mirror of https://github.com/razor-ai/soup.git
29 Commits
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Alpamys
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1e822af461 |
feat(v0.65.0): Eval Depth — judge calibration + behaviour battery + capability suite + CheckList DSL + IRT subset
5 LIVE parts closing axis 4 of the roadmap. Evals as first-class surface, not afterthought:
- Judge calibration: SCOPE/CJE-style bidirectional pairwise judging in eval/calibrate.py
with PairwiseJudgement / fit_position_bias / conformal_threshold +
ensure_judge_calibrated production gate that refuses to score with an uncalibrated
judge (RuntimeError on None / calibrated=False / low agreement / extreme bias).
- Behaviour battery (soup eval behavior): closed allowlist of XSTest / HarmBench /
JailbreakBench / ELEPHANT / SycEval with 5 tiny bundled redacted probe sets under
soup_cli/data/_fixtures/behavior/. Word-boundary regex agreement rejects
"safe" in "unsafe" false positives. Pre/post diff with OK/MINOR/MAJOR verdict
(matches v0.26 / v0.56 taxonomy).
- Capability auto-suite (soup eval capability): MMLU-Pro / GPQA / BBEH / AIME /
MATH-500 / HumanEval+ / SWE-bench-Verified with full / fast / math / code profile
selector. Emits (benchmark, lm-eval task) manifest for downstream
soup eval benchmark chaining.
- CheckList DSL (soup eval checklist): Ribeiro et al. 2020 MFT / INV / DIR test kinds
rendered from YAML. Word-boundary matching prevents "and" matching "sand".
Per-test pass/fail + OK/MINOR/MAJOR overall verdict.
- IRT eval-cost optimizer (soup eval irt-subset): 1PL Rasch closed-form fit on
per-item correctness signals + high-info subset selector (full / small / tiny
profiles). 5-10x cut in eval bills without losing ranking power.
Cross-cutting hardening (review-fix coverage across 2 review waves):
- TOCTOU defence: every new read path uses O_NOFOLLOW + os.fstat on SAME fd
(load_checklist_spec, load_response_rows, _read_evidence_json). Earlier
double-lstat-on-path was a race the attacker could win by swapping the file
between calls.
- Namespace-package safety: load_battery_probes uses importlib.resources.files
Traversable / op + as_file (was Path(os.path.join(str(pkg_root), ...)) which
silently fails is_file() on MultiplexedPath installs).
- Word-boundary regex agreement in behavior_battery + checklist_dsl.
- CLI _validate_run_id gate; 16 MiB --evidence cap with O_NOFOLLOW;
_MAX_ROWS=1_000_000 cap counts skipped lines toward total in load_response_rows.
- INV empty-string normalisation no longer spuriously passes.
- _write_json_output / _read_evidence_json / _validate_run_id dedup helpers in
commands/_eval_v0650.py.
Review fixes: 0 CRITICAL + 6 HIGH + 9 MEDIUM + 7 LOW resolved across 2 waves.
Test count: 10306 -> 10577 (+271 net across test_v0650_part_{a,b,c,d,e}.py +
test_v0650_followups.py). Full suite 10577/10577 passing. 0 regressions.
Step 6 smoke: every new CLI command + 3 failure modes exercised end-to-end
(behavior with --evidence happy + MAJOR exit 2; capability fast with output;
checklist with real YAML; irt-subset on 600-row synthetic data; unknown
battery / size / kind all exit 2; outside-cwd evidence rejected).
Known limitations:
1. Live lm-eval-harness invocation deferred — soup eval capability emits the
manifest for downstream soup eval benchmark chaining (Typer commands aren't
safe to re-enter; matches v0.46.0 / v0.44.0 design).
2. Live model-driven soup eval behavior deferred — without --evidence, emits a
neutral OK report (v0.65.1).
3. Behaviour battery probe sets ship as tiny redacted placeholders — operators
pull real harmful prompts from upstream papers.
4. IRT model is 1PL Rasch only (2PL / 3PL deferred to v0.65.x).
Step 6 quirk worth noting: --evidence containment rejects /tmp/ on Windows
WSL bash; operators must run from cwd or pass cwd-contained paths.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Alpamys
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0d6f95181a |
feat(v0.62.0): RAG & Activation Steering — RAFT + RA-DIT + soup steer + citation-faithful + GRACE codebook
5 Parts shipping wedge 14 of the roadmap (RAG-aware fine-tuning +
activation steering + lifelong edit codebook). Schema-only release;
live training loops + decode-hook intervention + codebook lookup all
land in v0.62.1 (mirrors v0.50.0 / v0.52.0 / v0.61.0 stub-then-live).
Part A — RAFT data format: new data.format='raft' schema +
_convert_raft validator (64 KiB per-field cap, 64-distractor cap,
null-byte rejection on every field) + raft-llama3-8b recipe.
Part B — RA-DIT two-stage: TrainingConfig.ra_dit_stage Literal
{retriever, generator} + ra_dit_retriever_model field + closed
allowlist + cross-validator enforcing stage to base-task pairing
(retriever to embedding, generator to sft) + 2 recipes.
Part C — soup steer (CAA / ITI / RepE): closed-allowlist control-vector
methods + validate_steering_method/name/strength + Typer subcommands
train/apply/list + soup serve --steer/--steer-strength flags +
steering_vector Registry artifact kind. apply_steering +
build_steering_vector deferred-live stubs raise NotImplementedError
with v0.62.1 marker after validating inputs.
