astrea

name	astrea
description	Use for hipfire quant calibration, imatrix-driven experiments, KLD/PPL quality evaluation, k-map/format selection, MQ/HFQ/HFP/MFP tradeoff work, ParoQuant-style weight transform planning, and KV policy planning. Use when deciding whether a calibrated model candidate should be promoted, rejected, packaged, or sent through Atlas for AR/DFlash perf validation.

Astrea

Astrea is hipfire's agent-native model calibration harness. It is a Python CLI for humans and a workflow contract for agents. The CLI emits plain JSON artifacts for weight calibration, ParoQuant-style transform planning, KV-cache policy planning, and future single-file model packaging; the agent supplies judgment, guardrails, and the next experiment.

Core Rule

Do not claim a quant candidate is better without measured quality evidence. Do not claim it is ship-ready without runtime compatibility and perf evidence. Do not claim Astrea wrote a packaged model unless the loader/package work exists; bundle-plan is a contract artifact, not a model writer.

CLI

Run from the hipfire repo root:

python3 scripts/astrea.py inspect --model MODEL [--imatrix IMATRIX] [--format FORMAT] [--pretty] [--out PATH]
python3 scripts/astrea.py imatrix-join --model MODEL --imatrix IMATRIX [--max-tensors N] [--pretty] [--out PATH]
python3 scripts/astrea.py fingerprint [--engine-root REPO] [--pretty] [--out PATH]
python3 scripts/astrea.py plan --model MODEL --format FORMAT --method METHOD [--recipe-stage STAGE:METHOD] [--imatrix IMATRIX] [--source-dir BF16_DIR] [--eval-command CMD] [--atlas-command CMD] [--pretty] [--out PATH]
python3 scripts/astrea.py calibrate --plan PLAN.json [--source-dir BF16_DIR] [--write-candidate] [--max-tensors N] [--tensor-filter NAME] [--workers N] --pretty [--out PATH]
python3 scripts/astrea.py eval --plan PLAN.json [--run] --pretty [--out PATH]
python3 scripts/astrea.py metrics --quality-json result-data.json --candidate-variant NAME [--baseline-variant NAME] [--floor-variant NAME] [--arch ARCH] [--scoring-mode MODE] [--engine-root REPO] --pretty [--out PATH]
python3 scripts/astrea.py policy --model MODEL --base-format FORMAT --promotion-format FORMAT (--sensitivity-json SCORES.json | --imatrix IMATRIX) --max-extra-bytes N [--method METHOD] [--objective dynamic-tensor-policy|moe-probe|model-ingress|kv-policy] [--domain weights|kv] [--model-family FAMILY] --pretty [--out PATH]
python3 scripts/astrea.py promote --policy POLICY.json --source-dir BF16_DIR --output CANDIDATE.hfq [--max-tensors N] [--tensor-filter NAME] --pretty [--out PATH]
python3 scripts/astrea.py kv-profile --model MODEL [--mode q8|asym3|triattn|cask|turbo3|rotor] [--triattn PATH] [--model-family FAMILY] [--engine-root REPO] --pretty [--out PATH]
python3 scripts/astrea.py bundle-plan --model MODEL --output MODEL.hfq [--include weights|paro|kv-policy|triattn|evidence] [--triattn PATH] [--policy-id ID] --pretty [--out PATH]
python3 scripts/astrea.py report ARTIFACT.json ... --pretty [--out PATH]

Prefer --pretty for human review and compact JSON for ledgers. Use --out for reproducible run directories; it writes JSON to the path and leaves stdout empty.

