{"version":3,"file":"adapter.cjs","names":["#baseline","#pipeline","#resolvePipeline","#pipelinePromise","#mmEngine","#mmEnginePromise","#gen","#resolveMultimodalEngine","#multimodalFlags","#generateKwargs"],"sources":["../../../../src/batteries/llm/transformers_js/adapter.ts"],"sourcesContent":["/**\n * Dual-environment (Node + browser) executor adapter for transformers.js (`@huggingface/transformers`).\n *\n * @module @nhtio/adk/batteries/llm/transformers_js/adapter\n *\n * @remarks\n * On-device text generation via ONNX Runtime — `onnxruntime-node` (native) in Node, `onnxruntime-web`\n * (WASM + WebGPU) in the browser, auto-selected by the package. So this battery is\n * **environment-neutral**: it does NOT gate on WebGPU.\n *\n * **transformers.js is text-in / text-out.** It injects tool definitions into the chat template but\n * does NOT return structured tool calls or reasoning — the model emits both as **family-specific raw\n * text**. This adapter parses them out via the shared, configurable parser layer (`toolCallParser` /\n * `reasoningParser`, both defaulting to `'auto'`): after generation, the reasoning parser pulls\n * thinking into ADK Thoughts and the tool-call parser pulls calls into ADK ToolCalls, leaving clean\n * prose as the assistant Message.\n *\n * Three pluggable layers mirror the other LLM batteries: swappable translation helpers, three-layer\n * options merging (constructor → `executor()` overrides → `ctx.stash.transformersJs`), and an\n * injectable/lazy pipeline (`pipeline` or `createPipeline`, defaulting to a dynamic import).\n */\n\nimport { DateTime } from 'luxon'\nimport { sha256 } from 'js-sha256'\nimport { v6 as uuidv6 } from 'uuid'\nimport { validateOptions } from './validation'\nimport { withModelSource } from './model_source'\nimport { emitLifecycle } from '../chat_common/lifecycle'\nimport { E_LLM_GPU_OUT_OF_MEMORY } from '../chat_common/exceptions'\nimport { isError, isInstanceOf, isObject } from '@nhtio/adk/guards'\nimport { resolveToolCallParser } from '../chat_common/tool_parsers'\nimport { resolveGenerationOptions } from '../chat_common/generation'\nimport { canonicalStringify } from '../../../lib/utils/canonical_json'\nimport { resolveReasoningParser } from '../chat_common/reasoning_parsers'\nimport { InMemorySpoolStore } from '@nhtio/adk/batteries/storage/in_memory'\nimport { isGpuOutOfMemoryError, probeGpuBudget } from '../chat_common/gpu_budget'\nimport {\n looksLikeSpooledArtifact,\n stripEnvelopeSpecialTokens,\n normalizeToolName,\n} from '../chat_common/helpers'\nimport {\n Tokenizable,\n ToolCall,\n Message,\n Thought,\n SpooledArtifact,\n Media,\n ArtifactTool,\n} from '@nhtio/adk/common'\nimport {\n E_TRANSFORMERS_JS_CONTEXT_OVERFLOW,\n E_TRANSFORMERS_JS_STREAM_ERROR,\n E_TRANSFORMERS_JS_INVALID_TOOL_CALL_ARGS,\n} from './exceptions'\nimport {\n defaultDescriptionToChatCompletionsJsonSchema,\n defaultRenderUntrustedContent,\n defaultRenderTrustedContent,\n defaultRenderStandingInstructions,\n defaultRenderMemories,\n defaultRenderRetrievables,\n defaultRenderRetrievableHandleBody,\n defaultRenderRetrievableSafetyDirective,\n defaultRenderFirstPartyRetrievables,\n defaultRenderThirdPartyPublicRetrievables,\n defaultRenderThirdPartyPrivateRetrievables,\n defaultRenderThought,\n defaultFilterThoughts,\n defaultRenderChatCompletionsSystemPrompt,\n defaultToolsToTransformersJsTools,\n defaultRenderTransformersJsToolResult,\n defaultRenderArtifactHandleBody,\n defaultBuildTransformersJsMessages,\n defaultMediaToTransformersInput,\n defaultCreateTransformersJsStreamAccumulator,\n} from './helpers'\nimport type { Tool } from '@nhtio/adk/common'\nimport type { DispatchContext } from '@nhtio/adk/types'\nimport type { ParsedToolCall } from '../chat_common/tool_parsers'\nimport type { TokenEncoding, TokenEncodingId } from '@nhtio/adk/types'\nimport type { ChatSampler, ResolvedGenerationOptions } from '../chat_common/generation'\nimport type { DispatchExecutorFn, DispatchExecutorHelpers } from '@nhtio/adk/dispatch_runner'\nimport type {\n TransformersJsAdapterOptions,\n TransformersJsPipeline,\n TransformersJsModel,\n TransformersJsProcessor,\n} from './types'\n\n// ─── Option merging (constructor → executor overrides → stash) ────────────────────────────────────\n\nconst mergeHelpers = (\n layers: ReadonlyArray> | undefined>\n): Partial> | undefined => {\n let merged: Partial> | undefined\n for (const layer of layers) {\n if (!layer) continue\n merged = { ...(merged ?? {}), ...layer }\n }\n return merged\n}\n\nconst mergeOptions = (\n baseline: TransformersJsAdapterOptions,\n exec: Partial | undefined,\n stash: Partial | undefined\n): Partial => {\n const layers = [baseline as Partial, exec ?? {}, stash ?? {}]\n const out: Record = {}\n for (const layer of layers) {\n for (const [k, v] of Object.entries(layer)) {\n if (v === undefined) continue\n if (k === 'helpers') continue\n out[k] = v\n }\n }\n const helpers = mergeHelpers(layers.map((l) => l.helpers))\n if (helpers !== undefined) out.helpers = helpers\n return out as Partial\n}\n\n// ─── Helper resolution (fall back to bundled defaults per field) ──────────────────────────────────\n\ninterface ResolvedHelpers {\n descriptionToChatCompletionsJsonSchema: typeof defaultDescriptionToChatCompletionsJsonSchema\n renderUntrustedContent: typeof defaultRenderUntrustedContent\n renderTrustedContent: typeof defaultRenderTrustedContent\n renderStandingInstructions: typeof defaultRenderStandingInstructions\n renderMemories: typeof defaultRenderMemories\n renderRetrievables: typeof defaultRenderRetrievables\n renderRetrievableSafetyDirective: typeof defaultRenderRetrievableSafetyDirective\n renderFirstPartyRetrievables: typeof defaultRenderFirstPartyRetrievables\n renderThirdPartyPublicRetrievables: typeof defaultRenderThirdPartyPublicRetrievables\n renderThirdPartyPrivateRetrievables: typeof defaultRenderThirdPartyPrivateRetrievables\n renderThought: typeof defaultRenderThought\n filterThoughts: typeof defaultFilterThoughts\n renderChatCompletionsSystemPrompt: typeof defaultRenderChatCompletionsSystemPrompt\n toolsToTransformersJsTools: typeof defaultToolsToTransformersJsTools\n renderTransformersJsToolResult: typeof defaultRenderTransformersJsToolResult\n renderArtifactHandleBody: typeof defaultRenderArtifactHandleBody\n renderRetrievableHandleBody: typeof defaultRenderRetrievableHandleBody\n buildTransformersJsMessages: typeof defaultBuildTransformersJsMessages\n createTransformersJsStreamAccumulator: typeof defaultCreateTransformersJsStreamAccumulator\n}\n\nconst resolveHelpers = (\n overrides: Partial | undefined\n): ResolvedHelpers => {\n const src = (overrides ?? {}) as Record\n const pick = (\n key: K,\n dflt: ResolvedHelpers[K]\n ): ResolvedHelpers[K] => (src[key as string] as ResolvedHelpers[K]) ?? dflt\n return {\n descriptionToChatCompletionsJsonSchema: pick(\n 'descriptionToChatCompletionsJsonSchema',\n defaultDescriptionToChatCompletionsJsonSchema\n ),\n renderUntrustedContent: pick('renderUntrustedContent', defaultRenderUntrustedContent),\n renderTrustedContent: pick('renderTrustedContent', defaultRenderTrustedContent),\n