Part D — Citation-faithful FT: score_citations precision/recall/F1
kernel + extract_citation_ids public API + citation_faithful /
citation_style / citation_recall_threshold schema. Cross-validator:
citation_faithful=true requires data.format='raft' AND task in
{sft, pretrain} (silent-no-op footgun rejection mirroring v0.52.0
distill / classifier task-gate policy).
Part E — GRACE codebook: GraceCodebookConfig + bounded size [1, 100k]
+ bounded dim [1, 16384]. Extends v0.61.0 SUPPORTED_EDIT_METHODS
allowlist with 'grace'; apply_edit routes grace plans to v0.62.1
marker while legacy rome/memit/alphaedit retain v0.61.1 marker
(regression-guarded via TestEditMarkerRegressionGuard).
Test count: 9571 -> 9786 (+215 net). 4 review-agent waves resolved
0 CRITICAL + 0 HIGH + 4 MEDIUM + 11 LOW (broken list_steers registry
context-manager + dict-key access; missing version bump;
citation_faithful task-gate; shared TOCTOU helper delegation; Rich
markup escape on --steer exception messages; --base length cap +
null-byte rejection; typing.Iterable -> collections.abc.Iterable
migration; except Exception -> except ImportError narrowing).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Alpamys
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740832e1b4 |
feat(unlearn/edit): v0.61.0 — Unlearning & Knowledge Edit (NPO/SimNPO/RMU + ROME/MEMIT/AlphaEdit)
5 Parts shipping schema + CLI surface for two of the most under-served axes in fine-tuning: GDPR right-to-be-forgotten unlearning (the legal-liability axis upstream TRL avoids) and surgical knowledge editing (research-coded everywhere, productized nowhere). Schema-only release; live trainer + kernel wiring deferred to v0.61.1 (matches established v0.50.0 / v0.52.0 / v0.53.0 stub-then-live cadence). Part A — task='unlearn' + NPO/SimNPO/RMU allowlist + UnlearnTrainerWrapper + data.forget_set / data.retain_set + training.unlearn_method/_alpha Part B — soup eval unlearning (TOFU/MUSE/WMDP) with Forget Quality + Model Utility + PrivLeak kernels + OK/MINOR/MAJOR taxonomy; bundled TOFU mini-fixture under soup_cli/data/_fixtures/unlearning/ Part C — soup edit set (ROME/MEMIT/AlphaEdit) + EditPlan + per-method default layer; --plan-only ships live, apply_edit kernel deferred Part D — Sequential edit governor: norm-blowup detection (OK/WARN/BLOWUP), auto-switch ROME→AlphaEdit at edit#10 or BLOWUP, refuses past cap Part E — soup edit diff: cwd-contained probe loader, atomic JSONL out, shape + table renderer (live before/after generation v0.61.1) Net: +125 tests (9446 → 9571), +5 utility modules + 1 trainer wrapper + 2 commands. Review-fix coverage: 0 CRITICAL + 5 HIGH + 11 MEDIUM + 11 LOW. All ruff + pytest green; Step 6 smokes (CLI plumbing + happy paths + 5 schema rejection paths) confirmed end-to-end. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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6d2170c4f3 |
feat(v0.53.8): Remote data + Hubs + Trackers (wave 2) — 6 features
- #85 fsspec live loaders — data/loader.py routes the v0.42.0 fsspec scheme allowlist (s3:// / gs:// / gcs:// / az:// / abfs:// / abfss:// / oci://) through fsspec.open with validate_remote_uri containment BEFORE connection. Friendly Rich panel names the pip install advisory when the backend SDK is missing. Threads data.streaming + data.buffer_size. Row count capped at 1M. - #130 Hub dispatcher live — utils/hubs.download_repo() and upload_repo() lazy-import per backend (huggingface_hub / modelscope / openmind_hub). Shared _validate_repo_id_shape (bool / null-byte / leading-slash / .. / control-char / oversize) + cwd containment on local_dir / folder_path. commands/train.py pre-fetches non-HF base into .soup_hub_cache/ (sanitised slug, idempotent on resume, cfg.base updated via model_copy). soup data download --hub flag plumbed. Multi-command rollout for chat / serve / infer / merge / export / push tracked for v0.53.9. - #89 [trackers] pyproject extra bundles mlflow / swanlab / trackio; tracker_missing_dep_message surfaces a friendly pip install advisory via importlib.util.find_spec (non-executing probe). - #90 utils/trackers.send_telemetry_payload — opt-IN via SOUP_TELEMETRY=1; lazy httpx; 1s hard timeout; HTTPS-only with SSRF re-validation (mirrors v0.51.0 hub endpoint policy); silent-fail on every exception. - #93 Fixtures migrated to soup_cli/data/_fixtures/ — zipapp / namespace-package safe via [tool.hatch.build.targets.wheel.force-include]; _bundle_source_path falls back to examples/data/ for editable installs. - #69 utils/hf_space.detect_space_sdk(requirements_text) — picks "streamlit" / "gradio" from the rendered requirements.txt; closes the v0.40.2 known limitation that custom Spaces always defaulted to gradio. Wired into commands/deploy.py. Review pass: python-review + code-review + security-review ran in parallel; 16 findings fixed (3 HIGH + 8 MEDIUM + 5 LOW). Highlights: cwd-containment on local_dir/folder_path, Windows ..\ traversal defence on .soup_hub_cache slug, Pydantic model_copy(update=...) instead of attribute mutation, idempotent pre-fetch via cache probe, 1M-row cap on remote materialisation, SSRF re-validation on telemetry endpoint override, 256 KB cap on detect_space_sdk input, modelscope.push_model commit_message kwarg removed (would TypeError at runtime), find_spec instead of __import__ to avoid swanlab side-effects. Test count: 8162 -> 8257 (+66 in tests/test_v0538.py + 29 net adjustments). Lint clean. CPU smoke: version, --help, load_config_from_string with hub: modelscope passes; mlx + non-HF rejected; data download --hub modelscope advisory rendered; detect_space_sdk live on real requirements.txt bodies; package-data fixtures resolve from soup_cli/data/_fixtures/. v0.53.7 known limitation #1 (bash 501 marker) bumped to v0.53.9. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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18d8b36114 |
