haylanandClaude-Bot
828bd4c046
fastModel in ~/.qwen/settings.json (permissions.autoMode.classifier) was aliased onto llama-server's own 27B connection, so every tool-call safety check queued behind whatever heavy generation was already running on that model's 2 GPU slots. Add qwen-classifier: a separate llama.cpp instance, CPU-only, running Qwen3-4B-Instruct-2507 (the smallest Qwen3 with native >=131072 context, qwen-code's requirement, without lossy RoPE scaling). Structurally isolated from llama-server's queue instead of sharing it. Sized for gameserver's ~17GiB free system RAM: q8_0/q8_0 KV at full 131072 ctx (~9.8GiB) + Q4_K_M- class weights (~2.3GiB) fits comfortably, with better KV quality than the q4_0 that would've been needed to fit this on the GPU's ~6GiB free VRAM. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
130 lines
7.2 KiB
Bash
130 lines
7.2 KiB
Bash
# Copy to .env and adjust — or just run ./scripts/update.sh, which creates
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# .env from this file and fills in every secret/key below it can generate
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# itself (see each var's comment). All values below are defaults baked into
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# docker-compose.yml — only uncomment/change what you actually want to
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# override.
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# --- llama.cpp / model ---
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LLAMA_MODEL_FILE=Qwen3.8-27B-UD-Q4_K_XL.gguf
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# 999 = every layer on GPU (this model is dense, not MoE, and already fits
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# fully in 32GB VRAM — see docs/research/qwen3.8-27b-quant.md). Lower this
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# to leave that many fewer layers on GPU and push the rest to CPU/system RAM
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# if something else is contending for VRAM — llama.cpp has no separate
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# "RAM offload" flag, --n-gpu-layers *is* the RAM-offload knob for a dense
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# model. Don't reach for --n-cpu-moe/--cpu-moe/--override-tensor "exps" —
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# those target Mixture-of-Experts models (e.g. Qwen3.8-2.4T-A95B), not this
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# one, and are no-ops here.
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# There's no separate "then SSD" tier to enable either: llama.cpp mmaps the
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# model file by default (no --no-mmap here), so if GPU+RAM ever can't hold
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# the working set, the OS pages the rest in from disk automatically — an
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# implicit, slow last resort, not a config knob. An explicit tiered SSD
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# offload has been an open llama.cpp feature request since 2025 (still
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# unimplemented): https://github.com/ggml-org/llama.cpp/discussions/12507
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LLAMA_GPU_LAYERS=999
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# 262144 = this model's true max (max_position_embeddings in Qwen/Qwen3.8-27B's
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# config.json) — the largest --ctx-size llama.cpp will even accept for it.
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# fp16 KV cache at full context would be ~16GB, on top of 17.6GB weights =
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# ~33.6GB, which does NOT fit the 32GB R9700 on its own. docker-compose.yml
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# now runs --cache-type-k/v q8_0, which roughly halves KV memory (~8GB at
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# this size) — total ~25.6GB, ~6GB headroom, the same footprint the old
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# 131072 fp16 setting used. See docs/research/qwen3.8-27b-quant.md.
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LLAMA_CTX_SIZE=262144
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# Concurrent request slots — the real hardware ceiling for this GPU, not a
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# tunable to raise for throughput (was implicitly 4, llama.cpp's compiled-in
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# default; dropped to 2 because more contended prefill was blowing requests
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# past OmniRoute's idle timeout — see OMNIROUTE_STREAM_IDLE_TIMEOUT_MS below).
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# The 3rd+ request now queues on llama.cpp itself instead — its own queue has
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# no timeout (tools/server/server-queue.cpp), it just waits for a slot — so
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# the timeout that matters moved to OmniRoute's per-connection
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# providerSpecificData.timeoutMs (dashboard/API only, not in this file; see
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# handoff notes in the issue tracker). Each slot gets LLAMA_CTX_SIZE /
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# LLAMA_PARALLEL tokens of context — real sessions have hit ~66K tokens, so
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# don't drop LLAMA_CTX_SIZE without checking that per-slot number stays
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# comfortably above observed usage.
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LLAMA_PARALLEL=2
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# Dedicated CPU-only backend for qwen-code's tool-call harmfulness classifier
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# (fastModel in ~/.qwen/settings.json) — see docker-compose.yml's
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# qwen-classifier service comment for the why. 131072 ctx / q8_0 KV / Q4_K_M-
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# class weights ≈ 12GiB, fits gameserver's ~17GiB free system RAM with room
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# to spare.
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LLAMA_CLASSIFIER_MODEL_FILE=Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf
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# --- Lazytainer ---
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# Seconds of inactivity before llama-server is stopped. 900 = 15 min.
