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@cccclai cccclai commented Nov 24, 2025

Added hardware support details and memory limit error handling instructions.

It seems like lots of users try to use the llama flow on other hardware other than phones. Let's try to document it first

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@meta-cla meta-cla bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Nov 24, 2025
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Thanks to make document better!


We’ve validated this flow on the **Samsung Galaxy S23**, **Samsung Galaxy S24**, and **OnePlus 12**.
Support on other hardware depends on the **HTP architecture (HtpArch)**.
The **16a4w_block** format and **weight sharing between prefill and decode** are supported on **V73 and newer**.
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Minimum requirement of LPBQ is v69.
Minimum requirement of weight sharing is v73
Minimum requirement of 16bit activation and 16 bit weight for matmul is V73. (16 bit kv)

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Updated, what do you think?

For older devices, you may need to **retune the quantization recipe**. A good starting point is:

- Use **16a4w**
- Optionally apply **SpinQuant** for better stability and accuracy
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Partially layers apply 16a8w to get better accuracy.

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Updated

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LGTM. Thanks!

Added hardware support details and memory limit error handling instructions.
Updated hardware support section to include Samsung Galaxy S25.
@cccclai cccclai merged commit 60a2bd6 into main Dec 1, 2025
165 checks passed
@cccclai cccclai deleted the cccclai-patch-12 branch December 1, 2025 21:48
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