feat(v0.53.7): Data Forge + Pipeline live (wave 1) — 11 items
Closes v0.47.0 deferrals (#111, #112), community QA (#75), v0.42.1 wave 1 (#87, #86, #88), and 5 v0.53.6 stub-to-live carry-overs (#102 vLLM parity + SSE streaming, #103 tool HTTP endpoints, #105 instantiate_trainer_plugins, #106 run_recipe DAG runner). - #88 markdown ingest heading split (split_markdown_by_headings) - #112 soup data decontaminate --benchmark-file (cwd-contained operator corpus + symlink rejection) - #87 prompt_strategy live resolver (resolve_prompt_strategy + lru_cache, importlib-based; per-row hook in sft_format.py) - #86 soup data preprocess AOT tokenize (atomic Arrow shard write + cache metadata sidecar; SFT + Pretrain wrappers short-circuit on format='pre_tokenized' + tokenized_path with cache-hash gate) - #111 forge --judge-provider {ollama,anthropic,vllm} live (lazy v0.20.0 providers; SSRF parity) - #75 QA log entry for synth-data provider manual smoke - #106 run_recipe LIVE for 6 NODE_KINDS (seed / llm_text / code / judge / validator / sampler); atomic checkpoint via tempfile.mkstemp; resume rehydrates predecessor outputs from per-node sidecar JSONL; lstat-on- raw-path symlink rejection (v0.33.0 #22 TOCTOU parity); failed_reason path-redacted - #105 instantiate_trainer_plugins LIVE for cce_plugin / grokfast / spectrum / llmcompressor / sonicmoe / math_verify (lazy imports, friendly pip-install advisory on missing dep) - #103 POST /v1/tools/python + /v1/tools/web_search LIVE (Bearer auth gate, deny-by-default domain allowlist, 5s timeout, 5-result cap). bash reverted to HTTP 501 — security review caught /bin/sh -c child escapes RLVR sandbox's OS-level isolation; deferred to v0.53.8. - #102 vLLM /v1/messages parity LIVE on both backends; CORS loopback-only - #102 Anthropic-shape SSE streaming on /v1/messages LIVE; Cache-Control: no-store; CRLF/NUL/oversize strip on model+msg_id (header injection) Review fixes (1 CRITICAL + 11 HIGH + 17 MEDIUM + 10 LOW from python-reviewer + code-reviewer + security-reviewer + tdd-guide) all addressed in this commit. Test count: 8051 → 8162 (+111 in tests/test_v0537.py). Test files: 189 → 190. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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2292e81c3f |
feat(modality): v0.53.2 — lift Modality II stubs (distill + classifier + EBFT/GDPO + reasoning_effort)
Closes #132, #133, #135, #137. Records #71 ONNX QA (partial — tiny-gpt2 PASS, TinyLlama-1.1B blocked by host RAM during onnx.load post-process). New trainer wrappers: - DistillTrainerWrapper (soup_cli/trainer/distill.py) — student + frozen teacher, KL/JS divergence kernels scaled by T**2, device-bridge for HF Trainer auto-CUDA promotion, DataCollatorForSeq2Seq for variable-length loss-masked rows, separate trust_remote_code resolution per model. - ClassifierTrainerWrapper (soup_cli/trainer/classifier.py) — single/multi label sequence classification, 1024-entry multi-label cap, label_names string-to-int resolution. Routes classifier / reranker / cross_encoder. Live loss kernels: - apply_ebft_loss (structured / strided) + attach_ebft_compute_loss (SFT) - apply_gdpo_loss (standard / length_normalized / margin) + attach_gdpo_compute_loss (DPO). Both attach hooks idempotent. Prompt-format wiring: - apply_reasoning_effort_prefix injects gpt-oss <|reasoning_effort|>{low,medium,high}<|/reasoning_effort|> header. - build_assistant_only_labels(train_on_eot=True) keeps EOT/EOS unmasked. Bugs surfaced + fixed during Wave 3 CPU smoke (regression guards in tests): - Distill collator did not pad pre-tokenised labels (variable-length crash) - Distill compute_loss device-mismatch when HF Trainer auto-promoted student to CUDA while teacher stayed on CPU. Tests: 7722 -> 7842 (+120 in test_v0532.py). 5 review agents run; every CRITICAL/HIGH/MEDIUM/LOW finding fixed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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05093ebfdb |
feat(data): Data Pipeline Pro — 18 features, axolotl + LF parity (v0.42.0)
Schema-first surface for the data pipeline gap with Axolotl + LlamaFactory. Ships in one release: 5 new formats (prm, pre_tokenized, input_output, video, multimodal), remote URI allowlist (s3/gs/gcs/az/abfs/abfss/oci) + streaming + sharding, AOT preprocess cache + `soup data preprocess` CLI, multi-dataset interleave (concat/under/over/probs) + 8 advanced masking fields, vocab expansion (add_new_tokens / new_special_tokens / resize_vocab) + custom prompt_strategy, and document ingestion (`soup data ingest` for PDF/DOCX/MD/TXT). Live wiring for fsspec backends, AOT tokenize loop, custom prompt-strategy runtime, and PRM trainer integration is deferred to v0.42.1+ (stub-then-live pattern from v0.27.0 / v0.37.0 / v0.41.0). Schema gates fire at config load so misconfiguration fails fast. Security: full v0.42.0 hardening matrix — `_REMOTE_SCHEMES` MappingProxyType allowlist; bucket regex 1-63 chars per S3/GCS spec; userinfo / fragment / query-string rejection on remote URIs (query-string forwarded to fsspec is SSRF-adjacent); null-byte + length caps on every string-shaped input; bool-rejected-before-int on every numeric input; frozen InterleaveSpec dataclass; 10k caps on add_new_tokens; `is_under_cwd` containment on video_dir + tokenized_path schema fields and on preprocess --config / both ingest paths; `os.lstat + S_ISLNK` symlink rejection on ingest input; PRM converter type-checks completions (str) + labels (bool, not int); video field null-byte + 2KB cap; field-name threading on image-pixels validator so error messages name the actual field. 