Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	CMakePresets.json
#	ggml/src/ggml-cuda/CMakeLists.txt
#	tests/test-sampling.cpp
#	tools/mtmd/clip.cpp

convert_hf_to_gguf.py

@@ -3915,6 +3915,16 @@ class Gemma3VisionModel(VisionModel):
         # default values below are taken from HF tranformers code
         self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
         self.gguf_writer.add_vision_use_gelu(True)
+        # calculate proj_scale_factor (used by tinygemma3 test model)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        n_per_side = int(image_seq_length ** 0.5)
+        image_size = self.hparams["image_size"]
+        patch_size = self.hparams["patch_size"]
+        proj_scale_factor = (image_size // patch_size) // n_per_side
+        if proj_scale_factor > 0 and proj_scale_factor != 4:
+            # we only need to write this if it's not the default value
+            # in this case, we are converting a test model
+            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
 
     def tensor_force_quant(self, name, new_name, bid, n_dims):
         del bid, new_name, n_dims  # unused
@@ -3928,6 +3938,9 @@ class Gemma3VisionModel(VisionModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
+        if "vision_model.head." in name:
+            return [] # skip redundant tensors for tinygemma3
+
         if name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \
                 or name.startswith("multimodal_projector.") or name.startswith("vision_model."):
             # process vision tensors

src/llama-sampling.cpp

@@ -1750,27 +1750,35 @@ static const char * llama_sampler_top_n_sigma_name(const struct llama_sampler *
 static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
     const auto * ctx = (llama_sampler_top_n_sigma *) smpl->ctx;
 
-    if (ctx->n < 0.0f) {
+    if (ctx->n <= 0.0f || cur_p->size <= 1) {
         return;
     }
 
     // find max logit and calculate mean
     float max = cur_p->data[0].logit;
     float logits_sum = 0;
+    size_t valid_count = 0;
     for (size_t i = 0; i < cur_p->size; ++i) {
-        if (cur_p->data[i].logit > max) {
+        // Only count non-negative infinity values
-            max = cur_p->data[i].logit;
+        if (cur_p->data[i].logit != -INFINITY) {
+            if (cur_p->data[i].logit > max) {
+                max = cur_p->data[i].logit;
+            }
+            logits_sum += cur_p->data[i].logit;
+            valid_count++;
         }
-        logits_sum += cur_p->data[i].logit;
     }
-    float mean = logits_sum/cur_p->size;
+    float mean = valid_count > 0 ? logits_sum/valid_count : 0;
 
     // calculate standard deviation
     float acc = 0;
     for (size_t i = 0; i < cur_p->size; ++i) {
-        acc += pow(cur_p->data[i].logit - mean, 2);
+        // Skip -infinity in std calculation
+        if (cur_p->data[i].logit != -INFINITY) {
+            acc += pow(cur_p->data[i].logit - mean, 2);
+        }
     }
-    float std = sqrt(acc/cur_p->size);
+    float std = valid_count > 0 ? sqrt(acc/valid_count) : 0;
 
     //apply mask
     for (size_t i = 0; i < cur_p->size; ++i) {