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I work on quantizing models to run efficiently on local hardware, and kept being curious how existing quants spend their "bit budget" during optimization and built a local tool to explore. Many quants apply one setting across all tensors, but some do more interesting things: the model in the screenshot holds attention K at 4.5 bits while Q/V/O get 8.5, and protects layer 0 MLP.
It turned out useful enough that I made it public: https://tensorlens.dev
Explore any HF model in the browser without downloading it. The anatomy map is read from the safetensors header via a range request, and only tensors you click ever stream. Large tensors are sampled rather than streamed in full.
Limitations: safetensors only (no GGUF yet), some exotic variants don't work yet, and gated repos aren't supported yet.
Feedback very welcome, especially models that break it.
It turned out useful enough that I made it public: https://tensorlens.dev
Explore any HF model in the browser without downloading it. The anatomy map is read from the safetensors header via a range request, and only tensors you click ever stream. Large tensors are sampled rather than streamed in full.
Limitations: safetensors only (no GGUF yet), some exotic variants don't work yet, and gated repos aren't supported yet.
Feedback very welcome, especially models that break it.
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