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

Code before:
    float f_attention_scale = 0.0f;

    bool causal_attn   = true;
    bool use_alibi     = false;
    bool attn_soft_cap = false;
    bool use_kq_norm   = true;

    // for Classifiers
    uint32_t n_cls_out = 1;

    // llama4 smallthinker
    uint32_t n_moe_layer_step        = 0;
    uint32_t n_no_rope_layer_step    = 4;
    uint32_t n_attn_temp_floor_scale = 8192;
    float    f_attn_temp_scale       = 0.1;

    // gemma3n altup
    uint32_t n_altup      = 4; // altup_num_inputs
    uint32_t i_altup_act  = 0; // altup_active_idx
    uint32_t laurel_rank  = 64;
    uint32_t n_embd_altup = 256;

    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
    uint32_t    n_dec_layer = 0;

    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;

    // this value n_pattern means that every nth layer is dense (i.e. non-SWA)
    // dense_first means whether the pattern is start with a dense layer
    // note that if n_pattern == 0, all layers are SWA
    //           if n_pattern == 1, all layers are dense
    // example 1: n_pattern = 3, dense_first = false
    //   il == 0: swa
    //   il == 1: swa
    //   il == 2: dense
    //   il == 3: swa
    //   il == 4: swa
    //   il == 5: dense
    //   il == 6: swa
    //   etc ...
    // example 2: n_pattern = 2, dense_first = true
    //   il == 0: dense
    //   il == 1: swa
    //   il == 2: dense
    //   il == 3: swa
    //   etc ...
    void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);

Review comment (suggestion):
```suggestion
    uint32_t    n_dec_layer        = 0;
```

Code after:
    float f_attention_scale = 0.0f;

    bool causal_attn   = true;
    bool use_alibi     = false;
    bool attn_soft_cap = false;
    bool use_kq_norm   = true;

    // for Classifiers
    uint32_t n_cls_out = 1;

    // llama4 smallthinker
    uint32_t n_moe_layer_step        = 0;
    uint32_t n_no_rope_layer_step    = 4;
    uint32_t n_attn_temp_floor_scale = 8192;
    float    f_attn_temp_scale       = 0.1;

    // gemma3n altup
    uint32_t n_altup      = 4; // altup_num_inputs
    uint32_t i_altup_act  = 0; // altup_active_idx
    uint32_t laurel_rank  = 64;
    uint32_t n_embd_altup = 256;

    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
    uint32_t    dec_n_layer        = 0;

    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;

    // this value n_pattern means that every nth layer is dense (i.e. non-SWA)
    // dense_first means whether the pattern is start with a dense layer
    // note that if n_pattern == 0, all layers are SWA
    //           if n_pattern == 1, all layers are dense
    // example 1: n_pattern = 3, dense_first = false
    //   il == 0: swa
    //   il == 1: swa
    //   il == 2: dense
    //   il == 3: swa
    //   il == 4: swa
    //   il == 5: dense
    //   il == 6: swa
    //   etc ...
    // example 2: n_pattern = 2, dense_first = true
    //   il == 0: dense
    //   il == 1: swa
    //   il == 2: dense
    //   il == 3: swa
    //   etc ...
    void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);