renderStandingInstructions: pick(\n 'renderStandingInstructions',\n defaultRenderStandingInstructions\n ),\n renderMemories: pick('renderMemories', defaultRenderMemories),\n renderRetrievables: pick('renderRetrievables', defaultRenderRetrievables),\n renderRetrievableSafetyDirective: pick(\n 'renderRetrievableSafetyDirective',\n defaultRenderRetrievableSafetyDirective\n ),\n renderFirstPartyRetrievables: pick(\n 'renderFirstPartyRetrievables',\n defaultRenderFirstPartyRetrievables\n ),\n renderThirdPartyPublicRetrievables: pick(\n 'renderThirdPartyPublicRetrievables',\n defaultRenderThirdPartyPublicRetrievables\n ),\n renderThirdPartyPrivateRetrievables: pick(\n 'renderThirdPartyPrivateRetrievables',\n defaultRenderThirdPartyPrivateRetrievables\n ),\n renderThought: pick('renderThought', defaultRenderThought),\n filterThoughts: pick('filterThoughts', defaultFilterThoughts),\n renderChatCompletionsSystemPrompt: pick(\n 'renderChatCompletionsSystemPrompt',\n defaultRenderChatCompletionsSystemPrompt\n ),\n toolsToTransformersJsTools: pick(\n 'toolsToTransformersJsTools',\n defaultToolsToTransformersJsTools\n ),\n renderTransformersJsToolResult: pick(\n 'renderTransformersJsToolResult',\n defaultRenderTransformersJsToolResult\n ),\n renderArtifactHandleBody: pick('renderArtifactHandleBody', defaultRenderArtifactHandleBody),\n renderRetrievableHandleBody: pick(\n 'renderRetrievableHandleBody',\n defaultRenderRetrievableHandleBody\n ),\n buildTransformersJsMessages: pick(\n 'buildTransformersJsMessages',\n defaultBuildTransformersJsMessages\n ),\n createTransformersJsStreamAccumulator: pick(\n 'createTransformersJsStreamAccumulator',\n defaultCreateTransformersJsStreamAccumulator\n ),\n }\n}\n\nconst nowIso = (): string => DateTime.now().toISO() as string\n\nconst computeChecksum = (tool: string, args: Record): string =>\n sha256(canonicalStringify({ tool, args }))\n\n/**\n * Wrap the consumer's `onInitProgress` so each transformers.js download event ALSO emits a normalized\n * `loading` lifecycle report. The HF `progress` field is 0..100 → forwarded as `progress` 0..1, with the\n * raw payload on `raw`. The original `onInitProgress` is still called verbatim (additive). Returns the\n * original callback unchanged when no lifecycle hooks are configured (zero overhead on the text path).\n */\nconst wrapTransformersInitProgress = (\n merged: TransformersJsAdapterOptions\n): TransformersJsAdapterOptions['onInitProgress'] => {\n const hasLifecycle =\n merged.onLifecycle ??\n merged.onLoading ??\n merged.onReady ??\n merged.onGenerating ??\n merged.onError\n if (!hasLifecycle) return merged.onInitProgress\n return (info: unknown) => {\n const p = (info as { progress?: number } | undefined)?.progress\n emitLifecycle(merged, 'transformers_js', merged.model, 'loading', {\n ...(typeof p === 'number' ? { progress: p / 100 } : {}),\n raw: info,\n })\n merged.onInitProgress?.(info as never)\n }\n}\n\n/** Markers that signal the start of tool-call / reasoning markup — used to stop streaming prose. */\nconst TEXT_MARKUP_MARKERS = [\n '',\n '<|tool_call>',\n '<|channel',\n '\n | InstanceType => {\n const message = isError(err) ? err.message : String(err)\n if (isGpuOutOfMemoryError(message)) {\n return new E_LLM_GPU_OUT_OF_MEMORY([message, contextNote], {\n cause: isError(err) ? err : undefined,\n })\n }\n return new E_TRANSFORMERS_JS_STREAM_ERROR([message])\n}\n\n/**\n * Whether the resolved device targets the WebGPU execution provider (a scalar `'webgpu'` or a\n * per-submodule record that puts the decoder on webgpu). KV-cache GPU-pinning only applies there —\n * on the wasm/cpu EP there is no `'gpu-buffer'` location and the pin would be meaningless.\n */\nconst isWebGpuDevice = (device: TransformersJsAdapterOptions['device']): boolean => {\n if (device === undefined) return false\n if (typeof device === 'string') return device === 'webgpu' || device === 'gpu'\n // Per-submodule record: pin if the decoder (or any submodule) runs on webgpu.\n return Object.values(device).some((d) => d === 'webgpu' || d === 'gpu')\n}\n\n/**\n * Build a `preferredOutputLocation` map pinning every KV-cache output (`present.N.key` /\n * `present.N.value`) to `'gpu-buffer'`, so the autoregressive KV cache lives in GPU memory instead of\n * the ONNX-Runtime-Web **wasm32 linear-memory heap** (hard-capped at 4 GiB by the 32-bit address space).\n *\n * @remarks\n * This is the battery's headline in-browser memory fix. transformers.js HAS auto-pinning for this\n * (session.js builds the same map for `cache_sessions` on webgpu), but it silently no-ops on models\n * whose config nests the head/layer counts (e.g. Gemma-4's `text_config`): `getCacheNames` returns\n * empty, so the decoder loads with `preferredOutputLocation: null` and the KV cache stays on the wasm\n * heap (measured: the live `present.*` tensors report `location: \"cpu\"`). Passing the map EXPLICITLY\n * moves them to `location: \"gpu-buffer\"` (measured). We over-specify the layer count — extra\n * `present.*` names that the model doesn't emit are simply ignored by the runtime — so we don't have to\n * read the architecture's layer count first. 96 covers every open-weight decoder we target.\n */\nconst buildKvCacheGpuPinMap = (layers = 96): Record => {\n const map: Record = {}\n for (let i = 0; i < layers; i++) {\n map[`present.${i}.key`] = 'gpu-buffer'\n map[`present.${i}.value`] = 'gpu-buffer'\n }\n return map\n}\n\n/**\n * Resolve the effective ONNX `session_options` for a load, applying the KV-cache GPU-pin DEFAULT.\n *\n * @remarks\n * Policy (surface-a-safe-default, keep the escape hatch): on the WebGPU EP, pin the KV cache to\n * `'gpu-buffer'` BY DEFAULT (keeps it off the 4 GiB wasm32 heap — the wall hit first in-browser). The\n * consumer can opt out wholesale (`pinKvCacheToGpu: false`) or override precisely (set their own\n * `sessionOptions.preferredOutputLocation`, which always wins — we never clobber an explicit one). On a\n * non-WebGPU device the pin is skipped (no `'gpu-buffer'` location exists there). Returns `undefined`\n * when there is nothing to pass (so the loader's `...(sessionOptions ? … : {})` spread stays a no-op).\n */\nconst resolveSessionOptions = (\n merged: TransformersJsAdapterOptions\n): TransformersJsAdapterOptions['sessionOptions'] | undefined => {\n const explicit = merged.sessionOptions\n const wantPin = (merged.pinKvCacheToGpu ?? true) && isWebGpuDevice(merged.device)\n // Don't pin if disabled, off-WebGPU, or the consumer already chose an output location (their call).\n if (!wantPin || explicit?.preferredOutputLocation !== undefined) return explicit\n return { ...(explicit ?? {}), preferredOutputLocation: buildKvCacheGpuPinMap() }\n}\n\n/**\n * Dual-environment executor adapter for transformers.js text generation.\n *\n * @remarks\n * Construct with at least `{ model }`; wire `new TransformersJsAdapter(opts).executor()` into a\n * `DispatchRunner` as the `executorCallback`. The pipeline is resolved lazily on first dispatch (or\n * eagerly via {@link TransformersJsAdapter.preload}); pass `pipeline` to inject a pre-built one.