baiying/AMD395 integration (2026-06): ctx sizing, model knowledge, openclaw controls, engine-registry env, A1/A5/A6 fixes

catalog/models/glm-4.7-flash.yaml

@@ -36,13 +36,16 @@ variants:
     default_config:
       quantization: int4
       n_gpu_layers: 999
-      ctx_size: 8192
+      # Full trained context (202752). Deploy auto-clamps it down to fit hardware
+      # memory on smaller machines (weights ~18GB + KV cache), so this large default
+      # is safe everywhere.
+      ctx_size: 202752
     expected_performance:
       startup_time_s: 20
       cold_start_time_s: 40
       tokens_per_second: [48, 60]
       latency_first_token_ms: [30, 200]
-      notes: "Verified 2026-06-08 on AMD Strix Halo Radeon 8060S iGPU, llama.cpp b9180 HIP, Q4_K_M (deepseek2 arch, 30B-A3B MoE) all 999 layers offloaded: 58.9 tok/s decode, 83 tok/s prompt."
+      notes: "Verified 2026-06-08 on AMD Strix Halo Radeon 8060S iGPU, llama.cpp b9180 HIP, Q4_K_M (deepseek2 arch, 30B-A3B MoE) all 999 layers offloaded: 58.9 tok/s decode, 83 tok/s prompt. Context verified 2026-06-16: llama-server loads & serves n_ctx=202752 (full trained context) on the iGPU."
   # --- GB10 (Blackwell, 128GB unified, K3S) ---
   # BF16 ~59GB, 128GB unified memory 可容纳
   # Glm4MoeLiteForCausalLM 架构 (glm4_moe_lite model_type)

catalog/models/qwen2.5-vl-3b-instruct.yaml

@@ -38,13 +38,22 @@ variants:
     hardware:
       gpu_arch: "*"
       vram_min_mib: 0
-      ram_min_mib: 4096
+      ram_min_mib: 8192
     engine: llamacpp
     format: gguf
     default_config:
       quantization: int4
       n_gpu_layers: 999
-      ctx_size: 8192
+      # Context window = the model's full trained context, 128000 (128K) — verified
+      # on Strix Halo: llama-server loads & serves n_ctx=128000 (KV cache ~36 KB/token
+      # → ~5 GB at 128K; model Q4 ~2 GB; so ~8 GB total, hence ram_min 8192). 8192 was
+      # too small for agent clients (OpenClaw injects all MCP tool schemas → base prompt
+      # ~9.3K tokens > 8192 → "context overflow", model never replies). A large n_ctx
+      # only reserves KV memory up front; it does NOT slow short conversations (llama
+      # processes only the actual tokens). Do NOT exceed 128000 (the trained limit) or
+      # llama-server warns of training-context overflow. Override per-deploy with
+      # `--config ctx_size=N` on memory-constrained hardware.
+      ctx_size: 128000
     expected_performance:
       startup_time_s: 10
       cold_start_time_s: 20