5242 tests → 5389 (+147 net). 11 review findings addressed across python-review / code-review / security-review / tdd-guide (CRITICAL→LOW). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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1f050fc235 |
feat(v0.40.3): Stub-to-live wave 1 (#33, #64; #65 still deferred)
Three v0.X.0 deferred-stub features become live runtime — closes #33 (harvester judge filter + serve trace log) and #64 (live CUDA OOM probe). #65 (multipack live wiring in HF Trainer) remains deferred to v0.40.4 after the adversarial 5th-review pass surfaced a Sampler[int] vs list[list[int]] shape mismatch with HF Trainer's DataLoader; helpers (`make_multipack_trainer_class`, `attach_multipack_state`, `lengths_from_dataset`, `detect_arch_name`) ship as a stub used by unit tests, but the SFT/Pretrain wrappers print a yellow advisory and fall back to the standard sampler when `multipack: true`. Live CUDA OOM probe (#64): `make_cuda_probe_fn` builds a closure that runs ONE forward+backward+step on a synthetic batch per candidate. `model.zero_grad(set_to_none=True)` runs BEFORE forward; intermediate ids/attn/labels/outputs are del-ed before `loss.backward()` so peak VRAM reflects a realistic training step (matches v0.35.0 #45 policy). `pad_id` is bounded by `len(tokenizer)` (not `vocab_size`) so extended vocabs (Llama-3 + `<|pad|>`) don't fold pad to a random byte token. SFT-only this release. Trace-to-Preference judge filter (#33 (a)): `judge_filter_pairs` reuses v0.19.0 JudgeEvaluator backends (openai/server/ollama). Threshold rejects bool/NaN/out-of-[0,1]; `_MAX_BATCH=100_000` cap applied via lazy `itertools.islice`; per-pair backend exceptions caught and DEBUG- logged (matches v0.33.0 #47 policy); `judge_provider` validated against the allowlist at the CLI boundary BEFORE constructor with a Rich-escape error message; yellow projected-call-count warning before the loop (2× per pair). Inference Server trace log (#33 (b)): `TraceLogWriter` is thread-safe (single-process lock — multi-worker documented as known limitation); path containment via shared `is_under_cwd`; null-byte/empty/non-string path rejected; cap_mb bounds [1, 10000] with explicit bool rejection. Rotation (one backup retained) refuses symlink at the backup path via `os.lstat + stat.S_ISLNK` (matches v0.33.0 #22 TOCTOU policy). Secret redaction (`hf_*` ≥8, `sk-*` ≥16, `Bearer …` ≥8 with `.` excluded so end-of-sentence period survives) applied to prompt + response and recursively to caller-supplied `extra` dict values. Streaming SSE path also records (was a coverage gap caught in adversarial review). Behaviour change: v0.40.2 users with `auto_batch_size_strategy: probe` were silently getting the static fallback. v0.40.3 actually runs a CUDA probe on first run (~5–30s, cached per (model, max_length, quant, lora_r, gpu) tuple). Reviews: 5 agents (python, code, security, tdd, verification-loop). Verification-loop run twice — once shallow smoke (PASS), once adversarial bug-hunt which found C1/C2 (multipack live wiring crash — demoted to v0.40.4), H1 (streaming SSE missing trace log — fixed), H4 (vocab_size vs len(tokenizer) on extended vocabs — fixed), H3 (Bearer regex consumed trailing period — fixed), H2 (judge cost shock — warning added), M2 (empty lengths accepted — rejected), L1 (extra dict bypassed redaction — recursive walk added). Tests: 4756 → 4855 (+99 net new) across test_v0403_part_a/b/c.py. Lint clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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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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a5540fa1e2 |
feat(correctness): v0.36.0 — Correctness First (4 Parts: A/B/C/D)
Four silent-failure modes Soup had → loud failures, plus a
security default-deny.
- Part A: assistant-only loss masking (default true). Replaces TRL's
multi-turn heuristic with explicit IGNORE_INDEX masking. New
data.train_on_responses_only / train_on_messages_with_train_field
+ per-message train: bool field. Preferred path uses
return_assistant_tokens_mask; fallback uses incremental tokenize
delta with add_special_tokens=False to avoid double-BOS drift.
- Part B: --trust-remote-code opt-in default-deny on soup train /
chat / serve / data download / eval auto. KNOWN_SAFE_PREFIXES
allowlist (15 first-party orgs) suppresses warning panel.
Replaces 9 unconditional trust_remote_code=True call sites in
the SFT path. Non-SFT trainers + diff/export/merge/infer/generate
still hardcode trust_remote_code=True — documented v0.36.x patch.
- Part C: chat-template hardening. Tokenizers without chat_template
raise loudly instead of silent f"{role}: {content}" fallback.