\n */\nexport class TransformersJsAdapter {\n /** The `ctx.stash` key under which per-dispatch option overrides are read. */\n public static readonly STASH_KEY = 'transformersJs' as const\n\n readonly #baseline: TransformersJsAdapterOptions\n #pipeline: TransformersJsPipeline | undefined\n #pipelinePromise: Promise | undefined\n #mmEngine: { model: TransformersJsModel; processor: TransformersJsProcessor } | undefined\n #mmEnginePromise:\n | Promise<{\n model: TransformersJsModel\n processor: TransformersJsProcessor\n }>\n | undefined\n\n /**\n * Whether this battery is available. transformers.js is environment-neutral (Node + browser), so this\n * is `true` whenever the runtime can import the peer — there is no WebGPU requirement. Static form\n * returns `true`; the instance form honours an injected `isAvailable` override.\n */\n public static isAvailable(): boolean {\n return true\n }\n\n /**\n * @param options - Raw adapter options, validated against `transformersJsOptionsSchema`.\n * @throws {@link @nhtio/adk/batteries!E_INVALID_TRANSFORMERS_JS_OPTIONS} when `options` are invalid.\n */\n constructor(options: unknown) {\n this.#baseline = validateOptions(options)\n this.#pipeline = this.#baseline.pipeline\n }\n\n /** Instance availability probe (honours the `isAvailable` option override). */\n isAvailable(): boolean {\n return (this.#baseline.isAvailable ?? TransformersJsAdapter.isAvailable)()\n }\n\n /**\n * Eagerly resolve (load) the pipeline before the first dispatch.\n *\n * @param overrides - Optional option overrides applied for this load.\n */\n async preload(\n overrides?: Partial\n ): Promise {\n const merged = validateOptions(mergeOptions(this.#baseline, overrides, undefined))\n return this.#resolvePipeline(merged)\n }\n\n /** Drop the cached pipeline/engine and any in-flight load so the next dispatch re-resolves it. */\n reset(): void {\n this.#pipeline = undefined\n this.#pipelinePromise = undefined\n this.#mmEngine = undefined\n this.#mmEnginePromise = undefined\n }\n\n /**\n * Release the loaded model's underlying ONNX sessions + GPU/wasm buffers, then drop all cached\n * references (so the next dispatch re-resolves a fresh pipeline).\n *\n * @remarks\n * `reset()` only nulls the JS references — it does NOT free the native ONNX Runtime sessions or the\n * WebGPU/wasm device memory they hold. Those leak until GC, and in a browser session that loads many\n * models back-to-back (e.g. a full matrix run) the accumulated sessions exhaust the heap, surfacing as\n * `Can't create a session … Failed to load external data file … memory copy`. transformers.js exposes\n * `PreTrainedModel.dispose()` (\"disposes of all the ONNX sessions created during inference\") and\n * `Pipeline.dispose()` — this awaits them so the memory is actually reclaimed between loads. Settles any\n * in-flight load first, swallows per-handle disposal errors (a half-loaded model must not throw out of\n * teardown), and finishes with `reset()`. Idempotent and safe to call when nothing is loaded.\n */\n async dispose(): Promise {\n // Settle any in-flight load so we dispose the resolved handle rather than orphaning it.\n const pipeline = this.#pipeline ?? (await this.#pipelinePromise?.catch(() => undefined))\n const mmEngine = this.#mmEngine ?? (await this.#mmEnginePromise?.catch(() => undefined))\n const disposables: Array> = []\n const pipeWithDispose = pipeline as { dispose?: () => Promise } | undefined\n if (typeof pipeWithDispose?.dispose === 'function') {\n disposables.push(Promise.resolve(pipeWithDispose.dispose()).catch(() => undefined))\n }\n const mmModel = mmEngine?.model as { dispose?: () => Promise } | undefined\n if (typeof mmModel?.dispose === 'function') {\n disposables.push(Promise.resolve(mmModel.dispose()).catch(() => undefined))\n }\n await Promise.all(disposables)\n this.reset()\n }\n\n /**\n * Free the WebGPU buffer cache by releasing the model's ONNX sessions, then reload the same model.\n *\n * @remarks\n * The consumer-facing lever for the ONNX Runtime Web WebGPU **buffer-freelist high-water-mark** (see\n * {@link @nhtio/adk/batteries!probeGpuBudget} and the battery's GPU-budget notes). ORT-web parks freed\n * activation buffers in per-size buckets sized to the largest tensor shape the model has run; the pool\n * is flushed ONLY when every session of the model is released (ORT clears the cache at\n * `sessionCount === 0`; microsoft/onnxruntime#22490). There is no public flag to flush it mid-life, so\n * the supported way to reclaim that retained working-set without permanently unloading the model is to\n * dispose the sessions and load again.\n *\n * This is exactly `dispose()` followed by `preload()` — surfaced as a named method because \"recycle to\n * free the GPU buffer cache\" is a distinct, intentional operation (e.g. an application offering a\n * \"free GPU memory\" action after a {@link @nhtio/adk/batteries!E_LLM_GPU_OUT_OF_MEMORY}), not a\n * teardown. It is NOT invoked automatically by the battery — the ADK surfaces the lever and leaves the\n * decision to the consumer. Re-incurs the cold-load cost (download is cached; the WebGPU graph/shader\n * compile is not). Idempotent.\n *\n * @param overrides - Optional option overrides applied to the reload (same as {@link preload}).\n */\n async recycle(overrides?: Partial): Promise {\n await this.dispose()\n await this.preload(overrides)\n }\n\n /**\n * Resolve the PORTABLE generation contract (shared with LiteRT-LM) from the merged options. Canonical\n * fields win; the transformers.js-native fields ({@link TransformersJsAdapterOptions.maxNewTokens},\n * `doSample`, `multimodal`, …) are the fallback layer consulted only when the canonical one is unset.\n */\n #gen(merged: TransformersJsAdapterOptions): ResolvedGenerationOptions {\n // Native `multimodal` is `boolean | {image,audio}` → normalise to the canonical `{image,audio}` shape.\n const mm = merged.multimodal\n const normalizedMultimodal: { image?: boolean; audio?: boolean } | undefined =\n mm === undefined || mm === false\n ? mm === false\n ? { image: false, audio: false }\n : undefined\n : mm === true\n ? { image: true, audio: true }\n : mm\n // Native `doSample` boolean → canonical sampler strategy. `true` becomes `'top-p'` (the common\n // nucleus default); an explicit `sampler` canonical field overrides this entirely.\n const nativeSampler: ChatSampler | undefined =\n merged.doSample === undefined ? undefined : merged.doSample ? 'top-p' : 'greedy'\n return resolveGenerationOptions(\n {\n maxTokens: merged.maxTokens,\n sampler: merged.sampler,\n temperature: merged.temperature,\n topK: merged.topK,\n topP: merged.topP,\n seed: merged.seed,\n enableThinking: merged.enableThinking,\n // The canonical `multimodal` IS the native field here (transformers.js already used `{image,audio}`);\n // pass it as both so canonical-wins is a no-op and the normalized shape flows through.\n multimodal: normalizedMultimodal,\n },\n {\n maxTokens: merged.maxNewTokens,\n sampler: nativeSampler,\n multimodal: normalizedMultimodal,\n }\n )\n }\n\n /** Normalise multimodal config to `{image,audio}` flags, or undefined when fully off. */\n #multimodalFlags(\n merged: TransformersJsAdapterOptions\n ): { image: boolean; audio: boolean } | undefined {\n const { multimodal } = this.