README.md

metadata

title: VibeThinker WebGPU Train + Hot-Swap LoRA
emoji: 🔥
colorFrom: red
colorTo: yellow
sdk: static
app_file: index.html
pinned: false
license: apache-2.0
models:
  - WeiboAI/VibeThinker-3B
short_description: In-browser WebGPU LoRA training + hot-swap (VibeThinker-3B)

VibeThinker-3B · in-browser WebGPU training + hot-swap LoRA

A static Space that runs VibeThinker-3B entirely in your browser tab on a custom WebGPU int4 runtime — no server, no upload, nothing leaves the page.

What it shows off:

• WebGPU kernels — int4 GEMV/GEMM, fused RMSNorm+QKV+RoPE, paged attention, f32 accumulation in f16 paths. The whole forward + backward stack is WGSL.
• In-browser training — fine-tune a LoRA adapter in the tab with a real backward pass + AdamW over the frozen int4 base (gradient checkpointing, f32 master weights). The guided demo teaches made-up facts in ~30s.
• Hot-swap LoRA — every trained adapter is saved locally (IndexedDB) and hot-swaps into inference instantly; export to .safetensors or re-load later.

The model weights are streamed once from WeiboAI/VibeThinker-3B on the Hub and cached in your browser. A Space does not have to be tied to a model; the model above is linked for discovery only.

Requires a WebGPU-capable browser (recent Chrome/Edge; Safari Technology Preview). ~6 GB weights, one-time download.

Source: https://github.com/maceip/vibethinker-webgpu-lora