serverAppConfigs/AGENTS.md

AGENTS.md — serverAppConfigs Agent Rules

Overview

This repository is the single source of truth for the two homelab servers. It tracks both the Podman container stacks and the LLM inference fleet. One repo, one checkout per host, one deploy script.

• Dankserver (192.168.0.235) — media/library stack (RomM, Copyparty, Calibre-Web, the *arr stack, Jellyfin) as bare containers, plus the frank-default-small LLM service.
• Dankclaw (192.168.0.143) — AI/search/development/observability stack (SearXNG via Gluetun, ComfyUI, OpenWebUI, Hermes, Gitea, Grafana, Prometheus, Loki, Alloy, Pi-hole) plus the frank-default-large LLM service. Caddy on Dankclaw reverse-proxies both hosts.

2026-08-22 merge: the former vllmScripts repository (LLM fleet scripts, units, env tunables, and runbook) is merged into this repo. Its full git history is preserved (two-parent import commit under the retired llm/ prefix, reorganized in follow-up commits). The retired target checkouts (/home/dank/vLLM on Dankserver, /home/dank/vllmScripts on Dankclaw) are archived. All services now run from /home/dank/serverAppConfigs on each host.

LLM fleet topology (2026-08-22)

Target	Runtime	Service	Model	Port
Dankserver	llama.cpp (upstream b10121)	frank-default-small	HauhauCS Qwen3.6-35B-A3B-Uncensored-Aggressive Q6_K_P + ngram-mod, 36 MoE experts on CPU	7770
Dankclaw	CIRU ROCmFPX llama.cpp, PromptForge v3 runtime (ROCm 7.15 SDK in the ling3-ciru venv)	frank-default-large	jcbtc Qwen3.8-27B-CIRU-ActiveFPX-PromptForge v3 (Q8-FFNEQ-LateQ6, full-W8 views, Output-K8 MTP proxy, BF16 vision), 262K context / 1 slot	7770

History: the 2026-08-17 redo retired the pipeline-parallel split and the FastFlowLM NPU endpoint. The 2026-08-18 swap retired the vLLM service on Dankclaw. On 2026-08-21 a DFlash2 Vulkan deployment briefly replaced CIRU, then was reverted the same day for speed. On 2026-08-22 the service briefly swapped to the Kairic Edge IU4 runtime, then was reverted to v3 the same day (operator decision). Runbooks: docs/LLM/index.md. Retired scripts live in old/ (reference only) and .AGENTTRASH/.

Repository Layout

• dankserver/ — compose.yaml + Caddyfile + .env.example for the media stack
• dankclaw/ — compose.yaml + Caddyfile + .env.example for the AI stack
• shared/ — homepage, Prometheus, Loki, Alloy, SearXNG configs
• scripts/ — deploy.sh (unified deployer), LLM serve scripts, systemd units, env tunables, bench harnesses
• old/ — retired PP/NPU-era LLM scripts (reference only)
• docs/ — knowledge base (Obsidian vault). docs/LLM/ is the LLM fleet
• tests/ — bats test suite

Git Workflow

All edits flow through git. Do not edit files on the remote machine directly.

Protocol

1. Source the config from the target machine — fetch the live compose/Caddyfile/serve script from the server where it runs
2. Place it in this repo — commit the fetched version (redact secrets into .env)
3. Edit here — make all changes locally in the working tree
4. Run the tests — ./tests/run.sh must pass
5. Commit and push — push to origin (Gitea on Dankclaw)
6. Pull on the target — git pull on the remote machine
7. Restart — scripts/deploy.sh pulls, restarts the stack and the LLM service, and checks health

Deployment

Use scripts/deploy.sh to deploy committed changes to a target. One script covers both workloads per host: the podman stack and the LLM service.

./scripts/deploy.sh dankclaw        # AI stack + frank-default-large (:7770)
./scripts/deploy.sh dankserver      # media stack + frank-default-small (:7770)
./scripts/deploy.sh dankclaw --dry-run   # preview without changes

Set FRANK_SUDO_PW for headless runs on a host without passwordless sudo (remote sudo reads it from stdin via sudo -S). The value is never stored in this repo.

graph LR
    A[Edit locally] --> B[Tests pass]
    B --> C[Commit]
    C --> D[Push to origin]
    D --> E[deploy.sh: pull on target]
    E --> F[restart podman stack]
    F --> G[relink + restart LLM unit]
    G --> H[health: homepage + LLM]

Testing

Tests are mandatory before commit. bats must be installed on the development machine.