#gen(merged)\n return multimodal.image || multimodal.audio ? multimodal : undefined\n }\n\n /** Resolve (and cache, single-flight) the multimodal model+processor pair. */\n async #resolveMultimodalEngine(merged: TransformersJsAdapterOptions): Promise<{\n model: TransformersJsModel\n processor: TransformersJsProcessor\n }> {\n if (merged.multimodalEngine) {\n this.#mmEngine = merged.multimodalEngine\n return merged.multimodalEngine\n }\n if (this.#mmEngine) return this.#mmEngine\n this.#mmEnginePromise ??= (async () => {\n emitLifecycle(merged, 'transformers_js', merged.model, 'loading', {\n detail: 'loading multimodal model + processor',\n })\n // Forward each provider download event into a `loading` lifecycle report (normalized 0..1).\n const forwardedInitProgress = wrapTransformersInitProgress(merged)\n try {\n const createMultimodal =\n merged.createMultimodal ??\n (async ({ model, device, dtype, onInitProgress, sessionOptions }) => {\n const transformers = await import('@huggingface/transformers')\n const { AutoModelForImageTextToText, AutoProcessor, env } = transformers\n const load = async () => {\n const [m, processor] = await Promise.all([\n AutoModelForImageTextToText.from_pretrained(model, {\n ...(device ? { device } : {}),\n ...(dtype ? { dtype } : {}),\n ...(onInitProgress ? { progress_callback: onInitProgress } : {}),\n // Forward ONNX Runtime session options verbatim (e.g. preferredOutputLocation,\n // graphOptimizationLevel). A reachable lever, not auto-applied — see options doc.\n ...(sessionOptions ? { session_options: sessionOptions } : {}),\n } as never) as unknown as Promise,\n AutoProcessor.from_pretrained(model) as unknown as Promise,\n ])\n return { model: m, processor }\n }\n // When a custom model source is configured, serve files through it (OPFS / bundled / etc.)\n // behind the global-`env` mutex; otherwise load straight from HF (unchanged path).\n return merged.modelSource\n ? withModelSource(env as never, merged.modelSource, load)\n : load()\n })\n // `from_pretrained` covers both fetch (reported via progress_callback → `loading`) and the\n // ONNX-graph / WebGPU-WASM warmup. Mark the latter as `compiling` — a COARSE upper-bound marker\n // (fetch + compile overlap inside the call, so this is \"now preparing the graph\", not a strict\n // post-download boundary).\n emitLifecycle(merged, 'transformers_js', merged.model, 'compiling', {\n detail: 'compiling multimodal model graph',\n })\n const mmSessionOptions = resolveSessionOptions(merged)\n const engine = await createMultimodal({\n model: merged.model,\n device: merged.device,\n dtype: merged.dtype,\n onInitProgress: forwardedInitProgress,\n ...(mmSessionOptions ? { sessionOptions: mmSessionOptions } : {}),\n })\n this.#mmEngine = engine\n emitLifecycle(merged, 'transformers_js', merged.model, 'ready', {\n detail: 'multimodal model + processor ready',\n // Surface the WebGPU budget so the consumer can relate its context window to the device's\n // per-allocation ceiling — observability, never an imposed cap. Absent on non-WebGPU runtimes.\n gpuBudget: await probeGpuBudget(),\n })\n return engine\n } catch (err) {\n this.#mmEnginePromise = undefined\n emitLifecycle(merged, 'transformers_js', merged.model, 'error', {\n error: err,\n })\n throw new E_TRANSFORMERS_JS_STREAM_ERROR([\n `could not load the transformers.js multimodal model: ${isError(err) ? err.message : String(err)} — install the peer dependency (pnpm add @huggingface/transformers)`,\n ])\n }\n })()\n return this.#mmEnginePromise\n }\n\n async #resolvePipeline(merged: TransformersJsAdapterOptions): Promise {\n if (merged.pipeline) {\n this.#pipeline = merged.pipeline\n return merged.pipeline\n }\n if (this.#pipeline) return this.#pipeline\n this.#pipelinePromise ??= (async () => {\n emitLifecycle(merged, 'transformers_js', merged.model, 'loading', {\n detail: 'loading text-generation pipeline',\n })\n const forwardedInitProgress = wrapTransformersInitProgress(merged)\n try {\n const createPipeline =\n merged.createPipeline ??\n (async ({ model, device, dtype, onInitProgress, sessionOptions }) => {\n const transformers = await import('@huggingface/transformers')\n const { pipeline, env } = transformers\n const load = async () =>\n (await pipeline('text-generation', model, {\n ...(device ? { device } : {}),\n ...(dtype ? { dtype } : {}),\n ...(onInitProgress ? { progress_callback: onInitProgress } : {}),\n // Forward ONNX Runtime session options verbatim (e.g. preferredOutputLocation,\n // graphOptimizationLevel). A reachable lever, not auto-applied — see options doc.\n ...(sessionOptions ? { session_options: sessionOptions } : {}),\n } as never)) as unknown as TransformersJsPipeline\n return merged.modelSource\n ? withModelSource(env as never, merged.modelSource, load)\n : load()\n })\n // `from_pretrained` covers both fetch (reported via progress_callback → `loading`) and the\n // ONNX-graph / WebGPU-WASM warmup. Mark the latter as `compiling` — a COARSE upper-bound marker\n // (fetch + compile overlap inside the call, so this is \"now preparing the graph\", not a strict\n // post-download boundary).\n emitLifecycle(merged, 'transformers_js', merged.model, 'compiling', {\n detail: 'compiling text-generation graph',\n })\n const pipelineSessionOptions = resolveSessionOptions(merged)\n const pipe = await createPipeline({\n model: merged.model,\n device: merged.device,\n dtype: merged.dtype,\n onInitProgress: forwardedInitProgress,\n ...(pipelineSessionOptions ? { sessionOptions: pipelineSessionOptions } : {}),\n })\n this.#pipeline = pipe\n emitLifecycle(merged, 'transformers_js', merged.model, 'ready', {\n detail: 'text-generation pipeline ready',\n // Surface the WebGPU budget so the consumer can relate its context window to the device's\n // per-allocation ceiling — observability, never an imposed cap. Absent on non-WebGPU runtimes.\n gpuBudget: await probeGpuBudget(),\n })\n return pipe\n } catch (err) {\n this.#pipelinePromise = undefined\n emitLifecycle(merged, 'transformers_js', merged.model, 'error', {\n error: err,\n })\n throw new E_TRANSFORMERS_JS_STREAM_ERROR([\n `could not load the transformers.js pipeline: ${isError(err) ? err.message : String(err)} — install the peer dependency (pnpm add @huggingface/transformers)`,\n ])\n }\n })()\n return this.#pipelinePromise\n }\n\n /**\n * Build the transformers.js `generate` kwargs from the merged options (excluding tools/streamer).\n *\n * @remarks\n * Every sampling/length knob is passed EXPLICITLY with a deterministic-friendly default (resolved via\n * the shared {@link resolveGenerationOptions} from the portable contract) so the downstream `generate`\n * never falls back to the model config's own guess — the source of per-model surprises. Greedy maps to\n * `do_sample:false`; `'top-k'`/`'top-p'` map to `do_sample:true` + the matching cutoff.\n */\n #generateKwargs(merged: TransformersJsAdapterOptions): Record {\n const gen = this.#gen(merged)\n const doSample = gen.sampler !