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hparams : refactor hparams.n_layer (#24060)

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* hparams : refactor hparams.n_layer

* cont : remove `n_layer_kv()`, use n_layer_all instead

* cont : type consistency

* pi : update SYSTEM.md

* models : fix Step3.5 MTP

* cont : remove duplicate switch cases

* cont : explicitly set `false` to extra layers for `is_swa` and `is_recr`

* cont : fix nextn layer count handling

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Georgi Gerganov
2026-06-05 11:09:36 +03:00
committed by GitHub
parent 3ecfb150a4
commit 7acb4e8cd2
129 changed files with 412 additions and 431 deletions
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src/models/kimi-linear.cpp
+2 -2
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@@ -14,7 +14,7 @@ void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) {
// Mark KDA layers as recurrent using n_head_kv pattern (like Jamba)
// Set n_head_kv = 0 for KDA layers (recurrent), n_head_kv = n_head for MLA layers (attention)
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0; // KDA layers are recurrent
}
@@ -25,7 +25,7 @@ void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
switch (hparams.n_layer) {
switch (hparams.n_layer()) {
case 27: type = LLM_TYPE_48B_A3B; break; // Kimi-Linear-48B-A3B
default: type = LLM_TYPE_UNKNOWN;
}