Workflow

1. Identify the target model, desired format, reference model, eval dataset, and budget.
2. Run inspect to fingerprint the model and imatrix inputs. Use imatrix-join when you need a focused report of GGUF imatrix tensor coverage against HFQ tensor names before planning a calibration run.
3. Run fingerprint to capture the engine path. This records git state, relevant source hashes, HIPFIRE_ROPE_INTERLEAVED_LEGACY, and whether the default Qwen3.5 FA RoPE path is halfsplit, interleaved_legacy, or unknown.
4. Run plan to create a bounded experiment artifact. Calibration methods are stackable recipe stages. Use repeated --method flags for the candidate stack, and optional repeated --recipe-stage flags when you need an explicit stage order such as scale_search:imatrix-scale, activation_aware:awq, rounding:gptq, promotion:kmap, or transform:quarot.
5. Run calibrate. Without --write-candidate, Astrea joins GGUF imatrix logical tensors to HFQ tensor names and reports whether the candidate is ready for a weight-mutation pass. With --write-candidate, Astrea can write an MFP4 imatrix-scale candidate or an MQ4 AWQ-style activation-weighted clipping candidate by copying the base HFQ and patching selected same-size tensor byte ranges. Use --max-tensors or --tensor-filter for smoke passes before a full rewrite. Use --workers N for process-parallel tensor rewrites on large models; start with 4 workers unless memory headroom is known.
6. Run eval with KLD/PPL commands against a BF16 or accepted higher-precision reference.
7. Run metrics on the kld_reduce.py result-data.json artifact. Prefer a Q8 or accepted high-precision floor row when available, so Astrea can report above-floor KLD and recovered quantization damage percentage. Always pass --engine-root when the evaluated engine is not the checkout running Astrea.
8. Run policy when you want an Unsloth-like dynamic quant policy. It ranks tensors by sensitivity per added byte and emits a mixed-format promotion recipe under a size budget. Use --objective moe-probe for MoE models and --objective model-ingress when bringing up a new model family; these add router/expert and alias-map probe work items to the artifact. Add repeated flags when the policy spans both weight transforms and KV-cache policy. Use to add the Paro weight-transform lane; this is a planned transform section until the quantizer/runtime have a compatible implementation. For rotated MQ/MFP bases promoted to Q8/F16, Astrea automatically bundles runtime anchor projections (, , ) with dependent projections so mixed-format candidates do not read stale normalized activation buffers.

Format Guidance

• Start with mfp4 + imatrix-scale when reproducing the known high-signal calibration path.
• For mq4, Astrea can now write a same-format AWQ-style activation-weighted clipping candidate. The first 9B run improved PPL but slightly worsened KLD, so treat this recipe as an empirical lane to iterate, not a validated win. Compare stackable recipes such as AWQ, imatrix-scale, GPTQ, k-map/promotion, and transform stages empirically.
• Treat ParoQuant as the highest-priority transform lane to prototype next after the existing imatrix/AWQ/GPTQ/k-map evidence loop is reliable. It needs a producer-consumer contract, not just an Astrea plan.
• Treat asym3 as the current KV baseline. TriAttention/CASK are packageable persistent sidecar data once embedded in the model package. TurboQuant-like and RotorQuant-like KV policies are research candidates until kernels, loader metadata, and AR/DFlash quality gates exist.
• For MoE, separate router tensors, expert tensors, and shared dense tensors in the policy artifact. Expert promotion should be justified by expert-hit distribution plus quality deltas, not static tensor names alone.
• Keep mq3, mq4, mq6, hfq4, hfq6, hfp4, and mfp4 eligible for experiments, but tie every recommendation to quality and perf artifacts.

Guardrails

• Preserve producer-consumer contracts. HFP/MFP candidates must remain compatible with the current fast-path block-size/runtime requirements unless quantizer, loader, docs, and kernels are moved together.
• Attach exact eval commands, reference model, dataset/chunk count, and output artifact size to quality claims.
• Do not compare KLD/PPL rows across different engine fingerprints or RoPE conventions without explicitly marking the comparison as historical.
• Attach Atlas rows for AR and DFlash when the candidate affects runtime formats used by both paths.
• Treat dry-run Astrea artifacts as plans only, not calibrated weights.
• Treat kv-profile and bundle-plan artifacts as policy contracts only. They should drive loader/kernel/Atlas work, but they do not prove runtime support.
• If an eval run emits non-finite logits, fail the candidate. Do not accept KLD=0 rows from older evaluators unless the logit path is confirmed finite.