Sanitize public model card metadata

README.md

@@ -1,96 +1,18 @@
 ---
-license: mit
-tags: [qhexrt, moe, llm, hexagon, npu, qnn, snapdragon]
-base_model: microsoft/Phi-tiny-MoE-instruct
-pipeline_tag: text-generation
+license: "mit"
+tags:
+- "hnpu"
+- "hexagon"
+- "npu"
+- "llm"
+base_model: "microsoft/Phi-tiny-MoE-instruct"
+pipeline_tag: "text-generation"
 ---
 
-# Phi-tiny-MoE — Hexagon v79 + v81 NPU bundle (QHexRT)
+# phi tiny moe HNPU
 
-**microsoft/Phi-tiny-MoE-instruct** (PhiMoEForCausalLM, 3.8 B total / **1.1 B active**, 16 experts top-2)
-compiled to run on the **Qualcomm Hexagon v79 NPU** (Snapdragon 8 Elite / SM8750, e.g. Galaxy S25) through
-[**QHexRT**](https://github.com/) — a thin C++ QNN runtime. **First MoE in the QHexRT family.**
+Prebuilt HNPU artifacts for [microsoft/Phi-tiny-MoE-instruct](https://huggingface.co/microsoft/Phi-tiny-MoE-instruct), a public text-generation model.
 
-> A native, manifest-driven QHexRT bundle: **text in → text out** via the standard `qhx_generate` tool,
-> exactly like the other models. Every number below is a **real on-device measurement** on a Samsung S25.
+For model behavior, license, intended use, and limitations, see the [upstream model card](https://huggingface.co/microsoft/Phi-tiny-MoE-instruct).
 