New data.chat_template (registered name or raw Jinja). Filesystem
-touching Jinja directives (include/import/from/macro/extends)
blocked at config-load. Override application warns that soup push
will persist the new Jinja into tokenizer_config.json.
- Part D: OOM-probe auto batch-size. New
training.auto_batch_size_strategy: auto|static|probe. Try-halve
-then-double-to-ceiling loop, max 8 doublings, ceiling = static
× 4. ~/.soup/batch_cache.json (0600 perms, env-override
containment-checked against ~/cwd/tempdir). make_cache_key
rejects bool inputs.
Net +134 tests (4115 → 4249). All 5 review-agent waves clean
before commit; 5 HIGH / 10 MEDIUM / 5 LOW findings fixed in one
review-fix wave.
Smoke: python -m soup_cli.cli version → soup v0.36.0; all 5 new
--trust-remote-code flags surface in --help; ruff clean; pytest
4249 passed / 3 skipped / 0 failed in 2m41s on Windows py3.10.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Alpamys
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ff55e751ab |
fix(v0.33.0): review-wave findings (CRITICAL + HIGH + MEDIUM + LOW)
Addresses findings from 5-agent review wave (python-reviewer, code-reviewer, security-reviewer, tdd-guide, smoke-verification). CRITICAL: - cans/run.py _deploy_target ollama path: rglob *.gguf result is now realpath+commonpath checked against extract_dir before forwarding to `soup deploy ollama --gguf`. Prevents a crafted symlink in the can from making rglob point at an arbitrary on-disk path. HIGH: - cans/publish.py: removed dead update_repo_settings + bare-except tag block (was a no-op network round-trip). Tag attachment via README front-matter is documented as a v0.33.x docs follow-up. - registry/attach.py lookup_entry_by_output_dir: emits ResourceWarning when the 1000-row scan limit is hit (was a silent miss). - data/collators.py CrossDocCollator: stops mutating input dicts via pop() — uses get + dict comprehension. HF Dataset rows are cached and reused; mutation broke subsequent batches silently. Bare-except now logs at DEBUG level so production degradation is inspectable. - monitoring/callback.py _write_spike_recovery_hint: added is_under_cwd guard. args.output_dir came from raw HF TrainingArguments without separate path-containment check. - trainer/rewards.py MACOS_SANDBOX_PROFILE: narrowed (allow mach-lookup) to a 3-name allowlist (SecurityServer, notification_center, opendirectoryd.libinfo). Broad mach-lookup permitted DNS / NSURLSession via launchd, defeating (deny network*). - cans/run.py: PermissionError → ValueError so a caller wrapping in `except OSError` cannot silently swallow the consent gate. PermissionError is an OSError subclass. - commands/can.py run_cmd: assigns result=None up front + explicit None guard so a future _fail bypass cannot trigger NameError on result. - utils/v028_features.py: added type annotations on apply_v028_speed_memory (model: Any, tcfg: TrainingConfig via TYPE_CHECKING, console: Console) and warn_unsupported_features. - cans/run.py: confirm_callback now annotated Callable[[Manifest], bool] for IDE introspection. - tests/test_part_b.py reexec test: drops env-var contamination (RANK/WORLD_SIZE/LOCAL_RANK/ACCELERATE_*) before run, patches imported names on train module, and forces assertion that os.execvp was called — no more silent skip-on-bypass. - tests/test_part_d.py: added TestGenerateResponseSignature source-level guard that catches the lenient logits_processor mock silently passing. MEDIUM: - cans/run.py _run_subprocess: catches subprocess.TimeoutExpired and returns rc=124 (coreutils convention) so callers see a clean CanRunResult instead of an unhandled traceback after the 24h cap. - cans/run.py: temp dir created via mkdtemp is now cleaned up on extract_can failure (try/except + cleanup_extract_dir). - cans/run.py cleanup_extract_dir: switched startswith path check to os.path.commonpath (project-standard idiom; Windows-safe). - cans/schema.py DeployTarget._safe_relpath: normalises mixed separators before splitting on '/' so foo/..\bar can no longer bypass the .. check. - utils/lr_finder.py run_lr_sweep: removed redundant local `import math as _math` (math already at module level). LOW: - eval/gate.py _parse_judge_url: removed bare http:// catchall after scheme allowlist. Defence-in-depth for callers that bypass the Pydantic GateTask validator. - utils/auto_quant.py evaluate_candidate: latency mean now divides by *completed* prompts (excludes crashed). Crashed candidate no longer appears artificially fast. - utils/auto_quant.py Candidate.__post_init__: explicitly rejects bool in score / latency_ms (bool is a subclass of int, was sneaking past). - utils/mii.py: removed `noqa: F401` on Optional import (now actually used in type annotation since we restored it). Tests added (+7, total 3811→3818): - test_part_a_wave1: attach_artifact outside-cwd rejection. - test_part_a_wave2: PermissionError→ValueError migration in 2 tests. - test_part_c: CrossDocCollator mismatched doc_lengths fallback, does-not-mutate-input-dict regression guard. - test_part_d: source-level _generate_response signature guard. - test_part_e: should_recover at max_attempts, outside-cwd skip. Lint: clean. Full suite: 3818 passed in 156s. Findings deliberately not actioned (with rationale): - code-review M1 (mii Pydantic at import-time): forward-ref resolution requires module-level definitions for FastAPI; documented in mii.py. - code-review M4 (supports_v028_features vs validator divergence): the v0.33.0 schema validator was renamed to _validate_v028_speed_memory_supported_tasks and now imports supports_v028_features — they cannot drift. - python-review LOW (_deploy_target vllm silent no-op): documented in the docstring as advisory; logging requires a console arg the helper does not currently take. - security-review LOW 8/9 (TOCTOU window, CLONE_NEWPID): theoretical; documented in CLAUDE.md security section in the next commit. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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55d1b9312c |
