Kimi-K2.5: Use merged QKV for vision

convert_hf_to_gguf.py

@@ -11113,15 +11113,6 @@ class KimiK25Model(MmprojModel):
         if not is_vision:
             return
 
-        # Split fused QKV tensors in vision encoder
-        if "wqkv" in name:
-            split_dim = 0 if "weight" in name else -1
-            wq, wk, wv = data_torch.chunk(3, dim=split_dim)
-            yield from super().modify_tensors(wq, name.replace("wqkv", "wq"), bid)
-            yield from super().modify_tensors(wk, name.replace("wqkv", "wk"), bid)
-            yield from super().modify_tensors(wv, name.replace("wqkv", "wv"), bid)
-            return
-
         # Temporal embeddings: (T, 1, C) → (T, C)
         if "pos_emb.time_weight" in name:
             T, _, C = data_torch.shape

tools/mtmd/clip.cpp

@@ -726,6 +726,11 @@ ggml_tensor * clip_graph::build_rope_2d_interleaved(
 
     GGML_ASSERT(n_dim % 4 == 0);  // Must be divisible by 4 for interleaved x,y pairs
 
+    // Ensure input is contiguous (needed when using merged QKV with ggml_view)
+    if (!ggml_is_contiguous(cur)) {
+        cur = ggml_cont(ctx0, cur);
+    }
+
     // Step 1: Reshape to expose interleaved structure
     // cur: [n_dim, n_head, n_pos] -> [4, n_dim/4, n_head, n_pos]
     ggml_tensor * reshaped = ggml_reshape_4d(ctx0, cur, 4, n_dim/4, n_head, n_pos);