== 'greedy'\n const kw: Record = {\n max_new_tokens: gen.maxTokens,\n do_sample: doSample,\n repetition_penalty: merged.repetitionPenalty ?? 1.1,\n }\n // Sampler knobs are meaningful ONLY when sampling. Greedy ignores them and transformers.js logs a\n // warning if they're set — so send them only when sampling. We always send BOTH top_k and top_p (the\n // generate() call accepts both regardless of strategy; the strategy just determines which dominates),\n // keeping the sampler fully specified. seed is forwarded when provided.\n if (doSample) {\n kw.temperature = gen.temperature\n kw.top_k = gen.topK\n kw.top_p = gen.topP\n if (gen.seed !== undefined) kw.seed = gen.seed\n }\n if (merged.stopStrings !== undefined) kw.stop_strings = [...merged.stopStrings]\n return kw\n }\n\n /**\n * Produce the bound {@link DispatchExecutorFn} the `DispatchRunner` invokes.\n *\n * @param overrides - Option overrides layered above the constructor baseline (below `ctx.stash`).\n */\n executor(overrides?: Partial): DispatchExecutorFn {\n const baseline = this.#baseline\n const adapterClass = TransformersJsAdapter\n const self = this\n return async (ctx: DispatchContext, helpers: DispatchExecutorHelpers): Promise => {\n // 1. Three-layer merge + re-validate.\n const stashRaw = ctx.stash.get(adapterClass.STASH_KEY, {}) as unknown\n const stashOverrides =\n stashRaw && typeof stashRaw === 'object'\n ? (stashRaw as Partial)\n : {}\n const merged = validateOptions(mergeOptions(baseline, overrides, stashOverrides))\n const h = resolveHelpers(merged.helpers)\n const selfIdentity = merged.selfIdentity ?? 'assistant'\n const unsupportedMediaPolicy = merged.unsupportedMediaPolicy ?? 'throw'\n const toolCallParser = resolveToolCallParser(merged.toolCallParser)\n const reasoningParser = resolveReasoningParser(merged.reasoningParser, undefined, {\n orphanRecovery: merged.reasoningOrphanRecovery,\n })\n\n // 2. Artifact-reader tools are forged by the DispatchRunner CORE into `ctx.tools` before the input\n // pipeline runs (generation is a generic core concern; this battery owns only representation).\n // Read the pre-forged `ctx.tools` directly — no local merge, no bindContext here.\n\n // 3. Pre-render persisted tool-call results into plain-text tool message bodies.\n const renderedToolCallResults = new Map()\n for (const tc of ctx.turnToolCalls) {\n const tool = ctx.tools.get(tc.tool)\n const body = await h.renderTransformersJsToolResult({\n toolCall: tc,\n results: tc.results,\n tool: tool as Tool | ArtifactTool | undefined,\n unsupportedMediaPolicy,\n renderUntrustedContent: h.renderUntrustedContent,\n renderTrustedContent: h.renderTrustedContent,\n renderArtifactHandleBody: h.renderArtifactHandleBody,\n warn: (m) =>\n helpers.log.warn({\n kind: 'transformers-render-warning',\n message: m,\n }),\n })\n renderedToolCallResults.set(tc, body)\n }\n\n // 4. Optional context-window enforcement.\n if (merged.tokenEncoding && merged.contextWindow !== undefined) {\n const enc = merged.tokenEncoding\n const tally = (s: string): number =>\n new Tokenizable(s).estimateTokens(enc as TokenEncodingId)\n let total = tally(ctx.systemPrompt.toString())\n for (const si of ctx.standingInstructions) total += tally(si.toString())\n for (const m of ctx.turnMemories) total += tally(m.content.toString())\n for (const r of ctx.turnRetrievables) {\n total +=\n !r.inline && SpooledArtifact.isSpooledArtifact(r.content) && r.content.hasSizeHints()\n ? r.content.estimateHandleTokens(\n r.id,\n enc as TokenEncoding,\n h.renderRetrievableHandleBody\n )\n : tally((await r.contentString?.()) ?? '')\n }\n for (const m of ctx.turnMessages) total += tally(m.content?.toString() ?? '')\n for (const t of ctx.turnThoughts) total += tally(t.content.toString())\n for (const body of renderedToolCallResults.values()) total += tally(body)\n // Tool DECLARATIONS: transformers.js feeds the visible tools to `apply_chat_template({tools})`,\n // where the MODEL'S OWN Jinja template wraps them (per-model, in-process) — so the exact rendered\n // string isn't reproducible here without running the processor. Tally the serialized tool JSON\n // (the reproducible, dominant component the adapter actually passes) as an honest FLOOR. Without\n // this the guard undercounts a tool-heavy prompt by the entire declaration block.\n const visibleTools = ctx.tools.visible()\n if (visibleTools.length > 0) {\n total += tally(JSON.stringify(h.toolsToTransformersJsTools(visibleTools)))\n }\n if (total > merged.contextWindow) {\n throw new E_TRANSFORMERS_JS_CONTEXT_OVERFLOW([\n total,\n merged.contextWindow,\n String(enc),\n `system+buckets+timeline+tools=${total}`,\n ])\n }\n }\n\n // 5. Build the transformers.js message array + tools (+ decoded media when multimodal).\n const mmFlags = self.#multimodalFlags(merged)\n const {\n messages: turnMessages,\n tools: toolDefs,\n images: mmImages,\n audio: mmAudio,\n } = await h.buildTransformersJsMessages({\n systemPrompt: ctx.systemPrompt,\n standingInstructions: ctx.standingInstructions,\n memories: ctx.turnMemories,\n retrievables: ctx.turnRetrievables,\n messages: ctx.turnMessages,\n thoughts: ctx.turnThoughts,\n toolCalls: ctx.turnToolCalls,\n tools: ctx.tools,\n renderedToolCallResults,\n bucketOrder: merged.bucketOrder ?? [\n 'standingInstructions',\n 'memories',\n 'retrievables',\n 'timeline',\n ],\n selfIdentity,\n thoughtSurfacing: merged.thoughtSurfacing ?? 'all-self',\n replayCompatibility: merged.replayCompatibility ?? [],\n toolsToTransformersJsTools: h.toolsToTransformersJsTools,\n renderThought: h.renderThought,\n filterThoughts: h.filterThoughts,\n renderUntrustedContent: h.renderUntrustedContent,\n renderTrustedContent: h.renderTrustedContent,\n renderChatCompletionsSystemPrompt: h.renderChatCompletionsSystemPrompt,\n renderStandingInstructions: h.renderStandingInstructions,\n renderMemories: h.renderMemories,\n renderRetrievables: h.renderRetrievables,\n renderRetrievableSafetyDirective: h.renderRetrievableSafetyDirective,\n renderFirstPartyRetrievables: h.renderFirstPartyRetrievables,\n renderThirdPartyPublicRetrievables: h.renderThirdPartyPublicRetrievables,\n renderThirdPartyPrivateRetrievables: h.renderThirdPartyPrivateRetrievables,\n renderRetrievableHandleBody: h.renderRetrievableHandleBody,\n multimodal: mmFlags,\n decodeMedia: mmFlags ? (media) => defaultMediaToTransformersInput(media) : undefined,\n unsupportedMediaPolicy,\n warn: (m) =>\n helpers.log.warn({\n kind: 'transformers-history-warning',\n message: m,\n }),\n })\n\n const spoolStore = merged.spoolStore ?? new InMemorySpoolStore()\n const stream = merged.stream ?? true\n\n // One id for this whole generation — correlates the TO tap (onPromptAssembled) with the FROM tap\n // (onRawGeneration) and the reported message; both dispatch paths below reuse it.\n const dispatchStreamId = uuidv6()\n\n // Prompt-assembled observability tap: the EXACT messages + tools going TO the model, the instant\n // assembly finished (above) and before the pipeline/generate dispatch (below). Mirror of\n // onRawGeneration. Handed back AS-IS — no redaction — and swallow observer errors so it can never\n // corrupt the generation path.