convert : Mistral format yarn apply_scale support (#22612)

* [BUGFIX] Mistral format apply_scale support.

* Update convert_hf_to_gguf.py

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

* fix misunderstood boolean parameters

---------

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

convert_hf_to_gguf.py

@@ -2889,6 +2889,20 @@ class LlamaModel(TextModel):
                 .swapaxes(1, 2)
                 .reshape(weights.shape))
 
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        # Mirror the BF16 Q/K RoPE permutation site in modify_tensors; the NVFP4 path bypasses it.
+        if self.undo_permute:
+            n_head = self.find_hparam(["n_heads", "num_attention_heads"], optional=True)
+            n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"], optional=True)
+            if n_head is not None:
+                if name.endswith("q_proj.weight"):
+                    weight = LlamaModel.permute(weight, n_head, n_head)
+                    scale  = LlamaModel.permute(scale, n_head, n_head)
+                elif name.endswith("k_proj.weight"):
+                    weight = LlamaModel.permute(weight, n_head, n_kv_head)
+                    scale  = LlamaModel.permute(scale, n_head, n_kv_head)
+        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
+
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
@@ -12702,11 +12716,12 @@ class MistralModel(LlamaModel):
     def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict):
         if "yarn" in hparams:
             yarn_params = hparams["yarn"]
+            mscale_all_dim = 1.0 if not yarn_params["apply_scale"] else 0.0
             gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
             gguf_writer.add_rope_scaling_factor(yarn_params["factor"])
             gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"])
             gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"])
-            gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim
+            gguf_writer.add_rope_scaling_yarn_log_mul(mscale_all_dim)
             gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"])
 
         if "llama_4_scaling" in hparams:

src/llama-model.cpp

@@ -1994,7 +1994,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     }
                 }
 
-                if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
+                if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false)) {
                     // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
                     // cancel the factor from the convert script
                     hparams.rope_yarn_log_mul /= 0.1f;
@@ -2868,7 +2868,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
 
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, 0.0f);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, false);
 
                 hparams.f_attn_temp_offset = 0.0f;