feat(speed,memory): v0.28.0 features go multi-trainer (v0.33.0 Part C)
Closes #43, #44, #47. #43 Multi-trainer wiring (sft/dpo/pretrain): - New utils/v028_features.apply_v028_speed_memory(model, tcfg, base_model, console) — single shared helper for use_cut_ce, quantization_aware="fp8", kernel_auto_compose. Each feature degrades silently to a yellow advisory if the underlying lib is missing; never crashes training kick-off. - Helpers supports_v028_features(task) and warn_unsupported_features(tcfg, task) drive both the schema validator and runtime advisories. - soup_cli/trainer/dpo.py and trainer/pretrain.py now call the helper after model load (post-LoRA, post-QAT) — same hook point as SFT. - soup_cli/config/schema.py validator _validate_v028_speed_memory_sft_only renamed _validate_v028_speed_memory_supported_tasks; allowlist now {sft, dpo, pretrain}. GRPO/KTO/ORPO/SimPO/IPO/PPO/RewardModel/Embedding still error out at config-load with a precise multi-trainer message. #44 Selective gradient-checkpoint hooks: - New utils/gradient_ckpt.install_selective_hooks(model, granularity) iterates ``model.named_modules()`` looking for transformer-block-shaped names (numeric suffix on layer path), wraps each module's ``forward`` with torch.utils.checkpoint.checkpoint based on tier: - selective: only attention sub-modules - medium: every second transformer block - full: every transformer block - Returns hook count so callers can fall back to HF native checkpointing when zero blocks were found. #47 CrossDocCollator: - New soup_cli/data/collators.CrossDocCollator wraps any base data collator and injects a block-diagonal causal ``cross_doc_attn_mask`` built from per-example ``doc_lengths``. Preferred over TRL's ``packing_strategy="attention_free"`` flag (best-effort across TRL versions). Degrades gracefully when doc_lengths is missing or shapes don't match — base attention_mask preserved, no crash. Tests: +16 in tests/test_part_c.py covering apply_v028_speed_memory (no-features, cut_ce graceful failure), supports/warn helpers extension, schema gate (dpo + pretrain accept, kto still rejects), selective hook installation across full/medium/selective with fake transformer-shaped models, CrossDocCollator passthrough + strip + injection. One existing test in test_training_speed.py updated: dpo+use_cut_ce now accepted. Known limitations: - 7 trainers (GRPO/KTO/ORPO/SimPO/IPO/PPO/RewardModel/Embedding) still reject v0.28.0 flags at config-load. Each is a 5-line addition once schema validation is satisfied; tracked as a v0.33.x follow-up. - install_selective_hooks doesn't undo earlier hooks — caller must be re-init aware. Not an issue for the typical "construct wrapper, train, exit" flow but worth noting. - CrossDocCollator emits ``cross_doc_attn_mask`` (not ``attention_mask``) to avoid clobbering the base collator's contract; downstream consumers must read the new key explicitly. The plan calls for "preferred over TRL's packing_strategy" which we satisfy via opt-in collation, not silent override. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Alpamys
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ddab34115c |
feat(v0.26.0): Parts B-E — Eval Gate, Trace-to-Pref, Quant-Check, Soup Cans
Closes the v0.26.0 "Red and Blue Ocean" flywheel after Part A (Registry): Train (eval-gated) -> Registry -> Deploy (quant-check) -> Trace-to-Pref -> Train. Part B — Eval-Gated Training: - soup_cli/config/schema.py: EvalGateConfig (enabled/suite/every_n_epochs/ regression_threshold/baseline/on_regression) + TrainingConfig.eval_gate field - soup_cli/eval/gate.py: EvalSuite, GateTask, run_gate, resolve_baseline, load_suite; baselines from registry:// or file - soup_cli/monitoring/callback.py: on_epoch_end + _run_eval_gate with fail-safe error handling (structured errors treated as regressions under on_regression=stop) - soup_cli/commands/train.py: --gate <suite.yaml> shortcut flag - soup_cli/commands/eval.py: gate subcommand (stub generator; live scoring v0.26.1) Part C — Trace-to-Preference: - soup_cli/data/traces/: parse_langchain, parse_openai, parse_soup_serve; build_pairs from thumbs_up / regenerations / user_edit - soup_cli/commands/data.py: from-traces + review subcommands - PII warning panel, 100,000-line cap, path containment, Literal validation Part D — Quant-Lobotomy Checker: - soup_cli/eval/quant_check.py: classify_delta (OK/MINOR/MAJOR), run_quant_check, resolve_model_ref with artifact kinds filter, table/json/markdown renderers - soup_cli/commands/eval.py: quant-check subcommand Part E — Soup Cans: - soup_cli/cans/: Manifest + DataRef (Pydantic v2); pack_entry + fork_can (100MB cap, dunder-key guard); safe tar extraction (filter='data' on py3.12+, narrow fallback, manual symlink rejection + commonpath check) - soup_cli/commands/can.py: pack/inspect/verify/fork subcommands Shared utility: - soup_cli/utils/paths.py: single is_under_cwd helper replacing 5 duplicates (os.path.realpath + commonpath — Windows 8.3 short-name safe) Tests: 103 new (29 eval_gate + 24 trace_to_pref + 23 quant_check + 27 cans) Full suite: 2511 passed on Windows Python 3.10. Security hardening (review-driven, all severities fixed): - EvalGateConfig bounds; GateTask null-byte + judge URL scheme allowlist - Narrow except in _safe_extract so TarError from filter='data' is not swallowed - resolve_model_ref artifact kinds filter (avoid wrong artifact) - Manifest.author cap + null/newline rejection; created_at ISO-8601 validation - fork_can dunder-key + null-byte rejection (prototype pollution prevention) - fork_can size cap (100MB matches pack_entry) - inspect_can/read_config refuse paths outside cwd Docs: - README.md: v0.26.0 "New in" block (flywheel); 43 recipes; all new commands in All Commands list; version examples bumped to 0.26.0; Windows-safe arrows - CLAUDE.md: architecture + test table + schema + CLI + security section extended with B/C/D/E; phase vs Part terminology clarified; release checklist step 18 adds Known Limitations section; step 20 adds comment template; step 21 adds completeness check via gh issue list --milestone - SECURITY.md: per-Part security notes (B/C/D/E) under v0.26.0 - CONTRIBUTING.md: test count + directory tree updates Local smoke: version, eval gate, eval quant-check (table + json), data from-traces, data review, can pack/inspect/verify/fork — all happy-path end-to-end. Fixed Unicode arrows (U+2192) in can.py + gate.py that crashed on Windows CP1252 consoles. Deferred to v0.26.1 (known limitations, filed as issues post-release): - eval gate/quant-check live model scoring (stub generator currently) - data from-traces quality.py judge validation; serve --trace-log collector - can run + can publish + orchestrator - eval --attach-to-registry flag; export auto-artifact registration Co-Authored-By: Claude Opus 4.6 (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> |