\n if (merged.onPromptAssembled) {\n try {\n merged.onPromptAssembled({\n battery: 'transformers_js',\n kind: 'rendered-prompt',\n messages: turnMessages,\n tools: toolDefs,\n streamed: stream,\n streamId: dispatchStreamId,\n })\n } catch {\n /* observer errors are non-fatal */\n }\n }\n const gen = self.#gen(merged)\n const generateKwargs = self.#generateKwargs(merged)\n const toolNames = ctx.tools.visible().map((t) => t.name)\n // A short, user-facing note carried on a GPU-OOM error so the application can show WHY it failed\n // and what to change. We surface the budget/window relationship, never silently cap it.\n const oomNote =\n merged.contextWindow !== undefined\n ? `The configured context window (${merged.contextWindow} tokens, max ${gen.maxTokens} output) exceeded the available GPU memory. Reduce the context window or max output tokens and retry, recycle the adapter to free the WebGPU buffer cache, or switch to a smaller model.`\n : `The request exceeded the available GPU memory. Reduce the context window or max output tokens and retry, recycle the adapter to free the WebGPU buffer cache, or switch to a smaller model.`\n\n // 6. Resolve the engine: multimodal model+processor, or the text-generation pipeline.\n let pipe: TransformersJsPipeline | undefined\n let mmEngine: { model: TransformersJsModel; processor: TransformersJsProcessor } | undefined\n try {\n if (mmFlags) mmEngine = await self.#resolveMultimodalEngine(merged)\n else pipe = await self.#resolvePipeline(merged)\n } catch (err) {\n // A cold WebGPU load can itself exhaust the device (\"Failed to create session\"). Surface that as\n // the typed GPU-OOM so the consumer gets one consistent, catchable signal for load- and\n // generation-time exhaustion alike; otherwise pass through the (already-typed) stream error.\n const loadMsg = isError(err) ? err.message : String(err)\n ctx.nack(\n isGpuOutOfMemoryError(loadMsg)\n ? toGenerationError(err, oomNote)\n : isInstanceOf(err, 'E_TRANSFORMERS_JS_STREAM_ERROR', E_TRANSFORMERS_JS_STREAM_ERROR)\n ? err\n : new E_TRANSFORMERS_JS_STREAM_ERROR([loadMsg])\n )\n return\n }\n\n if (ctx.abortSignal.aborted) return\n\n // ── Tool execution + persistence (args already an object — no JSON.parse) ──\n const executeAndPersistToolCall = async (call: AssembledToolCall): Promise => {\n const tool = ctx.tools.get(call.name)\n const completedAt = nowIso()\n if (!call.argsWellFormed) {\n const toolName = normalizeToolName(call.name)\n const err = new E_TRANSFORMERS_JS_INVALID_TOOL_CALL_ARGS([\n 'must be a JSON object',\n JSON.stringify(call.args),\n ])\n const results = new Tokenizable(err.message)\n helpers.reportToolCall(call.id, { tool: toolName, args: {} })\n helpers.reportToolCall(call.id, {\n results,\n isError: true,\n isComplete: true,\n })\n await ctx.storeToolCall(\n new ToolCall({\n id: call.id,\n tool: toolName,\n args: {},\n checksum: computeChecksum(toolName, {}),\n isComplete: true,\n isError: true,\n results,\n createdAt: completedAt,\n updatedAt: completedAt,\n completedAt,\n })\n )\n return\n }\n if (!tool) {\n const toolName = normalizeToolName(call.name)\n const available = ctx.tools\n .all()\n .map((t) => t.name)\n .sort()\n const errText =\n available.length > 0\n ? `Tool not found: ${toolName}. Available tools: ${available.join(', ')}.`\n : `Tool not found: ${toolName}. No tools are available this turn.`\n const results = new Tokenizable(errText)\n helpers.reportToolCall(call.id, { tool: toolName, args: call.args })\n helpers.reportToolCall(call.id, {\n results,\n isError: true,\n isComplete: true,\n })\n await ctx.storeToolCall(\n new ToolCall({\n id: call.id,\n tool: toolName,\n args: call.args,\n checksum: computeChecksum(toolName, call.args),\n isComplete: true,\n isError: true,\n results,\n createdAt: completedAt,\n updatedAt: completedAt,\n completedAt,\n })\n )\n return\n }\n helpers.reportToolCall(call.id, { tool: tool.name, args: call.args })\n const isArtifactTool = ArtifactTool.isArtifactTool(tool)\n let results: Tokenizable | SpooledArtifact | SpooledArtifact[] | Media | Media[] =\n new Tokenizable('')\n let toolHadError = false\n try {\n const raw = await tool.executor(ctx)(call.args)\n if (isArtifactTool) {\n results = Tokenizable.isTokenizable(raw)\n ? raw\n : typeof raw === 'string'\n ? new Tokenizable(raw)\n : (() => {\n throw new Error(\n `ArtifactTool \"${tool.name}\" returned a non-string/non-Tokenizable value`\n )\n })()\n } else if (Media.isMedia(raw)) {\n results = raw\n } else if (Array.isArray(raw) && raw.length > 0 && raw.every((m) => Media.isMedia(m))) {\n results = raw as Media[]\n } else if (looksLikeSpooledArtifact(raw)) {\n results = raw as SpooledArtifact\n } else if (typeof raw === 'string' || isInstanceOf(raw, 'Uint8Array', Uint8Array)) {\n const reader = await spoolStore.write(call.id, raw as string | Uint8Array)\n const ArtifactCtor = (tool as Tool).artifactConstructor?.() ?? SpooledArtifact\n results = new ArtifactCtor(reader)\n } else {\n const reader = await spoolStore.write(call.id, String(raw))\n const ArtifactCtor = (tool as Tool).artifactConstructor?.() ?? SpooledArtifact\n results = new ArtifactCtor(reader)\n }\n } catch (err) {\n toolHadError = true\n let detailMsg = isError(err) ? err.message : String(err)\n if (isError(err) && isError(err.cause) && err.cause.message !== err.message) {\n detailMsg = `${detailMsg} ${err.cause.message}`\n }\n results = new Tokenizable(detailMsg)\n }\n helpers.reportToolCall(call.id, {\n results,\n isError: toolHadError,\n isComplete: true,\n })\n const completedAt2 = nowIso()\n await ctx.storeToolCall(\n new ToolCall({\n id: call.id,\n tool: tool.name,\n args: call.args,\n checksum: computeChecksum(tool.name, call.args),\n isComplete: true,\n isError: toolHadError,\n results,\n fromArtifactTool: isArtifactTool,\n // ArtifactTool results are the documented exception: they inline the slice the model queried\n // from a prior artifact (handing back a handle to a query result would be recursion). Every\n // other result keeps the secure default (inline:false → handle).\n inline: isArtifactTool,\n createdAt: completedAt2,\n updatedAt: completedAt2,\n completedAt: completedAt2,\n })\n )\n }\n\n const assembleCalls = (raw: ReadonlyArray): AssembledToolCall[] =>\n raw.map((c) => ({\n id: uuidv6(),\n name: c.name,\n args: isObject(c.arguments) ? (c.arguments as Record) : {},\n argsWellFormed: isObject(c.arguments),\n }))\n\n // Parse the full generated text → reasoning + clean prose + tool calls, then persist.\n const finishFromText = async (\n rawText: string,\n streamId: string,\n streamedProse: boolean,\n generatedMedia: Media[] = []\n ): Promise => {\n // Normalise away non-semantic envelope/turn-boundary special tokens (Llama `<|python_tag|>`/\n // `<|eom_id|>`, ChatML `<|im_end|>`, …) before parsing. The streaming path decodes with\n // skip_special_tokens:false (its live prose-stop gate needs the markers), so without this the\n // parsers would see `<|python_tag|>{json}<|eom_id|>` on stream and decline — even though the\n // batch path (skip_special_tokens:true) parses the identical call. Idempotent on batch text.