catalog/models/qwen3-embedding-4b.yaml

@@ -0,0 +1,51 @@
+kind: model_asset
+metadata:
+  name: qwen3-embedding-4b
+  type: embedding
+  family: qwen
+  parameter_count: "4B"
+  released_at: "2025-06"
+  # Scan-name aliases: on-disk GGUF names so the local scanner matches this asset.
+  aliases:
+    - Qwen3-Embedding-4B
+    - Qwen3-Embedding-4B-Q4_K_M
+    - Qwen3-Embedding-4B-Q8_0
+    - qwen3-embedding-4b-q4_k_m
+    - qwen3-embedding-4b-q8_0
+storage:
+  formats: [safetensors, gguf]
+  default_path_pattern: "{{.DataDir}}/models/{{.Name}}"
+  sources:
+    - type: huggingface
+      repo: Qwen/Qwen3-Embedding-4B
+      format: safetensors
+    - type: huggingface
+      repo: Qwen/Qwen3-Embedding-4B-GGUF
+      format: gguf
+    - type: modelscope
+      repo: Qwen/Qwen3-Embedding-4B
+      format: safetensors
+    - type: local_path
+      path: ""
+variants:
+  # --- universal llamacpp GGUF (verified on AMD Strix Halo Radeon 8060S iGPU) ---
+  # Text-embedding model: deploy llama-server in --embedding mode (config embedding=true
+  # → --embedding) and serve OpenAI /v1/embeddings. 2560-dim output. Not a chat model,
+  # so it is NOT written into OpenClaw config by `aima openclaw sync` (embedding models
+  # have no chat provider mapping); use the /v1/embeddings endpoint directly.
+  - name: qwen3-embedding-4b-universal-llamacpp
+    hardware:
+      gpu_arch: "*"
+      vram_min_mib: 0
+      ram_min_mib: 6144
+    engine: llamacpp
+    format: gguf
+    default_config:
+      n_gpu_layers: 999
+      embedding: true
+      # Native context (40960). Deploy auto-clamps to fit hardware memory if needed.
+      ctx_size: 40960
+    expected_performance:
+      startup_time_s: 8
+      cold_start_time_s: 15
+      notes: "Verified 2026-06-16 on AMD Strix Halo Radeon 8060S iGPU, llama.cpp b9180 HIP, Q8_0 all layers offloaded: deploys in --embedding mode (config embedding=true), /v1/embeddings returns 2560-dim vectors. ctx_size 40960."

catalog/models/qwen3.6-35b-a3b.yaml

@@ -7,16 +7,42 @@ metadata:
   released_at: "2026-04"
   aliases:
     - Qwen3.6-35B-A3B
+    - Qwen3.6-35B-A3B-UD-Q4_K_M
+    - qwen3.6-35b-a3b-q4_k_m
 storage:
-  formats: [safetensors]
+  formats: [safetensors, gguf]
   default_path_pattern: "{{.DataDir}}/models/{{.Name}}"
   sources:
     - type: huggingface
       repo: Qwen/Qwen3.6-35B-A3B
       format: safetensors
+    - type: huggingface
+      repo: unsloth/Qwen3.6-35B-A3B-GGUF
+      format: gguf
     - type: local_path
       path: ""
 variants:
+  # --- universal llamacpp GGUF (verified on AMD Strix Halo Radeon 8060S iGPU) ---
+  # Qwen3.6-35B-A3B MoE, UD-Q4_K_M ~21GB, all layers offloaded. Native context 262144.
+  - name: qwen3.6-35b-a3b-universal-llamacpp-q4
+    hardware:
+      gpu_arch: "*"
+      vram_min_mib: 0
+      ram_min_mib: 24576
+    engine: llamacpp
+    format: gguf
+    default_config:
+      quantization: int4
+      n_gpu_layers: 999
+      # Full trained context (262144 / 256K). Deploy auto-clamps it down to fit
+      # hardware memory on smaller machines, so this large default is safe.
+      ctx_size: 262144
+    expected_performance:
+      startup_time_s: 30
+      cold_start_time_s: 60
+      tokens_per_second: [55, 70]
+      latency_first_token_ms: [30, 250]
+      notes: "Context verified 2026-06-16 on AMD Strix Halo Radeon 8060S iGPU, llama.cpp b9180 HIP, UD-Q4_K_M all 999 layers offloaded: llama-server loads & serves n_ctx=262144 (full trained context)."
   # --- GB10 (Blackwell, 128GB unified, Docker/K3S) ---
   # 实测稳定路径是 text-only vLLM:
   # - language_model_only=true

