mirror of https://github.com/razor-ai/soup.git
3 Commits
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Alpamys
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56bea56c08 |
fix(v0.40.1): QA Hardening — UTF-8 bootstrap, schema strictness, multi-objective preference runtime, CLI UX
Closes the QA findings from the Windows + RTX 3050 4 GB pass (2026-05-07): - Part A: UTF-8 stdio bootstrap on Windows (closes C1/C4/H1/N5/N8/G5) - Part B: root-level `lora:` migrates into training.lora (no more silent init_strategy bypass); multi-objective preference loss runtime no longer raises NotImplementedError (primary-loss approximation; full per-batch weighted combination deferred to v0.40.2) - Part C: autopilot 7B → 1B fallback + safetensors cache probe; transformers <5.0.0 cap with INCOMPATIBLE flag in `soup doctor`; quickstart auto-switches to SmolLM2-135M on ≤6 GB VRAM; --find-lr load_local → load_raw_data import fix - Part D (subset): dynamic --template help (H4); init --force (M2); migrate JSONL friendly error (N2); eval custom -o independent of attach-to-registry + loop-shadow bug fix (G10); history suggests dataset registry (N6); doctor importlib.metadata fallback (M1) + GPU diagnostic distinguishes CPU build (N3) + dual-Python detector (N4) - Part E: recipe fuzzy-match suggestions (M3); sample filename embeds strategy (no overwrite); JSONL BOM auto-strip Net +64 tests (4656 → 4720). 4 review agents clean (python/code/security/tdd). Long-tail UX papercuts (H2/H3/N7/M4/M5 + #36/#50/#51) deferred to v0.40.2. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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8878eb1aa6 |
feat(training): v0.32.0 — Training Stability & Auto-Tuning
Seven new opt-in flags that turn Soup into the "fast.ai of LLM fine-tuning" — pre-flight LR range finder, auto warmup schedule, auto mixed-precision, loss-spike auto-recovery, convergence detector, VRAM-pressure advisory, and autopilot integration. * Part A: soup train --find-lr / utils.lr_finder (TypedDict result, abs-divergence threshold, NaN/Infinity rejection, MAX_NUM_STEPS=10000, is_under_cwd containment on --find-lr-output). * Part B: utils.grad_accum (GradAccumMonitor + MAX_ACCUM=1024 cap; preserves effective batch on recommend()). * Part C: utils.mixed_precision (KNOWN_PRECISION_QUIRKS map, longest- substring iteration so qwen2.5/qwen2 + phi-3.5/phi-3 are deterministic; 200-char model-name cap, null-byte rejection). * Part D: utils.warmup (compute_warmup_steps clamped [10, 1000]; ratio==0 short-circuit matches HF Trainer "no warmup" convention). warmup_auto field reuses pre-existing warmup_ratio (no duplicate). * Part E: utils.spike_recovery (frozen dataclass policy; max_attempts<=10; min_lr floor) + schema cross-validator requiring loss_watchdog=true. * Part F: utils.convergence (detect_plateau + recommend_action; the latter reuses the former so plateau heuristic stays single-source). * Part G: autopilot.decide_warmup / decide_mixed_precision wrappers; generate_config validates BOTH the YAML output path AND embedded decisions["output"] via shared utils.paths.is_under_cwd. Tests: tests/test_auto_tuning.py — 89 tests covering bound boundaries, NaN/Infinity rejection, multi-version quirk ordering, frozen-dataclass post-construction validation, plateau non-positive-mean guard, and double-containment in generate_config. Total: 3607 -> 3696 tests passing. ruff clean. Review wave: python-review (8 findings), security-review (3), code-review (8 incl. duplicate warmup_ratio HIGH and synthetic stub-loss curve), and tdd-guide (11 coverage gaps) — every finding fixed before commit. Live in-process wiring (LR-sweep training loop, spike rollback, grad-accum DataLoader rebuild, SFT precision push) is deferred to v0.32.1 — same advisory pattern as v0.30.0 --auto-quant / structured-output. Stale-install gotcha: if `soup version` shows the old version after pulling this branch, run `python -m pip install -e . --force-reinstall --no-deps`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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e4c3042a56 |
feat(v0.25.0): Beyond the Wrapper — 8 major features
Ships v0.25.0 with eight new capabilities (Parts A–H) that close every competitive gap vs LLaMA-Factory/Axolotl/Unsloth and add unique differentiators: Part A — 9 new model recipes: Llama 4 Scout (sft/dpo/grpo), Qwen 3 14B/32B/8B-grpo, Gemma 3 12B/27B-dpo, DeepSeek V3 (MoE LoRA). Part B — Tool-calling / agentic fine-tuning: new "tool-calling" data format with detection + normalization, synth data template, init template, eval scoring (tool_call_match / tool_call_name_match / tool_call_args_subset), plus qwen3-8b-tools and llama4-scout-tools recipes. Part C — RLVR (RL from Verifiable Rewards): reward_fn=verifiable routing to math_verify_reward (regex-only, no eval), code_exec_reward (subprocess sandbox with RLIMIT_AS/RLIMIT_CPU on POSIX, ephemeral tempdir cwd, concurrency cap, one-time warning panel), and json_schema_reward. verifiable_domain Literal validated via model_validator. Part D — VeRA + OLoRA PEFT methods: LoraConfig.use_vera / use_olora with mutual-exclusion validator and a unified peft_builder helper that returns either LoraConfig or VeraConfig with the right init kwargs. Part E — Apple Silicon MLX backend: detection + hardware profiling in utils/mlx, MLXSFTTrainerWrapper via mlx-lm, scaffolding DPO/GRPO wrappers rejected at config load time by SoupConfig._validate_mlx_task_support, lazy trainer registry, doctor integration, 3 MLX SFT recipes, [mlx] extra in pyproject. Part F — Data augmentation: soup data augment with rephrase / translate / style strategies, path-traversal-protected input/output, count capped 1-10, lang/styles lists bounded (10 entries × 32 chars), rate limiting, and optional --dedup. Part G — Training intelligence: forgetting detection (ForgettingDetector with 3 built-in mini benchmarks and warning levels) and checkpoint intelligence (CheckpointTracker with composite metric, early-stop on regression, safe top-N pruning refusing symlinks and non-checkpoint dirs). SQLite schema extended with checkpoint_quality + forgetting_eval tables. Part H — Autopilot: soup autopilot command with dataset/model/hardware profilers, decision engine (task/quant/peft/batch/lr/epochs/max_length/perf flags), YAML generator, and full CLI with dry-run + --yes + path-traversal protection + goal whitelist + gpu_budget bounds [1GB, 1TB]. Bakes forgetting detection + checkpoint intelligence + early-stop into the generated config. Totals: - 2313 tests passing (183 new, up from 2130) - 86 test files (8 new) - 43 ready-made recipes (14 new) - 16 built-in templates (tool-calling added) - Review findings: all CRITICAL/HIGH/MEDIUM/LOW addressed (3 documented design limitations: code_exec best-effort sandbox, prune_checkpoints TOCTOU, MLX training integration test requires real hardware) Docs: CLAUDE.md, README.md, SECURITY.md, CONTRIBUTING.md updated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |