QHexRT launch: 100% NPU, multimodal roadmap, remove open-source refs

README.md

@@ -19,9 +19,13 @@ pipeline_tag: text-generation
 # LFM2.5-230M — Hexagon v81 NPU (QHexRT)
 
 [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) running fully on the **Qualcomm Hexagon
-v81 NPU** (Snapdragon 8 Elite Gen-2 / SM8850) via the **[QHexRT](https://github.com/RunanywhereAI/QHexRT)**
-runtime — **no Python in the hot path**. W8 weight-only, GQA-native decode, batched prefill, on-NPU lm-head.
-Greedy output is identical to the source model (`"The capital of France is"` → `" Paris."`).
+v81 NPU** (Snapdragon 8 Elite Gen-2 / SM8850) via **QHexRT** — RunAnywhere's inference engine for
+Qualcomm NPUs. **100% on the HTP.** No Python in the hot path. W8 weight-only, GQA-native decode,
+batched prefill, on-NPU lm-head. Greedy output is identical to the source model
+(`"The capital of France is"` → `" Paris."`).
+
+QHexRT is the first engine built to run **LLM, VLM, STT, TTS, and embeddings** fully on Qualcomm
+Hexagon NPUs. LFM 2.5 230M is the first model in the catalog.
 
 ## Why the NPU — measured on SM8850 (vs llama.cpp CPU, same device)
 
@@ -34,7 +38,7 @@ Greedy output is identical to the source model (`"The capital of France is"` →
 Batched **O(1) prefill** holds TTFT flat at **~36 ms regardless of prompt length**, so the NPU pulls further
 ahead the longer the context — at far lower power than driving 8 CPU cores at max clock.
 
-**Full benchmark write-up:** [runanywhere.ai/blog](https://www.runanywhere.ai/blog/qhexrt-npu-vs-cpu-lfm2.5-230m-qualcomm-hexagon) · [article draft in this repo](blog/qhexrt-npu-vs-cpu-lfm2.5-230m.md)
+**Full launch write-up:** [runanywhere.ai/blog](https://www.runanywhere.ai/blog/qhexrt-npu-vs-cpu-lfm2.5-230m-qualcomm-hexagon) · [article draft in this repo](blog/qhexrt-npu-vs-cpu-lfm2.5-230m.md)
 
 ## Run
 
@@ -46,8 +50,8 @@ adb shell "cd /data/local/tmp/lfm230 && LD_LIBRARY_PATH=. \
 ```
 
 Stage the QAIRT v81 runtime libs (`libQnnHtp.so`, `libQnnSystem.so`, `libQnnHtpV81Skel.so`/`Stub.so`) + the
-`qhx_generate` tool into the same dir (from the QAIRT SDK; see the
-[QHexRT deploy docs](https://github.com/RunanywhereAI/QHexRT)). Context binaries are **arch-pinned to v81**.
+`qhx_generate` tool into the same dir (from the QAIRT SDK). Context binaries are **arch-pinned to v81**.
+Contact [san@runanywhere.ai](mailto:san@runanywhere.ai) for QHexRT deployment access.
 
 ## `v81/`
 `lfm2-5-230m.json` (manifest) · `lfm230_dec_512_w8.bin` (decode) · `lfm230_pf_512_w8.bin` (prefill) ·