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LAZYTAINER_INACTIVE_TIMEOUT=900
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# --- SearXNG web search (see docs/research/litellm-searxng-search.md) ---
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# Resolved automatically by ./scripts/update.sh from search.home on this
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# host — leave blank. Only set by hand if that resolution fails (e.g.
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# search.home isn't a static DHCP reservation and its IP drifted).
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SEARXNG_LAN_IP=
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# --- OmniRoute gateway (see docs/proxy-key-onboarding.md, docs/network-access.md) ---
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# OMNIROUTE_PORT is the host-published port (reverse-proxied by NPM) — kept
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# at 4000, same as the old LiteLLM setup, so existing NPM/firewall config
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# doesn't need to change. It's mapped via plain Docker port publishing onto
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# API_PORT, omniroute's own container-internal port (left at its default,
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# not reconfigured to match). The dashboard (DASHBOARD_PORT) is never
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# published at all — see docker-compose.yml's omniroute service comment.
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OMNIROUTE_API_PORT=20129
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OMNIROUTE_DASHBOARD_PORT=20128
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# SSE inactivity timeout before OmniRoute gives up on a streaming request and
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# cancels it (which cancels the matching llama-server task too). 180s gives
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# contended prefill (see LLAMA_PARALLEL above) room to produce a first token.
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OMNIROUTE_STREAM_IDLE_TIMEOUT_MS=180000
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# Random values, filled in automatically by ./scripts/update.sh — leave
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# blank. Bootstrap dashboard admin password (log in at the dashboard port,
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# change it there afterwards — this is only the first-boot value):
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OMNIROUTE_INITIAL_PASSWORD=
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# Signs dashboard session cookies:
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OMNIROUTE_JWT_SECRET=
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# Encrypts API key values at rest in omniroute's SQLite DB:
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OMNIROUTE_API_KEY_SECRET=
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# Encrypts the whole SQLite DB at rest. Do not change after first run —
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# existing encrypted data becomes unreadable if you do (same caveat as
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# LiteLLM's old LITELLM_SALT_KEY):
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OMNIROUTE_STORAGE_ENCRYPTION_KEY=
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# Per-deployment salts — random is fine, just needs to be stable:
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OMNIROUTE_MACHINE_ID_SALT=
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OMNIROUTE_CLI_SALT=
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# Required (production) — shared secret for the internal Codex Responses
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# WebSocket bridge. Random value, filled in automatically:
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OMNIROUTE_WS_BRIDGE_SECRET=
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# Per-workload virtual keys (one per client that calls the gateway) have no
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# scripted /key/generate equivalent yet — omniroute's key-creation endpoint
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# needs a dashboard login session, not a static bearer key (see issue #37).
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# Mint them by hand in the dashboard, add a KEY=value line here per workload
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# as you onboard one. See docs/proxy-key-onboarding.md.
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# --- ComfyUI (local image generation, see issue #38 wayfinder map) ---
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# yurisasc/comfyui-rocm7.1 manages GPU-group access via these GID/UID env
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# vars rather than relying solely on docker-compose.yml's group_add.
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# Resolved automatically from the host by ./scripts/update.sh — leave blank.
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COMFYUI_PUID=
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COMFYUI_PGID=
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# Shared by every GPU-touching service (llama-server, llama-server-fast,
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# comfyui) for group_add: — resolved to real host GIDs by ./scripts/update.sh
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# rather than left as plain group names in docker-compose.yml, because Docker
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# resolves a *named* group_add entry against the container's own /etc/group,
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# not the host's, and fails unpredictably when the image doesn't define one
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# (worse with multiple GPU services racing on the same lookup at once — see
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# docs/research/rocm-gpu-pin-and-render-group.md and issue #5). Leave blank.
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HOST_VIDEO_GID=
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HOST_RENDER_GID=
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# --- ComfyUI diffusion model (Qwen-Image, FP8 — see docs/research/
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# image-generation-model-choice.md and issue #42) ---
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# Three files: diffusion weights, text encoder, VAE — all from the official
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# Comfy-Org FP8 split, chosen specifically because it's the only candidate
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# with a ComfyUI workflow pre-validated on this exact GPU (gfx1201/R9700).
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COMFYUI_DIFFUSION_MODEL_FILE=qwen_image_fp8_e4m3fn.safetensors
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COMFYUI_TEXT_ENCODER_FILE=qwen_2.5_vl_7b_fp8_scaled.safetensors
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COMFYUI_VAE_FILE=qwen_image_vae.safetensors
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# --- RAG databases (qdrant + neo4j, see wayfinder notes) ---
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# No auth on qdrant (its default) — same trust boundary as llama-server:
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# ai-stack is not exposed off-box. Random, filled in automatically:
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NEO4J_PASSWORD=
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