docs(paged): consolidate the dev-trail docs into one canonical README

The paged-attention patch directory had accumulated ~55 scattered dev docs
(results, progress, scope, lever, and gap-analysis notes). Consolidate the
durable content of all of them into one canonical
backend/cpp/llama-cpp/patches/paged/README.md covering: what the patchset is,
the architecture (paged KV + block-table flash-attn, the gated-DeltaNet SSM
decode path, NVFP4 FP4-MMA, the decode-first scheduler), the full 0001-0030
patch series table with bit-exact status, the GB10 benchmarks
(patched-vs-stock-vs-vLLM + the Apple M4 architectural note), the dev notes
(bit-exact methodology, the per-path gate, the MoE-parity conclusion, the
rejected/flat levers, the opt-in bf16-SSM mode), arch+quant generality, the
pin + canary maintenance policy, and the published NVFP4 gallery models.

Delete the consolidated-away dev trail. Keep the three operational docs the
README links to: PIN_SYNC_c299a92c.md (canary reference), PAGED_BITEXACT_NOTE.md
(per-path gate reference) and LOCALAI_LLAMACPP_BACKEND_PLAN.md (the
ship-as-own-backend design-of-record), plus the benchmark plots + csv. The
.patch files and the unit/bench .cpp are untouched.

Repoint every external reference to a deleted doc at the new README:
grpc-server.cpp, docs/content/features/backends.md, gallery/index.yaml, the
canary apply script (PIN_BUMP_APPLY_CHECK.md -> README), and the base
patches/README.md (ADDITIVE_DESIGN.md -> README). The canary's PIN_SYNC
reference still resolves; its inert SSM_DECODE_FIX_RESULTS.md glob (a
patch-internal path matcher, not a repo-doc link) is left intact.

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

docs/content/features/backends.md

@@ -125,7 +125,7 @@ For getting started, see the available backends in LocalAI here: https://github.
 LocalAI supports various types of backends:
 
 - **LLM Backends**: For running language models (e.g., llama.cpp, vLLM, SGLang, transformers, MLX)
-  - **`llama-cpp-localai-paged`**: LocalAI's paged-attention llama.cpp variant - on-demand paged KV cache plus a decode-first prefill budget, tuned for NVFP4 dense/MoE on Blackwell/GB10. Same upstream llama.cpp pin as the stock `llama-cpp` backend, reusing its gRPC server; the paged engine is enabled per-model via the `paged_kv` / `max_batch_tokens` options. For Qwen3.5 gated-DeltaNet (hybrid SSM) models you can additionally set `options: [ssm_bf16_tau:<tokens>]` to enable the reduced-precision hybrid SSM-state fast mode: fast-decaying recurrent heads (memory length tau below the threshold, e.g. `32` / `64`) persist their state as bf16, halving that head's decode byte stream. Default off (`0`) keeps every head f32 and is bit-exact; when enabled the mode is **not** bit-exact (~91% same-top-p ceiling - see `backend/cpp/llama-cpp/patches/paged/A_HYBRID_SSM_RESULTS.md` for the quality/throughput profile).
+  - **`llama-cpp-localai-paged`**: LocalAI's paged-attention llama.cpp variant - on-demand paged KV cache plus a decode-first prefill budget, tuned for NVFP4 dense/MoE on Blackwell/GB10. Same upstream llama.cpp pin as the stock `llama-cpp` backend, reusing its gRPC server; the paged engine is enabled per-model via the `paged_kv` / `max_batch_tokens` options. For Qwen3.5 gated-DeltaNet (hybrid SSM) models you can additionally set `options: [ssm_bf16_tau:<tokens>]` to enable the reduced-precision hybrid SSM-state fast mode: fast-decaying recurrent heads (memory length tau below the threshold, e.g. `32` / `64`) persist their state as bf16, halving that head's decode byte stream. Default off (`0`) keeps every head f32 and is bit-exact; when enabled the mode is **not** bit-exact (~91% same-top-p ceiling - see `backend/cpp/llama-cpp/patches/paged/README.md` for the quality/throughput profile).
 - **Speech-to-Text Backends**: For transcription (e.g., whisper.cpp, parakeet.cpp, faster-whisper, NeMo)
 - **Text-to-Speech Backends**: For speech synthesis (e.g., piper, Kokoro, VibeVoice, Qwen3-TTS)
 - **Sound Generation Backends**: For music and audio generation (e.g., ACE-Step)