AppLens-LLM is the local model outfitter workspace for AppLens.
It turns AppLens/AppLens-Tune machine evidence, workload goals, and benchmark results into validated local LLM deployment plans and training examples.
- Strict schemas for deployment plans, benchmark records, and training examples.
- Seed data for the first AppLens-Tailor training target.
- A validation CLI for JSON and JSONL artifacts.
- A capture ingestion CLI for AppLens
.mdreports and legacy.txtreports. - An OpenAI-compatible benchmark runner for Jan, llama.cpp, and similar local endpoints.
- Roadmap and architecture docs for the AppLens-LLM extension.
The first target is AppLens-Tailor:
machine evidence + local AI profile + benchmark facts + workload request
-> schema-valid AppLens-LLM deployment-plan JSON
The first base model target is Qwen3.5-2B. Training should wait until the baseline eval set proves that the base model misses structure, policy boundaries, or deployment-fit judgment.
uv sync --dev
uv run pytest
uv run applens-llm validate-jsonl --schema training-example data/examples.seed.jsonl
uv run applens-llm validate-jsonl --schema machine-profile data/machines.seed.jsonl
uv run applens-llm ingest-captures --source ../AppLens/raw --output data/raw/capture-records.jsonl
uv run applens-llm eval --examples data/examples.seed.jsonl --output out/eval-report.jsonJan default endpoint:
uv run applens-llm bench --model qwen-local --output out/jan-benchmark.jsonllama.cpp endpoint:
uv run applens-llm bench --endpoint http://127.0.0.1:18080/v1 --backend llama.cpp --model qwen2.5:7b --output out/llamacpp-benchmark.jsonGenerated benchmark output is ignored under out/.
V1 can start local model runtimes, run benchmarks, write manifests, and prepare sanitized datasets. It must not change services, startup entries, drivers, firewall rules, firmware, or user data.
See ROADMAP.md, docs/ARCHITECTURE.md, docs/CAPTURE_GUIDE.md, and docs/DEVELOPER_GUIDE.md.