Artifact contract
An OpenAI-compatible local model server plus a multi-turn tool loop with schema validation and explicit execution policy.
This agent runtime is classified as runnable lab. Workload guidance: local inference; model size controls RAM and latency.
Build
Serve a local artifact, then run one bounded tool-using conversation.
Modify
Change tool schemas, catalog routing, prompts, or execution policy independently.
Tune
Tune decoding, prompt reuse, constraints, and routing before assuming more training is needed.
Prove
Score call selection, arguments, legality, task completion, latency, and unsafe actions.
Package
Ship the model, tool catalog, policy, and eval gate as one bounded system contract.
CLI surface
- Run: posttrainllm serve
- Run: posttrainllm agent
- Run: posttrainllm eval-bfcl
- Run: posttrainllm eval-tau-bench
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/learn/artifact-journey.json. The links below are the tracked evidence and explanatory sources preserved with the record.
- native-mac/Sources/TinyGPT/AgentLoop.swift ↗
- native-mac/Sources/TinyGPTServe/Serve.swift ↗
- Serve a local artifact, then run one bounded tool-using conversation. ↗
- Change tool schemas, catalog routing, prompts, or execution policy independently. ↗
- Tune decoding, prompt reuse, constraints, and routing before assuming more training is needed. ↗
- Score call selection, arguments, legality, task completion, latency, and unsafe actions. ↗
- Ship the model, tool catalog, policy, and eval gate as one bounded system contract. ↗