-## How it runs
-A `phimoe_generate` host-op drives an **AR=1 KV-cache decode**: per layer a **GQA-native** attention+router NPU
-graph (K/V kept at 4 heads — no `repeat_interleave`/VTCM spill) → host `sparsemixer` top-2 → ONE **fused
-2-expert FFN graph** (`ffn2`: both selected experts dequantized from host-RAM int8 **in parallel** + `m1,m2` →
-`m1·SwiGLU_a + m2·SwiGLU_b` in a single execute). It reads only the **2 active experts** per token. Quant:
-**int8 experts + fp16 attention / router / lm-head** (int16 activations break attention; W4 too coarse). Fits the
-device in **4 QNN contexts** (the HTP caps concurrent contexts ≈ 8). The Phi-3 tokenizer + chat template
-(`<|user|> … <|end|><|assistant|>`) are applied on-device, so you pass plain **text**. **MAXCTX = 2048**
-(16 Q-heads → wide attention HTP-correct past the v79 512 wall — verified 12/12 vs fp32 on a 529-token
-needle-recall prompt).
-
-| Metric | Value |
-|---|---|
-| **Decode** | **~5–7 tok/s** (top-2; best ~7 cold, thermal-dependent) |
-| **Prefill / TTFT** | **~2.8 s for a 529-token prompt** (batched MoE prefill); short prompts (<24 tok) ≈1.5 s |
-| **Accuracy** | **100 % greedy parity** vs the fp32 reference — 5 prompts **+ a 529-token long-context test** |
-| **Context** | **2048** tokens |
-| **Peak RSS** | ~6 GB |
-
-> **GQA-native attention + a fused FFN** lifted decode ~2.5–3.3× over the first 2048 build (2.1 tok/s). A
-> **batched MoE prefill** (`pf_lo`/`pf_hi` + `ffn_pf`: one forward/layer over the whole prompt + an expert-grouped
-> FFN, seeding the decode KV cache) then cut the 529-token first-token latency from **~95 s → ~2.8 s (~34×)**.
-> Short prompts skip it; prompts > 576 fall back to decode-over-prompt.
-
-## Contents (`v79/`)
-| file | what | size |
-|---|---|---|
-| `phimoe.json` | the QHexRT manifest (`phimoe_generate` host-op) | small |
-| `a_lo.bin` / `a_hi.bin` | GQA-native attn+router graphs, layers 0–15 / 16–31 (KV cache, MAXCTX 2048) | 650 MB each |
-| `ffn2.bin` | fused 2-expert FFN graph (both top-2 experts + m1,m2 → 1 execute) | 108 KB |
-| `pf_lo.bin` / `pf_hi.bin` | batched-prefill attn+router graphs, layers 0–15 / 16–31 (causal, PN=576) | 659 MB each |
-| `ffn_pf.bin` | batched single-expert FFN for prefill (run once per used expert/layer) | 86 KB |
-| `lmhead_ar1.bin` | final LayerNorm + lm-head → logits | 251 MB |
-| `experts_i8.bin` | all 512 experts (32×16), per-output-channel int8 | 2.69 GB |
-| `experts_scale.f32` | int8 dequant scales | 9.8 MB |
-| `embed_f16.bin` | token embedding table (host lookup), fp16 | 251 MB |
-| `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` | Phi-3 tokenizer | small |
-
-## Run
-```bash
-# 1) download
-hf download runanywhere/phi_tiny_moe_HNPU --local-dir phi_tiny_moe_HNPU
-# 2) build qhx_generate from QHexRT for aarch64-android, push it + the QNN runtime libs
-#    (libQnnHtp.so, libQnnSystem.so, the v79 HTP skel) to /data/local/tmp/phimoe
-# 3) push this bundle
-adb push phi_tiny_moe_HNPU/v79 /data/local/tmp/phimoe         # (PowerShell on Windows — native paths)
-# 4) run — plain text in, text out
-adb shell "cd /data/local/tmp/phimoe && export ADSP_LIBRARY_PATH='/data/local/tmp/phimoe;/vendor/dsp/cdsp'; \
-  LD_LIBRARY_PATH=. ./qhx_generate phimoe.json libQnnHtp.so libQnnSystem.so . 24 'What is the capital of France?'"
-# -> "The capital of France is Paris. It is not only the largest city in France ..."
-```
-(The manifest also accepts a raw comma-separated token-id list in place of the text prompt, for exact-id repro.)
-
-## Caveats
-- **v79 only** (SM8750). Another arch = re-export (the build plane / `npu-forge`).
-- **MAXCTX 2048** (prompt + generation ≤ 2048). The decode-over-prompt prefill is ~0.46 s/prompt-token, so very
-  long prompts take minutes to the first token; decode is ~2.1 tok/s regardless of length. A faster small-window
-  variant (e.g. 512 + sliding KV ring) is a re-export (`MAXCTX=512 build_alo_ahi_2048_v2.sh`).
-- Greedy/argmax decode (temperature 0). Weight-only int8 occasionally flips a thin-margin token; output stays
-  coherent (the prior-port finding — int8 is the accuracy/memory sweet spot, W4 too coarse).
-
-Built with the `npu-forge` toolkit (weights → oracle-gated NPU graphs). Base model + tokenizer © Microsoft (MIT).
-
-## v81 (SM8850 / soc_model 87) — DECODE-ONLY
-
-Device-validated on SM8850: *"What is the capital of France?"* -> **"The capital of France is Paris. It is not
-only the country's largest city but also a global center for art"** — coherent, greedy first token 450 (= the
-PyTorch gold), **~5.7 tok/s** decode (matches v79), TTFT 1.7 s for a 10-token prompt (decode-over-prompt).
-
-**Why decode-only:** the full 7-context bundle (64 graphs incl. batched prefill) **crashes the v81 cDSP** — the
-unsigned protection-domain heap is exhausted during `Graph::setup_vtcm` in `libQnnHtpV81Skel.so` (fastrpc
-`0x8000040d` AEE_ENOMEMORY -> remoteproc-cdsp fatal, recovery disabled -> full device reboot). NOT host RAM
-(12.7 GB free). The `v81/` bundle therefore ships the **4 decode contexts only** (a_lo/a_hi/ffn2/lmhead_ar1);
-`phimoe_generate` auto-falls-back to decode-over-prompt (the batched-prefill graphs are optional). Trade-off:
-slower TTFT on long prompts; identical decode quality/speed. (v79 keeps the full batched-prefill bundle.)
-
-### Files (`v81/`)
-`phimoe.json` (decode-only manifest) · `a_lo.bin` `a_hi.bin` (decode attn+router a0..a31) · `ffn2.bin` (fused
-2-expert FFN) · `lmhead_ar1.bin` (final-norm + lm-head, input `h`) · `experts_i8.bin` (int8 experts, host) ·
-`experts_scale.f32` · `embed_f16.bin` · `tokenizer.json` (+ config/special-tokens).
+Artifacts are architecture-pinned. Available artifact directories: `v79/`, `v81/`.