haylanandClaude-Bot
c795993a64
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qx22CV9EUS3hGATucQav13
73 lines
4.0 KiB
Bash
73 lines
4.0 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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# 131072 (128K): ~25.6GB (17.6GB weights + ~8GB KV cache) on the 32GB
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# R9700, ~6GB headroom — see docs/research/qwen3.8-27b-quant.md for the full
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# table (64K only used ~19.6GB/~12GB headroom, but real usage was burning
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# through 64K fast). If headroom gets tight, quantize the KV cache instead
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# of dropping context: --cache-type-k/v q8_0 roughly halves it.
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LLAMA_CTX_SIZE=131072
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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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# 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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