

    add_kv(LLM_KV_TOKENIZER_PRE,                     vocab.get_tokenizer_pre());

    add_kv(LLM_KV_TOKENIZER_LIST,                    tokens);

    add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE,              token_types);

    add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,        vocab.n_token_types());

    add_kv(LLM_KV_TOKENIZER_SCORES,                  scores);

    add_kv(LLM_KV_TOKENIZER_MERGES,                  vocab.get_bpe_merges());

    // FIXME llama_token is type i32 but when reading in a GGUF file u32 is expected, not an issue for writing though

    add_kv(LLM_KV_TOKENIZER_BOS_ID,                  uint32_t(vocab.token_bos()));

    add_kv(LLM_KV_TOKENIZER_EOS_ID,                  uint32_t(vocab.token_eos()));

    add_kv(LLM_KV_TOKENIZER_EOT_ID,                  uint32_t(vocab.token_eot()));

    add_kv(LLM_KV_TOKENIZER_EOM_ID,                  uint32_t(vocab.token_eom()));

    add_kv(LLM_KV_TOKENIZER_UNK_ID,                  uint32_t(vocab.token_unk()));

    add_kv(LLM_KV_TOKENIZER_SEP_ID,                  uint32_t(vocab.token_sep()));

    add_kv(LLM_KV_TOKENIZER_PAD_ID,                  uint32_t(vocab.token_pad()));

    // add_kv(LLM_KV_TOKENIZER_CLS_ID,                  uint32_t(vocab.token_bos())); // deprecated

    // add_kv(LLM_KV_TOKENIZER_MASK_ID,                 ???);

    add_kv(LLM_KV_TOKENIZER_ADD_BOS,                 vocab.get_add_bos());

    add_kv(LLM_KV_TOKENIZER_ADD_EOS,                 vocab.get_add_eos());

    add_kv(LLM_KV_TOKENIZER_ADD_SEP,                 vocab.get_add_sep());

    add_kv(LLM_KV_TOKENIZER_ADD_PREFIX,              vocab.get_add_space_prefix());

    add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,         vocab.get_remove_extra_whitespaces());

    add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,    vocab.get_precompiled_charsmap());

    // add_kv(LLM_KV_TOKENIZER_HF_JSON,                 ???);

    // add_kv(LLM_KV_TOKENIZER_RWKV,                    ???);

    add_kv(LLM_KV_TOKENIZER_FIM_PRE_ID,              uint32_t(vocab.token_fim_pre()));

    add_kv(LLM_KV_TOKENIZER_FIM_SUF_ID,              uint32_t(vocab.token_fim_suf()));

    add_kv(LLM_KV_TOKENIZER_FIM_MID_ID,              uint32_t(vocab.token_fim_mid()));

    add_kv(LLM_KV_TOKENIZER_FIM_PAD_ID,              uint32_t(vocab.token_fim_pad()));

    add_kv(LLM_KV_TOKENIZER_FIM_REP_ID,              uint32_t(vocab.token_fim_rep()));

    add_kv(LLM_KV_TOKENIZER_FIM_SEP_ID,              uint32_t(vocab.token_fim_sep()));

    // TODO: implement LoRA support

    // add_kv(LLM_KV_ADAPTER_TYPE,                      ???);

    // add_kv(LLM_KV_ADAPTER_LORA_ALPHA,                ???);

    // add_kv(LLM_KV_ADAPTER_LORA_TASK_NAME,            ???);

    // add_kv(LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,        ???);

    // add_kv(LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS,   ???);

    add_kv(LLM_KV_POSNET_EMBEDDING_LENGTH,           hparams.posnet.n_embd);

    add_kv(LLM_KV_POSNET_BLOCK_COUNT,                hparams.posnet.n_layer);

    add_kv(LLM_KV_CONVNEXT_EMBEDDING_LENGTH,         hparams.convnext.n_embd);

    add_kv(LLM_KV_CONVNEXT_BLOCK_COUNT,              hparams.convnext.n_layer);

    add_kv(LLM_KV_CLASSIFIER_OUTPUT_LABELS,          model->classifier_labels);

    add_kv(LLM_KV_SHORTCONV_L_CACHE,                 hparams.n_shortconv_l_cache);

    add_kv(LLM_KV_XIELU_ALPHA_N,                     hparams.xielu_alpha_n);

    add_kv(LLM_KV_XIELU_ALPHA_P,                     hparams.xielu_alpha_p);

    add_kv(LLM_KV_XIELU_BETA,                        hparams.xielu_beta);

    add_kv(LLM_KV_XIELU_EPS,                         hparams.xielu_eps);

    // deprecated

    // add_kv(LLM_KV_TOKENIZER_PREFIX_ID,               ???);

    // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID,               ???);

    // add_kv(LLM_KV_TOKENIZER_MIDDLE_ID,               ???);

    add_kv(LLM_KV_DENSE_2_FEAT_IN,                   hparams.dense_2_feat_in);

    add_kv(LLM_KV_DENSE_2_FEAT_OUT,                  hparams.dense_2_feat_out);

    add_kv(LLM_KV_DENSE_3_FEAT_IN,                   hparams.dense_3_feat_in);

    add_kv(LLM_KV_DENSE_3_FEAT_OUT,                  hparams.dense_3_feat_out);

}

void llama_model_saver::add_tensors_from_model() {

    if (model->output != nullptr &&

            std::string(model->output->name) != std::string(model->tok_embd->name)) {

        add_tensor(model->tok_embd); // some models use the same tensor for tok_embd and output

    }

    add_tensor(model->type_embd);

    add_tensor(model->pos_embd);

    add_tensor(model->tok_norm);

    add_tensor(model->tok_norm_b);

    add_tensor(model->output_norm);

    add_tensor(model->output_norm_b);

    add_tensor(model->output);

    add_tensor(model->output_b);

    add_tensor(model->output_norm_enc);

    add_tensor(model->output_s);

    add_tensor(model->output_in_s);

    add_tensor(model->cls);

    add_tensor(model->cls_b);

    add_tensor(model->cls_out);

    add_tensor(model->cls_out_b);

    add_tensor(model->cls_norm);

    for (const struct llama_layer & layer : model->layers) {

        for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {

            add_tensor(reinterpret_cast<const struct ggml_tensor * const *>(&layer)[i]);

        }

    }

}

void llama_model_saver::save(const std::string & path_model) {

    gguf_write_to_file(gguf_ctx, path_model.c_str(), false);

}

void llama_model_saver::save(FILE * file) {

    gguf_write_to_file_ptr(gguf_ctx, file, false);

}