cactus-needle2-calendar

Needle 2 — Calendar Tool-Calling (German), task-tuned

LoRA-fine-tuned Needle 2 (45M) for a local calendar agent: short German/English calendar instructions → the exactly right tool call with evidence-only (sparse) arguments. Base stays Needle 2; this repo ships the merged .cact archive for the 5-tool production calendar set.

Model details

Tool schema (5 production tools)

calendar_create, calendar_move, calendar_delete, calendar_list, calendar_find_slot. Load with the same schemas you trained on (expected shape — compact objects):

import needle

tools = [
  {"name": "calendar_create", "description": "...", "parameters": {"type": "object", "properties": {
      "title": {"type": "string"}, "date": {"type": "string"}, "until": {"type": "string"},
      "time": {"type": "string"}, "end_time": {"type": "string"},
      "participants": {"type": "string"}}}},
  # ... calendar_move/delete/list/find_slot
]

agent = needle.Needle(tools=tools,
                      system="date: 2026-09-13 Sun 12:00; locale: de-DE; device: raspberry-pi",
                      weights="calendar-needle2-seed44.cact")

print(agent.complete("Trag morgen 10 Uhr Zahnarzt ein.")["function_calls"])
# [{'name': 'calendar_create', 'arguments': {'title': 'Zahnarzt', 'date': 'morgen', 'time': '10 Uhr'}}]

System facts are facts, not instructions (date:/locale:/device:). The model was trained with date: 2026-09-13 Sun 12:00; locale: de-DE; device: raspberry-pi.

Gold convention: sparse / evidenced-only

Arguments contain only literal spans evidenced in the query. Optional fields without evidence are omitted, not defaulted — an empty arguments: {} is legal (e.g. a bare calendar_list). Downstream code resolves spans to times/IDs deterministically. This convention was validated A/B (exact_sparse 0.887 vs exact_full 0.186 on the same outputs).

Training data (not included here)

Synthetic, deterministic (seed 42) German/English templates over the 5 production schemas: 10k train / 1k validation / 1.8k test, ~9 % negatives, family-exclusive splits, held-out value pools; plus a 25-item frozen challenge set of real (anonymized) usage traces. Dataset is not published in this repo (privacy review pending).

Evaluation (synthetic test n=1800 / challenge n=25 / final-DB 25 cases)

3-seed variance on exact_args (test): 0.974–0.981 (release = seed 44).

Known limitations

• Trained for single-request tool calls; multi-intent decomposition is done upstream.
• No calibrated confidence: finetuning does not update the confidence head, so confidence is None — validate calls deterministically instead.
• Non-English tokenization costs ~1.7× tokens; the 256-token window is a real budget.
• Challenge-set gaps remain for until ranges and some colloquial phrasings.
• End-to-end calendar correctness is ~70 %: the remaining errors are in the resolver/verification layer (deterministic Python), not in argument extraction.
• No collision logic, no multi-step planning — intentionally outside the model.

License

Inherits Apache-2.0 from Cactus-Compute/needle2.

Reproduce

uv run python experiments/ft/train_rtx.py --run-name sa-r16-lr1e-4-e8-seed44 \
    --rank 16 --lr 1e-4 --epochs 8 --seed 44 --batch-size 8
uv run python experiments/ft/model_manifest.py     # hashes/provenance

Artifact sha256 (seed 44): ba3212abcac8355c026178d98dc6f4d95d2822064fd9cc1d8d4cc57bf471add9