cmd/aima/adapters.go

@@ -6,7 +6,9 @@ import (
 	"encoding/json"
 	"fmt"
 	"log/slog"
+	"os"
 	"os/exec"
+	"path/filepath"
 	"strings"
 	"sync"
 	"time"
@@ -484,19 +486,35 @@ func (a *fleetMCPAdapter) ListToolDefs() json.RawMessage {
 // toEngineBinarySource converts a knowledge.EngineSource to engine.BinarySource.
 // Centralises the mapping so callers don't repeat the 4-field struct literal.
 func toEngineBinarySource(src *knowledge.EngineSource) *engine.BinarySource {
+	if src == nil {
+		return nil
+	}
 	var probePaths []string
-	if src != nil && src.Probe != nil {
+	if src.Probe != nil {
 		probePaths = append(probePaths, src.Probe.Paths...)
 	}
 	return &engine.BinarySource{
-		Binary:      src.Binary,
-		Platforms:   src.Platforms,
-		Download:    src.Download,
-		Mirror:      src.Mirror,
-		SHA256:      src.SHA256,
-		InstallType: src.InstallType,
-		ProbePaths:  probePaths,
+		Binary:       src.Binary,
+		Platforms:    src.Platforms,
+		Download:     src.Download,
+		Mirror:       src.Mirror,
+		SHA256:       src.SHA256,
+		InstallType:  src.InstallType,
+		ProbePaths:   probePaths,
+		LocalBundles: engineLocalBundlesFromEnv(),
+	}
+}
+
+func engineLocalBundlesFromEnv() []string {
+	var bundles []string
+	for _, name := range []string{"AIMA_ENGINE_BUNDLE", "AIMA_ENGINE_ARCHIVE", "AIMA_ENGINE_OFFLINE_PACKAGE"} {
+		for _, path := range filepath.SplitList(os.Getenv(name)) {
+			if path = strings.TrimSpace(path); path != "" {
+				bundles = append(bundles, path)
+			}
+		}
 	}
+	return bundles
 }
 
 // execRunner implements engine.CommandRunner using real exec.
@@ -595,6 +613,7 @@ func (a openClawBackendAdapter) ListBackends() map[string]*openclaw.Backend {
 		result[k] = &openclaw.Backend{
 			ModelName:           b.ModelName,
 			EngineType:          b.EngineType,
+			ModelType:           b.ModelType,
 			Address:             b.Address,
 			Ready:               b.Ready,
 			Remote:              b.Remote,
@@ -628,7 +647,7 @@ type catalogAdapter struct{ cat *knowledge.Catalog }
 
 func (a catalogAdapter) ModelType(name string) string {
 	for _, m := range a.cat.ModelAssets {
-		if strings.EqualFold(m.Metadata.Name, name) {
+		if catalogModelNameMatches(m, name) {
 			return m.Metadata.Type
 		}
 	}
@@ -641,7 +660,7 @@ func (a catalogAdapter) ModelContextWindow(name string) int {
 
 func (a catalogAdapter) ModelFamily(name string) string {
 	for _, m := range a.cat.ModelAssets {
-		if strings.EqualFold(m.Metadata.Name, name) {
+		if catalogModelNameMatches(m, name) {
 			return m.Metadata.Family
 		}
 	}
@@ -650,7 +669,7 @@ func (a catalogAdapter) ModelFamily(name string) string {
 
 func (a catalogAdapter) ModelChatProvider(name string) bool {
 	for _, m := range a.cat.ModelAssets {
-		if strings.EqualFold(m.Metadata.Name, name) {
+		if catalogModelNameMatches(m, name) {
 			if m.OpenClaw != nil && m.OpenClaw.ChatProvider != nil {
 				return *m.OpenClaw.ChatProvider
 			}
@@ -660,6 +679,18 @@ func (a catalogAdapter) ModelChatProvider(name string) bool {
 	return true
 }
 
