Tighten _can_estimate_kv gate and treat sliding_window=0 as disabled

Two additional fixes from review round 1 (5/8 and 4/8 reviewer consensus):

1. _can_estimate_kv now requires BOTH key_length AND value_length for
   the explicit-dims path. Previously key_length alone was enough,
   which could cause silent fallthrough to the legacy formula with
   fabricated defaults (n_kv=1, head_dim=128) when value_length was
   absent from the GGUF.

2. SWA path now requires sliding_window > 0. Some GGUFs use 0 as a
   disabled sentinel. Without this guard, min(ctx, 0) would zero out
   all SWA layer contributions, severely underestimating KV cache.

studio/backend/core/inference/llama_cpp.py

@@ -358,12 +358,12 @@ class LlamaCppBackend:
         """True if we have enough GGUF metadata to estimate KV cache size."""
         if self._n_layers is None:
             return False
-        # New-style: explicit key/value dimensions from GGUF
-        if self._kv_key_length is not None:
-            return True
-        # MLA: kv_lora_rank is sufficient
+        # MLA: kv_lora_rank is sufficient (K-only cache)
         if self._kv_lora_rank is not None:
             return True
+        # New-style: need both explicit key AND value dimensions
+        if self._kv_key_length is not None and self._kv_value_length is not None:
+            return True
         # Legacy: need embedding_length + head count
         return self._embedding_length is not None and (
             self._n_kv_heads is not None or self._n_heads is not None
@@ -434,6 +434,7 @@ class LlamaCppBackend:
         # which is still far more accurate than the legacy formula (which ignores SWA).
         if (
             self._sliding_window is not None
+            and self._sliding_window > 0
             and key_len is not None
             and val_len is not None
         ):