Sanitize public model card metadata

README.md

@@ -1,47 +1,18 @@
 ---
-license: other
-license_name: nvidia-open-model-license
-base_model: nvidia/nv-embedcode-7b-v1
-tags: [qhexrt, hexagon, npu, qualcomm, embedding, code, on-device, v81, snapdragon, 7b]
-library_name: qhexrt
-pipeline_tag: feature-extraction
+license: "other"
+tags:
+- "hnpu"
+- "hexagon"
+- "npu"
+- "embedding"
+base_model: "nvidia/nv-embedcode-7b-v1"
+pipeline_tag: "sentence-similarity"
 ---
 
-# NV-EmbedCode-7B → Hexagon NPU (QHexRT, v81)
+# nv embedcode 7b HNPU
 
-On-device **code-retrieval embedding** bundle of **[nvidia/nv-embedcode-7b-v1](https://huggingface.co/nvidia/nv-embedcode-7b-v1)**
-for the **Qualcomm Hexagon v81 NPU** (SM8850), runnable with the [QHexRT](https://github.com/RunanywhereAI)
-C++ runtime. **No Python in the hot path.**
+Prebuilt HNPU artifacts for [nvidia/nv-embedcode-7b-v1](https://huggingface.co/nvidia/nv-embedcode-7b-v1), a public embedding model.
 
-`MistralBiDirectionalModel` (bidirectional Mistral-7B: hidden 4096, 32 layers, 32q/8kv, head_dim 128, plain
-rope theta 10000, **full attention**) + avg-pool + L2 → a **4096-d** embedding. The 7B encoder ships as **4
-chained W8 context parts** (`llama_embed_sharded`, the encoder-split engine; 1.6 GB each ≈ 6.4 GB, fits the
-~12 GB device).
+For model behavior, license, intended use, and limitations, see the [upstream model card](https://huggingface.co/nvidia/nv-embedcode-7b-v1).
 
-## Device validation (v81, SM8850) — device-vs-reference embedding cosine
-| input | cosine |
-|---|---|
-| "function to compute fibonacci numbers" | **0.9992** |
-| "def fib(n): ... fib(n-1)+fib(n-2)" | **0.9993** |
-| "SELECT * FROM users WHERE age > 30" | **0.9993** |
-| "binary search implementation in python" | **0.9993** |
-
-W8 7B vs the fp32 reference — **near bit-faithful** (≥ 0.9992). Code retrieval: cos(fib-query, fib-code)
-**0.6878** device vs **0.6872** reference, and **0.037** for an irrelevant SQL doc (correct ranking).
-~930 ms/embedding. (Reference computed via a host-rope replication validated bit-exact to HF's real
-`MistralDecoderLayer`, since the model's shipped custom forward is transformers-5.x-incompatible.)
-
-## What's inside (`v81/`)
-- `nvembedcode7b_enc_p{0..3}_w8.bin` — the 4 chained W8 encoder parts (graphs `nvembedcode7b_enc_p{k}_w8`).
-- `nvembedcode7b_embed_f16.bin` — token-embedding table (fp16, vocab 32000).
-- `tokenizer.json` — Mistral sentencepiece BPE tokenizer.
-- `nv-embedcode-7b.json` — the QHexRT manifest (host-op `llama_embed_sharded`, plain rope).
-
-## Run
-```bash
-adb push v81 /data/local/tmp/wq/nb14emb
-adb shell "cd /data/local/tmp/wq && LD_LIBRARY_PATH=. QHX_EMB_QUERY_PREFIX= QHX_EMB_DOC_PREFIX= \
-  ./qhx_embed nb14emb/nv-embedcode-7b.json libQnnHtp.so libQnnSystem.so nb14emb \
-  'function to compute fibonacci numbers' 'def fib(n): return n if n<2 else fib(n-1)+fib(n-2)'"
-```
-Embeddings over **raw text** (no prefix). A QNN context binary is **arch/QAIRT-pinned** (v81, QAIRT 2.47, soc_model 87) — won't load on v79/v83.
+Artifacts are architecture-pinned. Available artifact directories: `v81/`.