\n const fullText = stripEnvelopeSpecialTokens(rawText)\n const reasoned = reasoningParser(fullText)\n const afterReasoning = reasoned.cleanedText\n const parsed = toolCallParser(afterReasoning, { toolNames })\n const cleanText = parsed.cleanedText\n\n // Raw-generation observability tap: surface what the model emitted vs. what parsed, before any\n // persistence. Purely observational — swallow callback errors so a misbehaving observer can\n // never corrupt the generation path.\n if (merged.onRawGeneration) {\n try {\n merged.onRawGeneration({\n rawText: fullText,\n cleanedText: cleanText,\n reasoning: reasoned.reasoning,\n toolCalls: parsed.calls,\n streamed: streamedProse,\n streamId,\n })\n } catch {\n /* observer errors are non-fatal */\n }\n }\n\n // Persist reasoning as Thoughts. Drop any trace whose trimmed content is empty — an\n // empty/whitespace thought (e.g. a model's `\\n\\n` no-think artifact) carries no\n // information and is just a model quirk; there is no point surfacing it to the consumer.\n for (const trace of reasoned.reasoning) {\n if (trace.trim().length === 0) continue\n const id = uuidv6()\n helpers.reportThought(id, trace, { isComplete: true })\n await ctx.storeThought(\n new Thought({\n id,\n content: trace,\n identity: selfIdentity,\n createdAt: nowIso(),\n updatedAt: nowIso(),\n })\n )\n }\n\n // Persist the assistant message when there is clean prose OR generated media to carry. Media-only\n // turns (empty text + an audio/image attachment) are legitimate; the contract requires at least one\n // of `content`/`attachments`, so a turn with neither stores nothing (unchanged from before).\n if (cleanText.length > 0 || generatedMedia.length > 0) {\n if (streamedProse) {\n helpers.reportMessage(streamId, '', { isComplete: true })\n } else if (cleanText.length > 0) {\n helpers.reportMessage(streamId, cleanText, { isComplete: true })\n }\n await ctx.storeMessage(\n new Message({\n id: streamId,\n role: 'assistant',\n ...(cleanText.length > 0 ? { content: cleanText } : {}),\n ...(generatedMedia.length > 0 ? { attachments: generatedMedia } : {}),\n identity: selfIdentity,\n createdAt: nowIso(),\n updatedAt: nowIso(),\n })\n )\n }\n\n // Execute tool calls.\n const calls = assembleCalls(parsed.calls)\n if (calls.length === 0) {\n if (merged.autoAck) ctx.ack()\n return\n }\n for (const call of calls) {\n if (ctx.abortSignal.aborted) return\n await executeAndPersistToolCall(call)\n }\n }\n\n // ── Streaming path ──\n // Unified generate: drives either the text-generation pipeline OR the multimodal model+processor.\n // Returns the final decoded text for the non-streaming path; streaming text arrives via `streamer`.\n // The raw generation object (model.generate / pipeline output) is captured here so the optional\n // `extractMediaOutputs` hook can surface GENERATED media (audio/image) as assistant attachments.\n let rawGenerationResult: unknown\n const runGenerate = async (streamer: unknown): Promise => {\n if (mmEngine) {\n const { model, processor } = mmEngine\n const proc = processor as unknown as {\n apply_chat_template: (m: unknown, o: unknown) => unknown\n batch_decode: (t: unknown, o: unknown) => string[]\n }\n const callProc = processor as unknown as (\n ...args: unknown[]\n ) => Promise>\n const prompt = proc.apply_chat_template(turnMessages, {\n add_generation_prompt: true,\n tokenize: false,\n // Explicit thinking flag — never let the template default decide (Qwen3/DeepSeek default ON).\n enable_thinking: gen.enableThinking,\n // Pass tool DEFINITIONS through the native template mechanism, exactly as the pipeline path\n // does. Gemma 4's chat_template renders these as `<|tool>…` blocks, which is what\n // cues the model to emit its TRAINED `call:NAME{…}` tool-call format. Omitting them (the\n // prior bug) left the model with no native cue, so it improvised raw JSON args that no\n // parser recognises. This is the multimodal-path equivalent of the pipeline's `tools` arg.\n ...(toolDefs.length > 0 ? { tools: toolDefs } : {}),\n })\n // processor(text, images, audio, options) — positional (verified against the real Gemma-4\n // `Gemma4Processor._call(text, images = null, audio = null, options)`). The audio slot is THIRD,\n // so when audio is present the images slot MUST be filled positionally (with `null` when there\n // is no image) — otherwise audio collapses into the images slot and the image processor throws\n // `image.rgb is not a function`. `if (images)`/`if (audio)` in the processor treat `null` as absent.\n const imageArg =\n mmImages.length === 0 ? null : mmImages.length === 1 ? mmImages[0] : mmImages\n const audioArg = mmAudio.length === 0 ? null : mmAudio.length === 1 ? mmAudio[0] : mmAudio\n const procArgs: unknown[] =\n mmAudio.length > 0\n ? [prompt, imageArg, audioArg] // audio needs the 3rd slot → fill images (null if none)\n : mmImages.length > 0\n ? [prompt, imageArg]\n : [prompt]\n const inputs = await callProc(...procArgs)\n // The processor INPUT tensors are GPU buffers we own. Free them no matter what — including\n // when generate() itself OOMs — so a failed generate doesn't leak its inputs and starve the\n // retry. (generate() is INSIDE this try precisely so the finally also covers its throw.)\n try {\n const out = await (\n model as unknown as { generate: (o: unknown) => Promise }\n ).generate({\n ...inputs,\n ...generateKwargs,\n ...(streamer ? { streamer } : {}),\n })\n rawGenerationResult = out\n // Non-stream decode: slice the prompt tokens off, decode the new tail.\n try {\n const inputLen = (inputs.input_ids as { dims?: number[] } | undefined)?.dims?.[1] ?? 0\n const seq = out as { slice?: (...a: unknown[]) => unknown }\n const newTokens =\n typeof seq.slice === 'function' ? seq.slice(null, [inputLen, null]) : out\n const decoded = proc.batch_decode(newTokens, {\n skip_special_tokens: true,\n })\n // Free the slice tensor (a fresh GPU buffer) once decoded — but only when it's distinct\n // from `out`, which we still need for the media extractor (disposed after this returns).\n if (newTokens !== out) disposeTensors(newTokens)\n return (decoded?.[0] ?? '').toString()\n } catch {\n return ''\n }\n } finally {\n disposeTensors(inputs)\n }\n }\n const p = pipe as unknown as (m: unknown, k: unknown) => Promise\n const output = await p(turnMessages, {\n ...generateKwargs,\n // Explicit thinking flag forwarded to the pipeline's internal apply_chat_template — never let\n // the template default decide (Qwen3/DeepSeek default thinking ON).\n enable_thinking: gen.enableThinking,\n ...(toolDefs.length > 0 ? { tools: toolDefs } : {}),\n ...(streamer ? { streamer } : {}),\n })\n rawGenerationResult = output\n return extractGeneratedText(output)\n }\n\n // Run the optional media-output extractor over the raw generation result, persisting each generated\n // media via `ctx.storeMediaBytes` and building first-party `Media` attachments. Returns [] when no\n // hook is configured or it yields nothing — the text-only path then attaches nothing (unchanged).