+func catalogModelNameMatches(m knowledge.ModelAsset, name string) bool {
+	if strings.EqualFold(m.Metadata.Name, name) {
+		return true
+	}
+	for _, alias := range m.Metadata.Aliases {
+		if strings.EqualFold(alias, name) {
+			return true
+		}
+	}
+	return false
+}
+
 func (a catalogAdapter) Adapters(name string) []inferencehttp.Adapter {
 	for _, m := range a.cat.ModelAssets {
 		if !strings.EqualFold(m.Metadata.Name, name) || m.HTTP == nil {

cmd/aima/compat.go

@@ -103,7 +103,8 @@ func prepareContainerCompatibility(
 	}
 
 	probeSummary := summarizeCompatibilityProbeOutput(probeOutput, probeErr)
-	if allowAutoPull && len(resolved.EngineRegistries) > 0 && !strings.EqualFold(resolved.EngineDistribution, "local") {
+	engineRegistries := engineRegistriesWithEnv(resolved.EngineRegistries)
+	if allowAutoPull && len(engineRegistries) > 0 && !strings.EqualFold(resolved.EngineDistribution, "local") {
 		if refreshErr := refreshDockerImageForCompatibilityProbe(ctx, runner, resolved); refreshErr == nil {
 			plan.DockerImageChanged = true
 			refreshedOutput, refreshedErr := timedContainerCompatibilityProbe(ctx, runner, resolved.CompatibilityProbe, resolved.EngineImage, modelPath, trustRemoteCode, nil)
@@ -159,7 +160,7 @@ func ensureDockerImageForCompatibilityProbe(ctx context.Context, runner engine.C
 	if engine.ImageExistsInDocker(ctx, resolved.EngineImage, runner) {
 		return true, false, nil
 	}
-	if !allowAutoPull || len(resolved.EngineRegistries) == 0 || strings.EqualFold(resolved.EngineDistribution, "local") {
+	if !allowAutoPull || len(engineRegistriesWithEnv(resolved.EngineRegistries)) == 0 || strings.EqualFold(resolved.EngineDistribution, "local") {
 		return false, false, nil
 	}
 	if err := pullDockerImageForCompatibilityProbe(ctx, runner, resolved); err != nil {
@@ -179,20 +180,21 @@ func pullDockerImageForCompatibilityProbe(ctx context.Context, runner engine.Com
 	if resolved == nil {
 		return fmt.Errorf("resolved config is nil")
 	}
-	if len(resolved.EngineRegistries) == 0 {
+	engineRegistries := engineRegistriesWithEnv(resolved.EngineRegistries)
+	if len(engineRegistries) == 0 {
 		return fmt.Errorf("no registries configured for %s", resolved.EngineImage)
 	}
 	imgName, imgTag := splitImageRef(resolved.EngineImage)
 	if err := engine.Pull(ctx, engine.PullOptions{
 		Image:          imgName,
 		Tag:            imgTag,
-		Registries:     resolved.EngineRegistries,
+		Registries:     engineRegistries,
 		Runner:         &dockerOnlyRunner{base: runner},
 		ExpectedDigest: resolved.EngineDigest,
 	}); err != nil {
 		return err
 	}
-	return ensureDockerImageAlias(ctx, runner, resolved.EngineImage, resolved.EngineRegistries)
+	return ensureDockerImageAlias(ctx, runner, resolved.EngineImage, engineRegistries)
 }
 
 func ensureDockerImageAlias(ctx context.Context, runner engine.CommandRunner, image string, registries []string) error {

