kami
76a6a007ef
The most load-bearing decision in the project was stated four incompatible ways: the docs said Qwen3-1.7B, deploy/mavend.json said Qwen3.5-2B, the repo's models/llm/ held an LFM2.5-1.2B gguf, and five code comments still said LFM. Answering "which model is deployed" meant re-deriving it from scratch every time. Two facts the review missed, found while resolving it: - /mnt/hdd1/llms is bind-mounted over /opt/maven/models/llm, which shadows the repo's models/llm/. The LFM2.5 gguf sitting there was never loaded by anything, so it was not evidence of the deployed model at all. - That library holds Qwen3.5-0.8B, -2B and -4B, and no Qwen3-1.7B. The config pointed at a file that does exist; the docs' Qwen3-1.7B was the stale claim, the reverse of the assumed direction. Qwen3-1.7B is the CPT target, and that training is still in flight (Vikunja #122), so no such gguf exists yet. phraser.model_path moves to Qwen3.5-0.8B (Q4_K_M) — the smallest checkpoint on disk, chosen for latency, and relevant to whether the LLM router is affordable on this box. Docs and comments now say the same thing in one voice: 0.8B resident now, CPT'd Qwen3-1.7B as the target, and the bind-mount shadowing written down so the next reader does not mistake models/llm/ for ground truth. Comments name the model, never a filename, so a swap stays a one-line config change. n_gpu_layers: 99 is correct and stays — compose passes /dev/dri and the render gid for Vulkan offload to the Vega iGPU. CLAUDE.md's "CPU-only" was the stale half of that contradiction and is corrected. phraser.go also dropped a wrong "sub-1b, prompted not trained" size claim: the target is trained end-to-end (RU CPT + joint persona/router SFT). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01X5JApcrCRVGmqrxnhynSik
119 lines
4.6 KiB
Markdown
119 lines
4.6 KiB
Markdown
# Maven — Agent Context
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## Vikunja
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This repo maps to **Maven** (project ID: 2) in Vikunja.
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Feature work, bugs, deployment tasks all go here.
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MCP endpoint: `http://localhost:9100/mcp` (or `http://192.168.1.104:9100/mcp` from workpc)
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## Rendering / previewing the web UI locally
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To see mavweb pages with real data without touching the production stack:
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```sh
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R=/tmp/mvn-preview; mkdir -p $R
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go build -o $R/mavend ./cmd/mavend/ && go build -o $R/mavweb ./cmd/mavweb/
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cat > $R/mavend.json <<EOF
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{ "db_path": "$R/maven.db", "socket_path": "$R/mavend.sock",
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"state_dir": "$R", "tick_interval": "10s" }
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EOF
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$R/mavend -config $R/mavend.json &
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$R/mavweb -addr 127.0.0.1:9299 -core $R/mavend.sock &
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```
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- No models/voice/phraser config needed — the phraser stub covers it; mavend
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runs fine bare. mavweb serves `/`, `/dash`, `/history`, `/trace`,
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`/notifications`, `/tools`.
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- **Socket path must be short** — unix sockets cap at ~108 chars; a deep tmp
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dir fails with `bind: invalid argument`.
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- Seed data through `ipc.Client` (internal package — the seeder must live
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inside the module, e.g. a throwaway `cmd/seedtmp/main.go`, deleted after):
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`WriteFact`, `RecordNudge`+`ResolveNudge`, `CreateReminder`, `ProposeTool`.
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- `/trace` is empty until the first tick fires (wait one `tick_interval`).
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- Screenshots: `chromium --headless --disable-gpu --screenshot=out.png
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--window-size=1280,900 --hide-scrollbars --virtual-time-budget=2000
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http://127.0.0.1:9299/dash` (use `--window-size=430,900` for the phone/PWA
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view). **Always pass `--virtual-time-budget`** — without it the screenshot
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can snap mid-layout and silently drop elements (the PWA lang toggle
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"disappeared" this way).
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## Embedder model for intent routing
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The router uses a multilingual sentence embedder to classify intents and recall
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notes. Without it, the floor `HashEmbedder` is used — deterministic but weak
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(Russian recall rarely clears the confidence gate, many commands fall to
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"clarify").
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**Download the embedder** (ONNX, ~90 MB):
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```sh
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make download-embedder
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```
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This fetches `paraphrase-multilingual-MiniLM-L12-v2` (384-dim, 12-layer,
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supports 50+ languages including Russian) to `models/embedder/`.
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**Also need ONNX Runtime** (`libonnxruntime.so`):
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```sh
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curl -sL "https://github.com/microsoft/onnxruntime/releases/download/v1.15.1/onnxruntime-linux-x64-1.15.1.tgz" | tar xz
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sudo cp onnxruntime-linux-x64-1.15.1/lib/libonnxruntime.so* /usr/local/lib/
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```
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**Configure in `deploy/mavend.json`**:
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```json
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"voice": {
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"embedder": {
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"model_path": "models/embedder/model_quantized.onnx",
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"tokenizer_path": "models/embedder/tokenizer.json",
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"lib_path": "/usr/local/lib/libonnxruntime.so"
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}
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}
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```
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Without the embedder block, the daemon uses `HashEmbedder` (works, but weak on
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Russian recall — you may see many "clarify" responses).
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## Qwen3 resident model for router + phraser
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The target daemon uses the locally trained Qwen3-1.7B checkpoint for both
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routing and phrasing. Training is Qwen3 Base → RU CPT → joint persona/router
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SFT → merged GGUF, and is still in flight (#122) — until it lands, the deployed
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resident model is stock **Qwen3.5-0.8B** (`Q4_K_M`), see `deploy/mavend.json`.
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Without a configured model, `StubPhraser` plus the classifier remain the
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deterministic floor.
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During training, use the runbook in
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`docs/plans/2026-07-18-qwen3-resident-training-eval.md`. After the decision gate
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and SFT pass, copy the merged GGUF into the mounted model directory and set:
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```json
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"phraser": {
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"model_path": "/opt/maven/models/llm/Qwen3-Maven-1.7B-Q8_0.gguf",
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"bin_path": "llama-server",
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"n_gpu_layers": 99,
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"n_ctx": 2048
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}
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```
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**Configure in `deploy/mavend.json`** — the `phraser` block points at this
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model and the daemon spawns `llama-server` as a subprocess. The router and
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replier use the same llama-server via the shared `internal/llm` client.
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Telegram tokens are read from `deploy/telegram.env` (gitignored), expanded
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via `${VAR}` in the JSON config.
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**Routing is Qwen-first** with classifier fallback. The LLM router runs
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after stage-0 (exact-match grammar) and before the classifier cascade. On any
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error or parse failure, the classifier handles the utterance — the turn never
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breaks on the model.
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## Web UI conventions
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- All server-rendered pages share `cmd/mavweb/static/ui.css` (served at
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`/ui.css`) and the `nav` template partial (`navHTML` in `cmd/mavweb/main.go`,
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invoked as `{{template "nav" "<active-page>"}}`). New pages must link both —
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no per-page inline `<style>` beyond true one-offs.
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- Wrap every table in `<div class=scroll>` so wide data pans on a phone
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instead of breaking the layout.
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