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Alpamys
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68d958d14c |
fix: address python review — extract parse_json_array, narrow exceptions
- Extract _parse_json_array into soup_cli/data/providers/_utils.py to avoid circular imports between generate.py and provider modules. - Narrow bare except Exception in detect_ollama to httpx.HTTPError/OSError with debug logging instead of silent swallow. |
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Alpamys
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ea8f785b50 |
feat: add synth data gen pro with multi-provider, templates, quality pipeline (v0.20.0)
New providers: Ollama (localhost-only), Anthropic Claude (env-only API key), vLLM (SSRF-protected). Domain templates: code, conversation, qa, preference, reasoning. Quality pipeline: --validate, --filter, --dedup, --quality-pipeline. 84 new tests, 1669 total. Security: SSRF protection on all providers, output path traversal prevention, rate limiting. |
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Alpamys
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20c2f4e515 |
fix: use AutoModel for audio, is_relative_to path check, early librosa import
- Use AutoModel instead of AutoModelForCausalLM for audio-language models (Qwen2-Audio, Whisper don't work with causal LM auto class) - Use Path.is_relative_to() for path traversal check (symlink-safe, Python 3.9+) - Fail fast with helpful error if librosa not installed before dataset processing |
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Alpamys
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0b7759898c |
fix: address review findings — immutable rows, response guard, GPU cleanup
- Stop mutating dataset rows in-place in _validate_audio_files (use shallow copy) - Guard _generate_server response parsing against unexpected JSON shape - Add empty dataset guard in _prepare_audio_dataset - Free GPU memory after perplexity scoring in compute_perplexity_scores |
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Alpamys
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3d66b41d00 |
v0.17.0: data quality filters, audio modality, SGLang backend, server provider
New features: - soup data filter: quality filters with perplexity and coherence scoring - modality: audio — Qwen2-Audio, Whisper fine-tuning with audio data format - --backend sglang for soup serve (SGLang high-throughput inference) - --provider server for soup data generate (local OpenAI-compatible servers) - Audio template: soup init --template audio Security hardening: - Server provider SSRF validation (scheme whitelist, localhost-only HTTP) - Audio file path traversal protection (resolved paths confined to audio_dir) - trust_remote_code warning panels for audio models and SGLang runtime 1348 tests, 56 test files, 58.8% coverage, ruff clean. |
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Alpamys
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cbc0a0e558 |
v0.16.0: embedding models, ONNX/TensorRT export, speculative decoding
New features: - task: embedding — fine-tune sentence embedding models (BGE, E5, GTE) with contrastive, triplet, or cosine loss and configurable pooling - soup export --format onnx — ONNX export via optimum - soup export --format tensorrt — TensorRT-LLM export for GPU inference - soup serve --speculative-decoding — draft model for 2-3x faster generation (transformers assisted generation + vLLM native speculative decoding) - soup init --template embedding — new template for embedding fine-tuning Security: - ONNX export: removed unconditional trust_remote_code, added warning - Speculative decoding: SSRF protection (URL blocked), warning panel - vLLM speculative: URL validation rejects http:// schemes - TensorRT export: separated try/except per subprocess call - Embedding config: Literal constraints, margin gt=0 validation 1270 tests, 52 test files, 58% coverage |
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Alpamys
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15a6daf342 |
feat: v0.14.0 — pre-training + MoE support
Add continued pre-training task and Mixture of Experts model support:
- `task: pretrain` for continued pre-training on raw text data
- `plaintext` data format ({"text": "..."} JSONL or .txt files)
- MoE model detection (Mixtral, Qwen3 MoE, DeepSeek V3, DBRX, OLMoE)
- ScatterMoE LoRA (`moe_lora: true`) targets expert FFN + attention layers
- `moe_aux_loss_coeff` for router load-balancing loss
- Templates: `soup init --template pretrain` and `--template moe`
- 85 new tests across test_pretrain.py and test_moe.py (1002 total)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Alpamys
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0be8a03a8a |
v0.12.0: ORPO/SimPO/IPO trainers + DoRA/LoRA+/GaLore
