mlx: mixed-precision quant and capability detection improvements (#15409)

Improve the MLX model creation pipeline with several model-agnostic changes:

- Rewrite supportsVision to use vision_config instead of architecture name
- Add supportsAudio for audio encoder detection
- Add alignment checking (isAligned) for quantization group sizes
- Support per-projection mixed quantization in MoE expert packing
- Record per-tensor quant metadata in safetensors blobs
- Parse per-tensor quant metadata at model load time
- Validate quantize output is non-empty before storing
- Fix pin/unpin cleanup in expert group quantization
- Promote v_proj/k_proj/down_proj to INT8 for INT4 base quant
- Add MetalIsAvailable() utility
- Skip audio encoder tensors from quantization

x/create/client/create_test.go

@@ -311,10 +311,30 @@ func TestInferSafetensorsCapabilities(t *testing.T) {
 			name: "qwen3.5 multimodal model",
 			configJSON: `{
 				"architectures": ["Qwen3_5ForConditionalGeneration"],
-				"model_type": "qwen3"
+				"model_type": "qwen3",
+				"vision_config": {"hidden_size": 1024}
 			}`,
 			want: []string{"completion", "vision", "thinking"},
 		},
+		{
+			name: "model with audio config",
+			configJSON: `{
+				"architectures": ["Gemma4ForConditionalGeneration"],
+				"model_type": "gemma4",
+				"vision_config": {"hidden_size": 1024},
+				"audio_config": {"num_mel_bins": 128}
+			}`,
+			want: []string{"completion", "vision", "audio"},
+		},
+		{
+			name: "model with audio but no vision",
+			configJSON: `{
+				"architectures": ["SomeAudioModel"],
+				"model_type": "other",
+				"audio_config": {"num_mel_bins": 128}
+			}`,
+			want: []string{"completion", "audio"},
+		},
 		{
 			name: "non-qwen conditional generation model",
 			configJSON: `{
@@ -339,6 +359,74 @@ func TestInferSafetensorsCapabilities(t *testing.T) {
 	}
 }
 
+func TestParsePerExpertInputs(t *testing.T) {
+	makeInput := func(name, quantize string) create.PackedTensorInput {
+		return create.PackedTensorInput{Name: name, Quantize: quantize}
+	}
+
+	t.Run("uniform quant across projections", func(t *testing.T) {
+		inputs := []create.PackedTensorInput{
+			makeInput("layer.moe.experts.0.gate_proj.weight", "int4"),
+			makeInput("layer.moe.experts.1.gate_proj.weight", "int4"),
+			makeInput("layer.moe.experts.0.down_proj.weight", "int4"),
+			makeInput("layer.moe.experts.1.down_proj.weight", "int4"),
+		}
+		groups, projQ := parsePerExpertInputs("layer.moe.experts", inputs)
+		if groups == nil {
+			t.Fatal("expected non-nil groups")
+		}
+		if len(groups) != 2 {
+			t.Fatalf("expected 2 projection groups, got %d", len(groups))
+		}
+		if projQ["gate_proj.weight"] != "int4" {
+			t.Errorf("gate_proj quant = %q, want int4", projQ["gate_proj.weight"])
+		}
+		if projQ["down_proj.weight"] != "int4" {
+			t.Errorf("down_proj quant = %q, want int4", projQ["down_proj.weight"])
+		}
+	})
+
+	t.Run("mixed quant across projections", func(t *testing.T) {
+		inputs := []create.PackedTensorInput{
+			makeInput("layer.moe.experts.0.gate_proj.weight", "int4"),
+			makeInput("layer.moe.experts.1.gate_proj.weight", "int4"),
+			makeInput("layer.moe.experts.0.down_proj.weight", "int8"),
+			makeInput("layer.moe.experts.1.down_proj.weight", "int8"),
+		}
+		groups, projQ := parsePerExpertInputs("layer.moe.experts", inputs)
+		if groups == nil {
+			t.Fatal("expected non-nil groups for mixed cross-projection quant")
+		}
+		if projQ["gate_proj.weight"] != "int4" {
+			t.Errorf("gate_proj quant = %q, want int4", projQ["gate_proj.weight"])
+		}
+		if projQ["down_proj.weight"] != "int8" {
+			t.Errorf("down_proj quant = %q, want int8", projQ["down_proj.weight"])
+		}
+	})
+
+	t.Run("mixed quant within same projection rejected", func(t *testing.T) {
+		inputs := []create.PackedTensorInput{
+			makeInput("layer.moe.experts.0.down_proj.weight", "int4"),
+			makeInput("layer.moe.experts.1.down_proj.weight", "int8"),
+		}
+		groups, _ := parsePerExpertInputs("layer.moe.experts", inputs)
+		if groups != nil {
+			t.Fatal("expected nil for mixed quant within same projection")
+		}
+	})
+
+	t.Run("non-experts group rejected", func(t *testing.T) {
+		inputs := []create.PackedTensorInput{
+			makeInput("layer.mlp.gate_proj.weight", "int4"),
+		}
+		groups, _ := parsePerExpertInputs("layer.mlp", inputs)
+		if groups != nil {
+			t.Fatal("expected nil for non-experts group")
+		}
+	})
+}
+
 func TestQuantizeSupported(t *testing.T) {
 	// This just verifies the function exists and returns a boolean
 	// The actual value depends on build tags (mlx vs non-mlx)