./tests/run.sh

Test seams: compose validity, env consistency (no leaked secrets), Caddyfile validity, quadlet units, docs completeness, script syntax (all of scripts/, including the LLM serve scripts), LLM unit integrity (units point at files in this repo), LLM secret hygiene, deployment flow. See docs/Architecture/Testing Policy.md.

Secret Rules

• Never commit real secrets. The live Gitea database password and the live sudo password are test-enforced as leak checks.
• .env files are git-ignored. Commit only .env.example with placeholders. The LLM env tunables in scripts/ are tracked by exception (!scripts/*.env). They hold paths and numbers, never credentials.
• Do not document passwords in this repo, including sudo passwords. SSH keys are the auth mechanism. Headless sudo passes the password via the FRANK_SUDO_PW environment variable. The value never enters the repo.

Deletion & Trash Policy (MANDATORY)

Never run rm to delete files. The command deletes permanently. Move every item you delete into trash instead. The trash keeps the deletion recoverable and reviewable.

1. In-repo files: move the item to .AGENTTRASH/ beside its directory.
2. Home and other paths: create a dated staging directory ~/.AGENTTRASH/YYYYMMDD-topic and move each item there.
3. List the staged items and their sizes for the user.
4. Wait for explicit user permission before you empty the trash.

Rules:

• rm, rm -rf, and git clean -f are forbidden without explicit user permission.
• git * --no-verify is forbidden.
• A moved item stays in the trash until the user approves its purge.
• The trash is a temporary hold, not a long-term archive.

Benchmarking Policy (MANDATORY)

STOP the production service BEFORE running any benchmark on a target. These servers serve real users (about 3 people). Running a bench while the service is up starves the GPU and corrupts results. Restart the service when the bench finishes.

1. Stop the service before any benchmark: sudo systemctl stop frank-default-small (Dankserver) or sudo systemctl stop frank-default-large (Dankclaw).
2. Run the bench on a scratch port (never the LLM service port 7770).
3. Restart the service when the bench completes: sudo systemctl start <svc>.
4. Verify health before reporting: curl http://192.168.0.235:7770/health or curl http://192.168.0.143:7770/health.

Pi Model Config (~/.pi/agent/models.json)

The Pi agent reaches the fleet through /home/dank/.pi/agent/models.json. This file lives outside the repository. Each provider is one host endpoint. Each model id is the served alias. The two aliases are frank-default-small (Dankserver :7770) and frank-default-large (Dankclaw :7770).

The model id values are stable. The file /home/dank/.pi/agent/settings.json references them. Two resolution rules apply (verified in @earendil-works/pi-coding-agent model-resolver.js):

• The default model resolves as a defaultProvider + defaultModel pair. The defaultProvider must name the provider that serves the defaultModel.
• A subagent override uses a bare model id. A bare id resolves only when it is unique across all providers.

When you rename a service or move a model to a new host, update models.json. Update the provider key, the model id, contextWindow, and input. If the default model moved hosts, also update defaultProvider in settings.json. Set contextWindow to the per-slot context (both hosts 262144). The full schema is in the agent knowledge base page ~/.pi/agent/docs/pi/models.md.

Runtime & Troubleshooting

v3 CIRU ActiveFPX PromptForge llama.cpp (Dankclaw, ACTIVE)

The v3 runtime serves jcbtc/Qwen3.8-27B-CIRU-ActiveFPX-PromptForge-v3 from /home/dank/ciru-qwen38/ROCmFPX/build-promptforge/bin/llama-server (PromptForge v2.3 + v3 Output-K8 runtime patch). The v3 profile: PROMPTFORGE_MODE=m2048_fused_tail1476, full-W8 companion views, Output-K8 MTP draft proxy, BF16 mmproj vision, TEMPERATURE 0.7 / TOP_P 0.95, batch 4096 / ubatch 4096, 16 context checkpoints, reasoning on (deepseek, budget -1), --no-mmap. Five-file payload (~36.7 GiB) in /home/dank/models/CIRU-Qwen3.8-27B-ActiveFPX-v3/. Full runbook: docs/LLM/ciru-activefpx-promptforge.md.

