Model card: add context window & prefill sizes; base_model = VibeThinker-3B (128K)

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 language:
 - en
 base_model:
-- Qwen/Qwen2.5-3B
+- WeiboAI/VibeThinker-3B
 pipeline_tag: text-generation
 tags:
 - webgpu
@@ -53,6 +53,30 @@ Every win was found by **measuring** — nanosecond GPU timestamp profiling —
 | Footprint | one static HTML page; weights supplied by the visitor (BYO-model) |
 | Privacy | absolute — inference never leaves the device |
 
+## Context window & prefill sizes
+
+The base model — [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B), a Qwen2.5-architecture 3B reasoning model (from Qwen2.5-Coder-3B) — supports **131072 (128K) positions** with a **32K sliding window**, and is built to *think long*: its generation config defaults to `max_new_tokens=65536`, and the authors suggest **60K–100K tokens** for the hardest problems. So context length is a first-class concern here, not an afterthought.
+
+The runtime exposes context + prefill as options:
+
+```js
+const rt = new QwenWGPU(device, QWEN25_3B, { maxCtx: 8192, maxPrefillT: 8192 });
+```
+
+- **`maxCtx`** — the context window (KV-cache length). Decode attention is **split-K** and prefill attention is **flash / online-softmax** (O(block) workgroup memory, not O(ctx)), so neither caps out at small sizes — context scales until you run out of VRAM.
+- **`maxPrefillT`** — the largest prompt processed in one batched (tiled-int4-GEMM) prefill pass. Longer prompts (or prefill while a LoRA adapter is active) fall back to the sequential path; clamped to `maxCtx`.
+
+Defaults are **8192 / 8192** — ample for the bug-bounty triage adapter (its chain-of-thought runs a few thousand tokens) at a modest footprint. Raise them toward the base model's 128K as memory allows. **The KV cache is the cost**, and it grows linearly (~72 KB per token of context, f32, across all 36 layers):
+
+| context (`maxCtx`) | KV cache (f32) |
+|---|---|
+| 8 192 *(default)* | ~0.6 GB |
+| 16 384 | ~1.2 GB |
+| 32 768 *(sliding window)* | ~2.4 GB |
+| 131 072 *(max positions)* | ~9.4 GB |
+
+Plus ~2 GB of int4/int8 weights and lazily-sized prefill scratch. **Verified in-browser:** batched prefill is bit-exact to the sequential path through ctx 1024; runs end-to-end at 4 096 / 8 192; and a `maxCtx: 16384` build prefills a 9 000-token prompt and decodes past it. (KV is f32 today — quantizing it would roughly halve these numbers.)
+
 ## Note on weights
 
 **This page hosts no multi-GB weights.** Emberglass is the *engine*; it is bring-your-own-model. Point it at a Qwen2.5-3B (or compatible) checkpoint served locally and it quantizes to int4 on the way to the GPU. Drag in a PEFT/MLX LoRA adapter to hot-swap a specialization live.