feat(char-rp-gguf): max context — char-rp 96K, char-rp-reasoning 40K, q8_0 KV
Raise both RP seats to near-max context within the GPU0 budget using q8_0 KV cache (near-lossless 8-bit, ~2x context/GB, flash-attn-backed). Verified coherent on both (no Qwen KV-quant gibberish) at 64/50 tok/s. - char-rp (Magidonia): 16K -> 96K (native 128K; 128K would starve the reasoning seat). - char-rp-reasoning (QwQ): 16K -> 40960 (QwQ native max; beyond needs YaRN). - kv_unified=true -> a single conversation gets the full n_ctx (slots share the pool). - GPU0 ~93/97G, ~4.3G margin (gen fixed-util + static KV = stable, no OOM risk). - New .env knobs: CHARRP_CTX / CHARRP_REASONING_CTX / CHARRP_KV_TYPE / CHARRP_REASONING_KV_TYPE.
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@@ -138,6 +138,11 @@ _As of 2026-07-08 — OFF-THE-SHELF INFERENCE STACK is the active work (home-tra
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old AEON trace-not-surfacing gap). One-model fallback (Magidonia both, lighter reasoning) documented in the stack
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header/README. Requirements met: prose#1, ≥50tps#2, low-refusal#3, dense#4, GGUF-not-Ollama#5, fit-GPU0#6, thinking#7.
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Candidate GGUFs also on disk for A/B: Cydonia-R1-24B-v4.1, PaintedFantasy-v4.1-24B, RpR-v4 Q6_K.
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**MAX CONTEXT (2026-07-08):** char-rp **96K** (Magidonia native 128K), char-rp-reasoning **40K** (QwQ native
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40960, YaRN-free max), **q8_0 KV cache both** (near-lossless, ~2× ctx/GB; verified coherent, no Qwen gibberish).
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GPU0 ~93/97G, ~4.3G margin (gen fixed-util + static KV → stable, won't OOM). 64/50 tps at max ctx. `kv_unified`
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→ a single conversation gets the FULL n_ctx (4 slots share the pool, not a /4 split). 128K char-rp only by
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dropping the reasoning seat's ctx (shared GPU0 budget). Tunables: CHARRP_CTX / CHARRP_REASONING_CTX / *_KV_TYPE.
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- **⏳ PENDING — wire LiteLLM gateway sampling defaults for GEN** (operator: "wire those settings in"). Config:
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`ana-docker:/opt/docker/conf/litellm/config.yaml`. **gen (Qwen3.6)** — thinking
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@@ -24,7 +24,11 @@ CHARRP_PORT=8016
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CHARRP_MODEL=rp/TheDrummer_Magidonia-24B-v4.3-Q6_K.gguf
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# Alternate prose model (more literary flair, looser POV adherence) — uncomment to swap:
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# CHARRP_MODEL=rp/MS3.2-PaintedFantasy-v4.1-24B-Q6_K.gguf
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CHARRP_CTX=16384
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# Context. Magidonia native = 131072 (128K). 96K @ q8_0 KV ~ 8.4G VRAM; f16 KV doubles
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# that. Raise toward 131072 only if you drop the reasoning seat's ctx (shared GPU-0 budget).
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CHARRP_CTX=98304
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# KV cache dtype: q8_0 (~half VRAM, near-lossless) or f16 (max fidelity, 2x VRAM).
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CHARRP_KV_TYPE=q8_0
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# ── REASONING seat (char-rp-reasoning) ──────────────────────────────────────
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CHARRP_REASONING_CONTAINER=llama-charrp-reasoning
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@@ -33,7 +37,10 @@ CHARRP_REASONING_PORT=8018
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# llama.cpp-managed CoT). ~50 tok/s @ Q5. Use Q6_K (~46 tok/s) for a touch more
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# quality if speed is not binding.
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CHARRP_REASONING_MODEL=rp/QwQ-32B-ArliAI-RpR-v4-Q5_K_M.gguf
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CHARRP_REASONING_CTX=16384
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# QwQ native ctx = 40960 (its max without YaRN). ~5.5G VRAM @ q8_0 KV.
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CHARRP_REASONING_CTX=40960
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# KV cache dtype (Qwen-arch): q8_0 verified coherent here; f16 if a future model gibbers.
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CHARRP_REASONING_KV_TYPE=q8_0
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# Thinking-token cap (QwQ over-thinks otherwise → starves the prose). 300-500 = a
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# concise, useful scene-plan before the response.
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CHARRP_REASONING_BUDGET=400
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@@ -69,9 +69,15 @@ services:
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- --n-gpu-layers
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- "999"
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- --ctx-size
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- "${CHARRP_CTX:-16384}"
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- "${CHARRP_CTX:-98304}"
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- --flash-attn
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- on
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# q8_0 KV cache ~halves KV VRAM (8-bit, near-lossless) → ~2x the context per GB.
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# Mistral/Magistral handles q8 KV cleanly. Set f16 in .env to disable.
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- --cache-type-k
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- ${CHARRP_KV_TYPE:-q8_0}
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- --cache-type-v
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- ${CHARRP_KV_TYPE:-q8_0}
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- --jinja
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healthcheck:
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test: ["CMD-SHELL", "curl -fsS http://localhost:8080/health >/dev/null || exit 1"]
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@@ -115,9 +121,16 @@ services:
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- --n-gpu-layers
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- "999"
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- --ctx-size
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- "${CHARRP_REASONING_CTX:-16384}"
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- "${CHARRP_REASONING_CTX:-40960}"
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- --flash-attn
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- on
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# QwQ native ctx = 40960 (beyond needs YaRN → quality loss; don't). Qwen-arch can be
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# KV-quant-sensitive: q8_0 is verified coherent here, but flip to f16 in .env if a
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# future model shows gibberish.
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- --cache-type-k
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- ${CHARRP_REASONING_KV_TYPE:-q8_0}
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- --cache-type-v
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- ${CHARRP_REASONING_KV_TYPE:-q8_0}
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- --jinja
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# QwQ reasoning is template-native → llama.cpp manages it. --reasoning on surfaces
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# the trace in reasoning_content (content stays clean prose); --reasoning-budget
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