Kairic Edge llama.cpp (Dankclaw, RETIRED experiment 2026-08-22)

The Kairic Edge llama-server serves jcbtc/Qwen3.8-27B-IU4-Kairic-Edge (pinned tag kairic-edge-qwen38-27b-v1). It was deployed and reverted to v3 the same day (operator decision). The runtime routes qualified prompt and verification shapes through the RDNA 3.5 native IU4 matrix instruction.

Release gap — CK config.h: the pinned CK commit ships only config.h.in. The release script and guide never generate third_party/composable_kernel/include/ck/config.h. Generate it via a CK CMake configure and copy it in before building (full runbook in docs/LLM/kairic-edge.md). The file is untracked in CK git. A git clean removes it. Local rebuilds are byte-identical (d7625a0c...).

Target greedy argmax fast path: the pinned runner sets LLAMA_TARGET_GREEDY_ARGMAX_FASTPATH=1. The fast path rejects non-greedy requests (400) and the card's own chat sampling is non-greedy, so the production attempt ran it at 0. It still rejects reasoning budgets unless compatibility mode is used. Do not re-deploy Kairic without re-testing chat first.

Rollback target: the v3 model files remain in /home/dank/models/CIRU-Qwen3.8-27B-ActiveFPX-v3/ and the v3 binary in /home/dank/ciru-qwen38/ROCmFPX/build-promptforge/bin/llama-server. Restored to v3 on 2026-08-22 (commit c60156f).

DFlash2 llama.cpp (Dankclaw, retired 2026-08-21)

A DFlash2 Vulkan deployment briefly replaced CIRU on 2026-08-21, then was reverted the same day for speed. Its serve script, env, and the build worktree are archived in .AGENTTRASH/ and documented in docs/LLM/dflash2-large-vulkan.md.

FastMTP patched llama.cpp (Dankserver, retired 2026-08-18)

Historical: the Qwen3.8-27B IQ4_XS FastMTP service was retired on 2026-08-18 (files in .AGENTTRASH/). The build notes are kept for reference. The HauhauCS FastMTP draft sidecar required a llama.cpp build from commit 4df29be4f4c3673f428170fda944a5b19f743bb8 plus the HauhauCS-FastMTP-llama.cpp.patch (from the HF repo). The build lives at /home/dank/llama.cpp-fastmtp/build on Dankserver. Loading the draft with a stock binary fails with expected 5120, 248320, got 5120, 32768. Do not rebuild from another commit without re-applying the patch.

REQUIRED build flags: -DGGML_CUDA_FA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON. Without GGML_CUDA_FA_ALL_QUANTS the q8_0 KV cache + flash attention falls back to a slow path and prefill collapses ~12x (measured 2026-08-17: 95 t/s vs 1300+ t/s at an 8K prompt on the 3090). See the HauhauCS HF repo README for the base recipe.

Env var naming rule — HIP runtime collision

The ROCm HIP runtime reads the bare environment variables TEMP, HOST, and GPU_MAX_HEAP_SIZE. The serve scripts export the env file with set -a. A tunable named TEMP leaks into HIP. On 2026-08-13 TEMP=1.0 made HIP use the path 1.0 for its JIT kernel cache, then segfaulted every model load. Commit 227ce01 fixed it by renaming the sampling variable to TEMPERATURE.

Never name a tunable TEMP, HOST, or GPU_MAX_HEAP_SIZE. Check for collisions with this command:

strings /opt/rocm-*/lib/libamdhip64.so.* | grep -xE "TEMP|HOST|GPU_MAX_HEAP_SIZE"

Crash-loop diagnostic procedure

When a llama service segfaults during model load, follow these steps.

graph TD
    A[Service SEGVs at load] --> B[Read journal + kernel segfault line]
    B --> C{Segfault in libamdhip64?}
    C -- yes --> D[Bisect script vs raw binary]
    C -- no --> E[Check config, memory, model files]
    D --> F[Run gdb for backtrace]
    F --> G[Find root cause]