cmd/aima/ctxfit_test.go

@@ -0,0 +1,105 @@
+package main
+
+import (
+	"testing"
+
+	"github.com/jguan/aima/internal/knowledge"
+)
+
+// Qwen2.5-VL-3B text backbone: 36 layers, 2 KV heads, head_dim 128 → 36864 B/token.
+const qwen25vl3bKVPerTok = int64(2 * 36 * 2 * 128 * 2)
+
+func TestClampContextForMemory(t *testing.T) {
+	tests := []struct {
+		name      string
+		reqCtx    int
+		nCtxTrain int
+		kvPerTok  int64
+		usableMiB int
+		nonKVMiB  int
+		want      int
+		clamped   bool // want < reqCtx
+	}{
+		{
+			name:      "huge memory, capped only by trained context",
+			reqCtx:    128000, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 112640, nonKVMiB: 4300, // ~110GB Strix Halo
+			want: 128000, clamped: false,
+		},
+		{
+			name:      "request above trained context is capped down",
+			reqCtx:    200000, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 112640, nonKVMiB: 4300,
+			want: 128000, clamped: true,
+		},
+		{
+			name:      "16GB unified clamps below 128k",
+			reqCtx:    128000, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 12288, nonKVMiB: 4300, // 16GB - 4GB reserve
+			// budget = 12288*0.9 - 4300 - 1024 = 5734 MiB → /36864 B = ~163k → but capped at train 128000? 163k>128000 so 128000
+			want: 128000, clamped: false,
+		},
+		{
+			name:      "8GB discrete GPU clamps below 128k",
+			reqCtx:    128000, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 8192, nonKVMiB: 4300,
+			// budget = int(8192*0.9) - 4300 - 1024 = 7372 - 5324 = 2048 MiB
+			// maxCtx = 2048*1024*1024/36864 = 58254 → round down to 58112
+			want: 58112, clamped: true,
+		},
+		{
+			name:      "tiny memory floors at minCtx",
+			reqCtx:    128000, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 6144, nonKVMiB: 4300,
+			// budget = 6144*0.9 - 4300 - 1024 = ~205 MiB → ~5832 → round 5632
+			want: 5632, clamped: true,
+		},
+		{
+			name:      "unknown memory: only trained-context cap applies",
+			reqCtx:    65536, nCtxTrain: 128000, kvPerTok: qwen25vl3bKVPerTok,
+			usableMiB: 0, nonKVMiB: 0,
+			want: 65536, clamped: false,
+		},
+		{
+			name:      "unknown arch (kvPerTok 0): no memory clamp",
+			reqCtx:    65536, nCtxTrain: 0, kvPerTok: 0,
+			usableMiB: 8192, nonKVMiB: 4300,
+			want: 65536, clamped: false,
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got, _ := clampContextForMemory(tt.reqCtx, tt.nCtxTrain, tt.kvPerTok, tt.usableMiB, tt.nonKVMiB)
+			if got != tt.want {
+				t.Errorf("clampContextForMemory = %d, want %d", got, tt.want)
+			}
+			if (got < tt.reqCtx) != tt.clamped {
+				t.Errorf("clamped = %v, want %v (got=%d req=%d)", got < tt.reqCtx, tt.clamped, got, tt.reqCtx)
+			}
+		})
+	}
+}
+
+func TestUsableMemoryMiB(t *testing.T) {
+	tests := []struct {
+		name string
+		hw   knowledge.HardwareInfo
+		want int
+	}{
+		{"unified 128GB reserves 16GB cap", knowledge.HardwareInfo{UnifiedMemory: true, RAMTotalMiB: 131072}, 131072 - 16384},
+		{"unified 16GB reserves 1/4", knowledge.HardwareInfo{UnifiedMemory: true, RAMTotalMiB: 16384}, 16384 - 4096},
+		{"unified 8GB reserve floored at 2GB", knowledge.HardwareInfo{UnifiedMemory: true, RAMTotalMiB: 8192}, 8192 - 2048},
+		{"unified APU prefers iGPU pool over under-detected OS RAM", knowledge.HardwareInfo{UnifiedMemory: true, RAMTotalMiB: 32768, GPUVRAMMiB: 110456}, 110456},
+		{"discrete prefers free VRAM", knowledge.HardwareInfo{GPUVRAMMiB: 8192, GPUMemFreeMiB: 7000}, 7000},
+		{"discrete falls back to total VRAM", knowledge.HardwareInfo{GPUVRAMMiB: 8192}, 8192},
+		{"cpu-only uses system RAM", knowledge.HardwareInfo{RAMTotalMiB: 32768}, 32768 - 8192},
+		{"unknown memory returns 0", knowledge.HardwareInfo{}, 0},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if got := usableMemoryMiB(tt.hw); got != tt.want {
+				t.Errorf("usableMemoryMiB = %d, want %d", got, tt.want)
+			}
+		})
+	}
+}