Repository: ggerganov/llama.cpp
File: src/llama-arch.cpp (C++)

Code before:
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,                "%s.attention.head_count_kv"                },
    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,               "%s.attention.max_alibi_bias"               },
    { LLM_KV_ATTENTION_CLAMP_KQV,                    "%s.attention.clamp_kqv"                    },
    { LLM_KV_ATTENTION_KEY_LENGTH,                   "%s.attention.key_length"                   },
    { LLM_KV_ATTENTION_VALUE_LENGTH,                 "%s.attention.value_length"                 },
    { LLM_KV_ATTENTION_LAYERNORM_EPS,                "%s.attention.layer_norm_epsilon"           },
    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            "%s.attention.layer_norm_rms_epsilon"       },
    { LLM_KV_ATTENTION_GROUPNORM_EPS,                "%s.attention.group_norm_epsilon"           },
    { LLM_KV_ATTENTION_GROUPNORM_GROUPS,             "%s.attention.group_norm_groups"            },
    { LLM_KV_ATTENTION_CAUSAL,                       "%s.attention.causal"                       },
    { LLM_KV_ATTENTION_Q_LORA_RANK,                  "%s.attention.q_lora_rank"                  },
    { LLM_KV_ATTENTION_KV_LORA_RANK,                 "%s.attention.kv_lora_rank"                 },
    { LLM_KV_ATTENTION_DECAY_LORA_RANK,              "%s.attention.decay_lora_rank"              },
    { LLM_KV_ATTENTION_ICLR_LORA_RANK,               "%s.attention.iclr_lora_rank"               },
    { LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, "%s.attention.value_residual_mix_lora_rank" },
    { LLM_KV_ATTENTION_GATE_LORA_RANK,               "%s.attention.gate_lora_rank"               },
    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,       "%s.attention.relative_buckets_count"       },
    { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
    { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
    { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
    { LLM_KV_ATTENTION_TEMPERATURE_LENGTH,           "%s.attention.temperature_length"           },
    { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
    { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },


    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
    { LLM_KV_ROPE_DIMENSION_SECTIONS,       "%s.rope.dimension_sections"              },
    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
    { LLM_KV_ROPE_FREQ_BASE_SWA,            "%s.rope.freq_base_swa"                   },
    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
    { LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,  "%s.rope.scaling.yarn_ext_factor"         },
    { LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor"        },
    { LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   "%s.rope.scaling.yarn_beta_fast"          },
    { LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   "%s.rope.scaling.yarn_beta_slow"          },

    { LLM_KV_SPLIT_NO,            "split.no"            },
    { LLM_KV_SPLIT_COUNT,         "split.count"         },
    { LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },

    { LLM_KV_SSM_CONV_KERNEL,    "%s.ssm.conv_kernel"    },
    { LLM_KV_SSM_INNER_SIZE,     "%s.ssm.inner_size"     },
    { LLM_KV_SSM_STATE_SIZE,     "%s.ssm.state_size"     },
    { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
    { LLM_KV_SSM_GROUP_COUNT,    "%s.ssm.group_count"    },
    { LLM_KV_SSM_DT_B_C_RMS,     "%s.ssm.dt_b_c_rms"     },

Review comment (suggestion):
```suggestion
    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
```

Code after:
    { LLM_KV_EXPERT_USED_COUNT,                 "%s.expert_used_count"                 },
    { LLM_KV_EXPERT_SHARED_COUNT,               "%s.expert_shared_count"               },
    { LLM_KV_EXPERT_GROUP_COUNT,                "%s.expert_group_count"                },
    { LLM_KV_EXPERT_GROUP_USED_COUNT,           "%s.expert_group_used_count"           },
    { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
    { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
    { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
    { LLM_KV_EXPERT_GROUP_SCALE,                "%s.expert_group_scale"                },
    { LLM_KV_EXPERTS_PER_GROUP,                 "%s.experts_per_group"                 },
    { LLM_KV_MOE_EVERY_N_LAYERS,                "%s.moe_every_n_layers"                },
    { LLM_KV_NEXTN_PREDICT_LAYERS,              "%s.nextn_predict_layers"              },
    { LLM_KV_NUM_DEEPSTACK_LAYERS,              "%s.n_deepstack_layers"                },
    { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
    { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
    { LLM_KV_DECODER_BLOCK_COUNT,               "%s.decoder_block_count"               },
    { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
    { LLM_KV_ROUTER_LOGIT_SOFTCAPPING,          "%s.router_logit_softcapping"          },
    { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
    { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },
    { LLM_KV_RESCALE_EVERY_N_LAYERS,            "%s.rescale_every_n_layers"            },
    { LLM_KV_TIME_MIX_EXTRA_DIM,                "%s.time_mix_extra_dim"                },
    { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
    { LLM_KV_RESIDUAL_SCALE,                    "%s.residual_scale"                    },
    { LLM_KV_EMBEDDING_SCALE,                   "%s.embedding_scale"                   },
    { LLM_KV_TOKEN_SHIFT_COUNT,                 "%s.token_shift_count"                 },
    { LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         "%s.interleave_moe_layer_step"         },

    { LLM_KV_ATTENTION_HEAD_COUNT,                   "%s.attention.head_count"                   },
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,                "%s.attention.head_count_kv"                },
    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,               "%s.attention.max_alibi_bias"               },
    { LLM_KV_ATTENTION_CLAMP_KQV,                    "%s.attention.clamp_kqv"                    },
    { LLM_KV_ATTENTION_KEY_LENGTH,                   "%s.attention.key_length"                   },
    { LLM_KV_ATTENTION_VALUE_LENGTH,                 "%s.attention.value_length"                 },
    { LLM_KV_ATTENTION_LAYERNORM_EPS,                "%s.attention.layer_norm_epsilon"           },
    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            "%s.attention.layer_norm_rms_epsilon"       },
    { LLM_KV_ATTENTION_GROUPNORM_EPS,                "%s.attention.group_norm_epsilon"           },
    { LLM_KV_ATTENTION_GROUPNORM_GROUPS,             "%s.attention.group_norm_groups"            },
    { LLM_KV_ATTENTION_CAUSAL,                       "%s.attention.causal"                       },
    { LLM_KV_ATTENTION_Q_LORA_RANK,                  "%s.attention.q_lora_rank"                  },
    { LLM_KV_ATTENTION_KV_LORA_RANK,                 "%s.attention.kv_lora_rank"                 },
    { LLM_KV_ATTENTION_DECAY_LORA_RANK,              "%s.attention.decay_lora_rank"              },
    { LLM_KV_ATTENTION_ICLR_LORA_RANK,               "%s.attention.iclr_lora_rank"               },
    { LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, "%s.attention.value_residual_mix_lora_rank" },
    { LLM_KV_ATTENTION_GATE_LORA_RANK,               "%s.attention.gate_lora_rank"               },
    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,       "%s.attention.relative_buckets_count"       },
    { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
    { LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,       "%s.attention.sliding_window_pattern"       },
    { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
    { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
    { LLM_KV_ATTENTION_TEMPERATURE_LENGTH,           "%s.attention.temperature_length"           },