1. Read the service journal with journalctl -u <service> -n 100. Then read the kernel segfault line with journalctl -k. A segfault at address 8 in libamdhip64 means a NULL unref in the HIP runtime.
2. Run the serve script directly. Then run the raw binary with the same flags. A different outcome isolates the env file as the cause.
3. Run gdb for the backtrace with this command:

   gdb -batch -ex run -ex "bt 25" --args bash scripts/serve-frank-large.sh
   

   llama-server buffers its timed stdout. On SIGSEGV the log tail is lost. Trust gdb, not the log.
4. Kernel spam about amdgpu: SVM mapping failed is a symptom, not the root cause. Investigate other causes before assuming memory pressure.

Operational quirks

• Both hosts allow passwordless sudo for the deploy user ((ALL) NOPASSWD: ALL, verified 2026-08-22). Use plain sudo in committed scripts. Do not use echo password | sudo -S in committed scripts. The FRANK_SUDO_PW env var covers headless runs on a host without that.
• Never run pkill -f with a pattern that appears in your own command line. It kills your ssh session. Use pkill -x <exact-name>.
• Model load is slow: Dankserver loads the 30.6 GB Q6_K_P through RAM (--no-mmap, MoE experts on CPU) in a few minutes cold. Dankclaw v3 loads the ~36.7 GiB five-file payload in ~18 s with a warm page cache, a few minutes cold. Health returns 503 while loading. Do not kill the process. deploy.sh waits up to 20 minutes on both targets (see LLM_WAIT).
• A client abort and a server release look different in the journal. A client abort logs W srv stop: cancel task, id_task = N. A natural stop (EOS or limit) logs only slot release with truncated = 0. The server never preempts a running generation. When all slots are busy, new tasks defer into a queue. See the 2026-08-13 incident in docs/LLM/deployment.md.
• ComfyUI on Dankclaw runs CPU-only (--cpu). ROCm wheels do not support gfx1151. Do not use CUDA images.
• SearXNG outbound traffic must stay on the Gluetun tunnel. Do not move its ports off the gluetun service.
• Retired hardware history: the Dankserver 35B cuBLAS crash and the DFlash premature-EOS bug are historical. See docs/LLM/dankserver-vram-growth.md and docs/LLM/qwen3.8-spec-matrix.md. The FastFlowLM NPU endpoint is retired. See docs/LLM/FastFlowLM NPU Inference.md.

Dankclaw memory model

The GPU budget on Dankclaw is the rocm-smi figure: 64 GiB VRAM reported by rocm-smi (SVM-backed). The docs' older 96 GiB figure was the addressable SVM pool. The GPU node reports local_mem_size 0, so all allocations are GTT/SVM-backed. Trust rocm-smi and the runtime. Do not use free or dmidecode for the GPU budget. They show the CPU-side carve-out. This is intentional. Do not change it.

Why

Direct edits on the remote drift from version control. Future agents will not know what changed. Git is the single source of truth. The two workloads share two hosts, one Caddy, and one Gitea. One repo keeps them in one history.

User request → Fetch from remote → Commit here → Edit → Test → Commit → Push → Pull on remote → Deploy + restart

Summary

Context	Action
Editing a compose/Caddyfile/serve script	Work through git — fetch, commit, edit, test, push, pull, deploy. Never edit on the remote directly.
Config exists only on remote	Pull it into the repo first — commit the fetched version (redacted) before making changes.
Unstaged local changes	Commit or discard — check git status before starting work. Ask the user if unsure.
Deploying a committed change	Run scripts/deploy.sh <target> — it pushes, pulls on the target, restarts the stack and the LLM service, and checks both health endpoints. See docs/LLM/deployment.md.
Benchmarking	Stop the service first — scratch port, restart, verify health. See the Benchmarking Policy above.
Deleting a file	Move it to trash — .AGENTTRASH/ (in-repo) or ~/.AGENTTRASH/YYYYMMDD-topic. Never rm.
Tests exist	Run them — verify tests pass after every change. Do not commit failing tests.
Real secrets	Never commit — use host .env files. .env.example holds placeholders only. FRANK_SUDO_PW covers headless sudo.