\n const collectGeneratedMedia = async (): Promise => {\n if (!merged.extractMediaOutputs || rawGenerationResult === undefined) return []\n const outputs = await merged.extractMediaOutputs(rawGenerationResult)\n const media: Media[] = []\n for (const o of outputs) {\n const id = uuidv6()\n const reader = await ctx.storeMediaBytes(id, o.bytes)\n media.push(\n Media.toolGenerated({\n id,\n kind: o.kind,\n mimeType: o.mimeType,\n filename: o.filename ?? `${id}.${o.kind}`,\n reader,\n })\n )\n }\n return media\n }\n // Free the captured generate OUTPUT tensor after the media extractor (the only consumer of\n // rawGenerationResult) has run. Together with disposing the processor inputs in runGenerate,\n // this releases the caller-owned tensors per generate() — hygiene for the manual multimodal\n // path, which (unlike the pipeline path) hands back tensors we own. Only the multimodal path\n // captures a tensor here; the pipeline path returns plain objects, for which this is a no-op.\n const disposeGenerationOutput = (): void => {\n if (mmEngine && rawGenerationResult !== undefined) disposeTensors(rawGenerationResult)\n rawGenerationResult = undefined\n }\n\n if (stream) {\n const accumulator = h.createTransformersJsStreamAccumulator()\n const streamId = dispatchStreamId\n let proseStopped = false\n let streamedProse = false\n\n // The decoded-text sink: feed the accumulator + stream safe prose deltas (stopping prose once\n // tool-call/think markup appears; the clean message is persisted after generation completes).\n const onText = (text: string): void => {\n accumulator.feed(text)\n if (proseStopped) return\n if (TEXT_MARKUP_MARKERS.some((m) => accumulator.content().includes(m))) {\n proseStopped = true\n return\n }\n if (text.length > 0) {\n streamedProse = true\n helpers.reportMessage(streamId, text)\n }\n }\n\n // Default streamer factory imports the peer's TextStreamer; `createStreamer` overrides it\n // (e.g. tests inject a lightweight sink to avoid importing the heavy peer in the browser env).\n // The tokenizer comes from the multimodal processor when present, else the pipeline.\n const tokenizerHost = (mmEngine?.processor ?? pipe) as unknown as {\n tokenizer: unknown\n }\n const createStreamer =\n merged.createStreamer ??\n (async ({ onText: cb }) => {\n const { TextStreamer } = await import('@huggingface/transformers')\n return new TextStreamer(\n tokenizerHost.tokenizer as never,\n {\n skip_prompt: true,\n skip_special_tokens: false,\n callback_function: cb,\n } as never\n )\n })\n\n let streamer: unknown\n try {\n streamer = await createStreamer({\n pipeline: pipe as TransformersJsPipeline,\n onText,\n })\n } catch (err) {\n emitLifecycle(merged, 'transformers_js', merged.model, 'error', {\n error: err,\n })\n ctx.nack(new E_TRANSFORMERS_JS_STREAM_ERROR([isError(err) ? err.message : String(err)]))\n return\n }\n\n emitLifecycle(merged, 'transformers_js', merged.model, 'generating')\n try {\n await runGenerate(streamer)\n } catch (err) {\n if (ctx.abortSignal.aborted) return\n emitLifecycle(merged, 'transformers_js', merged.model, 'error', {\n error: err,\n })\n ctx.nack(toGenerationError(err, oomNote))\n return\n }\n if (ctx.abortSignal.aborted) return\n const streamMedia = await collectGeneratedMedia()\n disposeGenerationOutput()\n await finishFromText(accumulator.content(), streamId, streamedProse, streamMedia)\n emitLifecycle(merged, 'transformers_js', merged.model, 'complete')\n return\n }\n\n // ── Non-streaming path ──\n let finalText: string\n emitLifecycle(merged, 'transformers_js', merged.model, 'generating')\n try {\n finalText = await runGenerate(undefined)\n } catch (err) {\n emitLifecycle(merged, 'transformers_js', merged.model, 'error', {\n error: err,\n })\n ctx.nack(toGenerationError(err, oomNote))\n return\n }\n if (ctx.abortSignal.aborted) return\n const nonStreamMedia = await collectGeneratedMedia()\n disposeGenerationOutput()\n await finishFromText(finalText, dispatchStreamId, false, nonStreamMedia)\n emitLifecycle(merged, 'transformers_js', merged.model, 'complete')\n }\n }\n}\n\n/**\n * Free the GPU buffers backing a transformers.js value when running on the WebGPU EP.\n *\n * @remarks\n * onnxruntime-web does NOT garbage-collect GPU tensors — each `Tensor` whose `location` is\n * `'gpu-buffer'` owns a Dawn buffer that is reclaimed ONLY by an explicit `.dispose()`. In the\n * manual multimodal `model.generate()` path the battery creates the processor INPUT tensors and\n * captures the generate OUTPUT tensor; transformers.js frees its own internal decode-loop tensors\n * and KV cache, but these caller-owned tensors are ours to free. Skipping that leaks ~the full\n * activation set per `generate()`, so the SECOND generate on a loaded model (a tool-loop iteration,\n * the answer classifier, or simply the next turn) fails with \"Failed to allocate memory for buffer\n * mapping\". The matrix tests never caught this — each cell builds a fresh adapter and generates\n * exactly once.\n *\n * Accepts a processor-inputs object (a map of named tensors), a single tensor, or a nested\n * generate output; walks it shallowly and disposes anything tensor-shaped. Best-effort and never\n * throws — a disposal failure must not break generation. No-op for CPU/WASM tensors (which have no\n * `dispose` or aren't GPU-backed), so it's safe across execution providers.\n */\nconst disposeTensors = (value: unknown): void => {\n const tryDispose = (t: unknown): void => {\n const d = t as { dispose?: () => void; location?: string } | null | undefined\n if (d && typeof d.dispose === 'function') {\n try {\n d.dispose()\n } catch {\n /* best-effort: a failed dispose must never break generation */\n }\n }\n }\n if (value === null || value === undefined) return\n // A bare tensor (has its own dispose).\n if (typeof (value as { dispose?: unknown }).dispose === 'function') {\n tryDispose(value)\n return\n }\n // A processor-inputs map { input_ids, attention_mask, pixel_values, … } or an array of tensors.\n if (typeof value === 'object') {\n for (const v of Object.values(value as Record)) tryDispose(v)\n }\n}\n\n/**\n * Pull the newly-generated assistant text out of a transformers.js text-generation result.\n *\n * @remarks\n * Chat input → `[{ generated_text: Message[] }]` (the last message is the new assistant turn);\n * string input → `[{ generated_text: string }]`. We always send chat input, so we take the last\n * message's content, falling back defensively to a string `generated_text`.\n */\nconst extractGeneratedText = (output: unknown): string => {\n const first = Array.isArray(output) ? output[0] : output\n const gen = (first as { generated_text?: unknown } | undefined)?.generated_text\n if (typeof gen === 'string') return gen\n if (Array.isArray(gen)) {\n const last = gen[gen.length - 1] as { content?: unknown } | undefined\n const content = last?.content\n if (typeof content === 'string') return content\n if (Array.isArray(content)) {\n return content\n .filter(\n (i): i is { type: string; text: string } =>\n isObject(i) && (i as { type?: unknown }).type === 'text'\n )\n .map((i) => i.text)\n .join('')\n }\n }\n return ''\n}\n\nexport { extractGeneratedText as __extractGeneratedText 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