#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_MUSE_GLIMMER:
case LLM_ARCH_MELLUM:
case LLM_ARCH_LAGUNA:
case LLM_ARCH_GRANITE_SWA:
case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config
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");
}
}
// 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);
template void llama_model_saver::add_kv<std::vector<float>>(const enum llm_kv, const std::vector<float> &, 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_LATENT_LENGTH, hparams.n_expert_latent);
add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
add_kv(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH, hparams.n_ff_chexp);
add_kv(LLM_KV_SWIGLU_CLAMP_EXP, std::vector<float>(
hparams.swiglu_clamp_exp.begin(), hparams.swiglu_clamp_exp.begin() + hparams.n_layer_all));
add_kv(LLM_KV_SWIGLU_CLAMP_SHEXP, std::vector<float>(
hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.begin() + hparams.n_layer_all));
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_ROPE_PATTERN, hparams.rope_pattern, true);
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_INDEXER_BLOCK_SIZE, hparams.indexer_block_size);
add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks);
add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, true);
add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, true);
add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, hparams.dsv4_o_group_count);
add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK, hparams.dsv4_o_lora_rank);
add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, hparams.dsv4_compress_rope_base);
if (model->arch == LLM_ARCH_DEEPSEEK4 || hparams.dsv4_hc_mult > 0) {
// the loader requires one compress ratio per layer, including nextn layers
const std::vector<uint32_t> compress_ratios(
hparams.dsv4_compress_ratios.begin(), hparams.dsv4_compress_ratios.begin() + hparams.n_layer_all);
add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, compress_ratios);
} else {
add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, true);
}
add_kv(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult);
add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
add_kv(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps);
add_kv(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count);
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_KDA_SAFE_GATE, hparams.kda_safe_gate);
add_kv(LLM_KV_KDA_GATE_LOWER_BOUND, hparams.kda_gate_lower_bound);
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);
add_kv(LLM_KV_ATTN_RES_BLOCK_SIZE, hparams.attn_res_block_size);
add_kv(LLM_KV_ACTIVATION_SITU_BETA, hparams.situ_beta);
add_kv(LLM_KV_ACTIVATION_SITU_LINEAR_BETA, hparams.situ_linear_beta);
// 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->output_res_score);
add_tensor(model->nextn_proj_pre);
add_tensor(model->nextn_proj_post);
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);
add_tensor(model->hc_head_fn);
add_tensor(model->hc_head_base);
add_tensor(model->hc_head_scale);
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);
}