v0.11.0 — Alignment methods: - ORPO (task: orpo) — wraps trl.ORPOTrainer, no reference model needed - SimPO (task: simpo) — wraps trl.CPOTrainer with loss_type='simpo' - IPO (task: ipo) — wraps trl.DPOTrainer with loss_type='ipo' - Templates: soup init --template orpo/simpo/ipo - Init wizard, train routing, sweep shortcuts for all three v0.12.0 — Advanced PEFT: - DoRA (use_dora: true) — weight-decomposed LoRA in all 9 trainers - LoRA+ (loraplus_lr_ratio) — different lr for A and B matrices - GaLore (use_galore: true) — memory-efficient full-param training - GaLore validation: incompatible with quantization and unsloth Security: - experiment_name path traversal validation (no / \ : null bytes) - GaLore optim_args type enforcement before string interpolation Tests: 877 passed (was 746), 42 test files, 56.98% coverage |
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Alpamys
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428c0f09a4 |
v0.10.1: Fix 6 bugs from manual testing report
- BUG-001: Replace Unicode arrows/dashes with ASCII in all console output to fix UnicodeEncodeError on Windows cp1252 (~10 commands affected) - BUG-002: PPO trainer uses inspect.signature to detect trl parameter names (ppo_epochs vs num_ppo_epochs) for trl 0.28.0 compatibility - BUG-003: Add get_compute_dtype() - uses float32 on CPU, bfloat16/float16 on CUDA. Fixes dtype mismatch in reward model and all trainers - BUG-004: Add warning when using quantization on CPU - BUG-005: Fix dtype -> torch_dtype in diff.py model loading - BUG-006: Pin wandb<0.18.0 to avoid trl import conflict, add runtime guard 13 new tests (624 total), ruff clean. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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Alpamys
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5b7ad3c358 |
Add multimodal vision fine-tuning support (Phase 6) — v0.5.0
- Add `modality: vision` config option for vision-language model training - Add LLaVA and ShareGPT4V data format detection and conversion - Add `image_dir` field in DataConfig for resolving image paths - Add vision model loading via AutoModelForVision2Seq + AutoProcessor in SFT trainer - Add `soup init --template vision` with LLaMA-3.2-Vision config - Add image statistics display in `soup data inspect` for vision datasets - Add Pillow as optional `vision` extra dependency - Add Pillow to `soup doctor` dependency checks - 51 new tests (455 total), ruff clean Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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Alpamys
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2aaa87fb4e |
Phase 2: experiment tracking, data tools, model evaluation
- Add SQLite experiment tracker (~/.soup/experiments.db) with auto-logging of config, per-step metrics, hardware info, and eval results - Add soup runs commands: list, show (with plotext loss curves), compare, delete - Integrate tracker into soup train (auto start_run/finish_run/fail_run) - Add soup data convert (alpaca/sharegpt/chatml bidirectional conversion) - Add soup data merge (concatenate datasets with optional shuffle) - Add soup data dedup (MinHash near-duplicate removal via datasketch) - Add soup data stats (length percentiles, token counts, language detection) - Add soup eval (lm-evaluation-harness wrapper with tracker integration) - Add reverse format conversion: messages_to_format() in data/formats.py - Add extended_stats() to data/validator.py - Update monitoring callback to log metrics to tracker - Add plotext to deps, datasketch as optional [data] dep - Update README and CLAUDE.md with Phase 2 docs - 70 tests passing, ruff clean Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> |
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Alpamys
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a2a0f2cab3 |
Phase 1.5: add soup chat, soup push, DPO trainer + smoke tests
- soup chat --model ./path: interactive terminal chat with LoRA adapters
(auto-detects base model, supports /quit /clear /system commands)
- soup push --model ./path --repo user/model: upload to HuggingFace Hub
(auto model card generation, token from env/cache/flag)
- DPO trainer: full DPOTrainerWrapper with LoRA + quantization support
(configurable dpo_beta, preference data format {prompt, chosen, rejected})
- Smoke tests: real SFT + DPO training with tiny-gpt2 (pytest -m smoke)
- SFT trainer: fallback for models without chat_template
- Updated README, schema, formats, pyproject.toml, .gitignore
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Alpamys
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d167cd4ddd |
Fix Python 3.9 compatibility + add .claude project settings
- Replace `str | list[str]` with `Union[str, List[str]]` (3.9 compat) - Replace `str | None` with `Optional[str]` in validator.py - Replace `Live | None` with `Optional[Live]` in display.py - Add .claude/settings.json: auto-allow git, ruff, pytest, pip, soup Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> |
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Alpamys
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7433029d19 |
Fix all ruff lint errors and failing test
- Fix 23 ruff errors: line too long, unused imports, ambiguous vars - Fix validator: empty string is valid data, only count None as empty - Remove unused imports in display.py and validator.py - Rename ambiguous `l` vars to `part`, `entry`, `length` - Break long lines in callback.py, display.py, sft.py, constants.py All 20 tests passing, ruff clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> |
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Alpamys
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d6e932a1d3 |
Initial project setup: CLI skeleton + config + trainer + data pipeline
- Typer CLI: soup init, soup train, soup data inspect/validate - Pydantic config schema with YAML loader and validation - Data pipeline: JSONL/JSON/CSV/Parquet + HuggingFace datasets - Format detection: Alpaca, ShareGPT, ChatML (auto-detect) - SFT trainer wrapper over transformers + peft + trl - QLoRA/LoRA support with auto batch size estimation - GPU detection (CUDA/MPS/CPU) and memory calculation - Rich live terminal dashboard for training monitoring - Config templates: chat, code, medical - Tests (pytest) + GitHub Actions CI - MIT license Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> |