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llama.cpp
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model : clean up and fix EXAONE-MoE configuration (#18840) 

* Fix mismatch of EXAONE-MoE configuration

* ensure gating func is set, cleanup

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Junwon Hwang 2026-01-15 03:38:21 +09:00 committed by GitHub
parent 516a4ca9b5
commit 8fb7175576
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2 changed files with 3 additions and 11 deletions
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6
convert_hf_to_gguf.py
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@ -8772,11 +8772,7 @@ class ExaoneMoEModel(Exaone4Model):
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
# For here, we hard-code the number of NextN/MTP layers to 1 for K-EXAONE,
# so that we can convert MTP weights to GGUF format for speculative decoding.
# This is because HF config of K-EXAONE does not have `num_nextn_predict_layers` at now.
# Will be updated when HF config is updated.
self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 1))
self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

8
src/llama-model.cpp
View File

@ -1942,16 +1942,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, true);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false);
ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);