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Independently converted from the pinned official checkpoint. Try the browser demo; its repository contains conversion scripts and validation evidence.
The included q4f16 model was converted directly from the official OpenBMB MiniCPM5-2B BF16 checkpoint, not from community-converted weights.
abe115e887989b14f05e64a3b260648329324c3f.14fb8e7f0a18d53d1f239773758bf581cee7e456a4523a54622c3a245b64402c.The package manifest contains file sizes and SHA-256 hashes.
Six prompts with prefill and cached decoding give 41 tested prediction positions. These are scoped conversion checks, not a broad quality evaluation.
| Comparison | Top-token agreement | Mean KL divergence |
|---|---|---|
| FP16 ONNX vs FP16 PyTorch | 41/41 | 0.00000815 |
| Int4 ONNX vs official BF16 PyTorch | 36/41 | 0.125916 |
| Packaged int4 vs our original int4 export | 41/41; all logits bit-identical | 0 |
| Browser WebGPU vs native packaged ONNX | 41/41 | 0.002517 |
Local Chromium tests on an AMD Radeon 8060S covered streaming, stopping, thinking mode, XML tool-call generation, and one retrieval fixture at 3,888 prompt tokens. Inference also passed with the GPU device capped at a 128 MiB storage binding limit and 256 MiB buffer limit. This desktop allocation test does not emulate a phone's memory pressure or thermals.
Warm local benchmark medians were about 35–42 decode tokens/s. At 1,890 prompt tokens, first-token latency improved about 9.5% against our own identical-weight control with all-position logits and unsplit embeddings. Timings depend on device, browser, prompt, and thermal state. See raw validation summaries.
Four-bit quantization has measurable quality loss. The base model also makes mistakes: one Chinese arithmetic prompt produced an incorrect answer in both the official BF16 model and the conversion. This Space is an inference demo, not an autonomous agent or a guarantee of accurate answers. Tool calls are XML; the demo does not execute them.
openbmb/MiniCPM5-2B