docs: expand package doc with workflow examples

Cover generate, chat, classify, batch generate, metrics, model info,
discovery, and Metal memory controls.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

TODO.md

@@ -41,7 +41,7 @@ Implementation plan: `docs/plans/2026-02-19-backend-abstraction-plan.md`
 - [x] **Integration tests** — 7 tests for public API (backend registration, options, LoadModel paths).
 - [x] **Error handling audit** — ✅ `checkError()` replaced with `lastError() error` (reads + clears C-level error string). Added `Eval(...*Array) error` and `EvalAsync(...*Array) error` as error-returning variants of Materialize. Generate loop propagates errors via `m.lastErr`. `LoadAllSafetensors` returns `(map, error)`. Model loaders (gemma3, qwen3) check `lastError()` after safetensors load. grad.go/lora.go now surface real MLX error messages. 4 new tests in error_test.go.
 - [x] **Memory management — deterministic cleanup** — ✅ `Model.Close()` now walks the full model tree (GemmaModel/Qwen3Model) and explicitly frees all weight arrays via `Free()`. Helpers: `freeLinear`, `freeEmbedding`, `freeRMSNorm`, `freeCaches`, `closeGemma`, `closeQwen3` in close.go. Handles tied output weights (skip double-free), nil safety, idempotent Close(). 8 new tests in close_test.go.
-- [ ] **Documentation** — Public API has godoc but needs examples for common workflows.
+- [x] **Documentation** — ✅ Package docs expanded with examples for all common workflows: Generate, Chat, Classify, BatchGenerate, Metrics, ModelInfo, Discover, memory controls. Both go-mlx and go-inference package docs updated with godoc heading sections.
 
 ## Phase 5: Ecosystem Integration (Virgil wishlist)
 

mlx.go

@@ -2,23 +2,98 @@
 //
 // This package implements the [inference.Backend] interface from
 // forge.lthn.ai/core/go-inference for Apple Silicon (M1-M4) GPUs.
+// Import it blank to register the "metal" backend automatically:
+//
+//	import _ "forge.lthn.ai/core/go-mlx"
 //
 // Build mlx-c before use:
 //
 //	go generate ./...
 //
-// Load a model and generate text:
-//
-//	import "forge.lthn.ai/core/go-inference"
-//	import _ "forge.lthn.ai/core/go-mlx" // register Metal backend
+// # Generate text
 //
 //	m, err := inference.LoadModel("/path/to/model/")
 //	if err != nil { log.Fatal(err) }
 //	defer m.Close()
 //
+//	ctx := context.Background()
 //	for tok := range m.Generate(ctx, "What is 2+2?", inference.WithMaxTokens(128)) {
 //	    fmt.Print(tok.Text)
 //	}
+//	if err := m.Err(); err != nil { log.Fatal(err) }
+//
+// # Multi-turn chat
+//
+// Chat applies the model's native template (Gemma3, Qwen3, Llama3):
+//
+//	for tok := range m.Chat(ctx, []inference.Message{
+//	    {Role: "system", Content: "You are a helpful assistant."},
+//	    {Role: "user", Content: "Translate 'hello' to French."},
+//	}, inference.WithMaxTokens(64)) {
+//	    fmt.Print(tok.Text)
+//	}
+//
+// # Batch classification
+//
+// Classify runs a single forward pass per prompt (prefill only, no decoding):
+//
+//	results, err := m.Classify(ctx, []string{
+//	    "Bonjour, comment allez-vous?",
+//	    "The quarterly report shows growth.",
+//	}, inference.WithTemperature(0))
+//	for i, r := range results {
+//	    fmt.Printf("prompt %d → %q\n", i, r.Token.Text)
+//	}
+//
+// # Batch generation
+//
+//	results, err := m.BatchGenerate(ctx, []string{
+//	    "The capital of France is",
+//	    "Water boils at",
+//	}, inference.WithMaxTokens(32))
+//	for i, r := range results {
+//	    for _, tok := range r.Tokens {
+//	        fmt.Print(tok.Text)
+//	    }
+//	    fmt.Println()
+//	}
+//
+// # Performance metrics
+//
+// After any inference call, retrieve timing and memory statistics:
+//
+//	for tok := range m.Generate(ctx, prompt, inference.WithMaxTokens(128)) {
+//	    fmt.Print(tok.Text)
+//	}
+//	met := m.Metrics()
+//	fmt.Printf("decode: %.0f tok/s, peak GPU: %d MB\n",
+//	    met.DecodeTokensPerSec, met.PeakMemoryBytes/1024/1024)
+//
+// # Model info
+//
+//	info := m.Info()
+//	fmt.Printf("%s %d-layer, %d-bit quantised\n",
+//	    info.Architecture, info.NumLayers, info.QuantBits)
+//
+// # Model discovery
+//
+//	models, err := inference.Discover("/path/to/models/")
+//	for _, d := range models {
+//	    fmt.Printf("%s (%s, %d-bit)\n", d.Path, d.ModelType, d.QuantBits)
+//	}
+//
+// # Metal memory controls
+//
+// These control the Metal allocator directly, not individual models:
+//
+//	mlx.SetCacheLimit(4 << 30)  // 4 GB cache limit
+//	mlx.SetMemoryLimit(32 << 30) // 32 GB hard limit
+//
+//	// Between chat turns, reclaim prompt cache memory:
+//	mlx.ClearCache()
+//
+//	fmt.Printf("active: %d MB, peak: %d MB\n",
+//	    mlx.GetActiveMemory()/1024/1024, mlx.GetPeakMemory()/1024/1024)
 package mlx
 
 //go:generate cmake -S . -B build -DCMAKE_INSTALL_PREFIX=dist -DCMAKE_BUILD_TYPE=Release