76 lines
4 KiB
Markdown
76 lines
4 KiB
Markdown
# Changelog
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All notable changes to sekft, the shell-operator SFT trainer behind the
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[posix-sdc](https://huggingface.co/datasets/tiararodney/posix-sdc) experiment,
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are documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and the project follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.0.3] - 2026-06-18
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### Changed
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- The README intro now states up front that this is **not tool-calling**: sekft
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trains shell operation, not function-calling; the model is given no typed tool
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API or JSON-schema action list, and writes plain-text commands at a real prompt
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with the whole system as its action space.
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## [1.0.2] - 2026-06-18
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### Fixed
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- The generation operators (`sekft-eval`, `sekft-resident`) passed the
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`BatchEncoding` from `apply_chat_template(..., return_tensors="pt")` straight
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to `model.generate`, which does `inputs.shape[0]` and raised `AttributeError`
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on transformers ≥ 5 — the holdout eval crashed on its first scenario. 1.0.1
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fixed only the trainer's masking; this sweeps the generation path too. A shared
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`_input_ids` helper and a `render_prompt_ids` function now extract the id
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tensor for both operators, with unit tests for the BatchEncoding and bare
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shapes.
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## [1.0.1] - 2026-06-18
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### Fixed
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- `build_masked_example` could not derive the assistant mask on transformers
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≥ 5: `apply_chat_template` now returns a `BatchEncoding` (`{input_ids: [...]}`)
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where 4.x returned a bare `list[int]`, so the render was treated as a dict and
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the prefix-differencing spuriously raised "chat template is not additive" on
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every real model. The id sequence is now extracted either way; verified the
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assistant-only mask against `mistralai/Mistral-7B-Instruct-v0.2`. The
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fake-tokenizer test gained a `BatchEncoding`-returning variant so this can't
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regress.
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## [1.0.0] - 2026-06-18
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First release: the training and evaluation pipeline that turns posix-sdc
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trajectories into a fine-tuned shell operator.
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### Added
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- `sekft-train`: LoRA / QLoRA supervised fine-tuning of a base model on
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shell-operation trajectories, with an **assistant-only loss mask** derived by
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token-prefix differencing — the commands and the terminal `exit` / `panic`
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token are trained; the environment turns (orientation, prompts, command
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output) are masked to `-100`. The render uses the tokenizer's own
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`apply_chat_template`, so training matches what the serving harness sends
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(train = serve), with `normalize_for_template` canonicalising trajectories for
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instruct templates that have no system role and require strict user/assistant
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alternation.
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- Three sources of training data: a directory of raw rollout `.json`
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(keep-filtered), a curated `.jsonl` corpus, or the published posix-sdc corpus
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over the Hugging Face Hub (`--hub`).
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- `--inspect` for mask and token statistics without training, and structured
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stderr logging across every phase (`-v` / `-q`): per-trajectory and progress
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lines while the corpus is tokenized, dataset accounting that warns on dropped
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(over-length / empty-mask) trajectories, and the per-step training curve.
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- `sekft-eval`: behavioural evaluation that drops the tuned model into held-out
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scenarios with no scaffold and scores whether it operates and terminates.
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- `sekft-resident`: a resident-base harness that loads the base model once and
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fits several adapters without reloading, for paired / STaR-style runs.
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- Packaging: the `tiararodney.sekft` namespace package with `sekft-train`,
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`sekft-eval`, and `sekft-resident` console scripts; a typed (`py.typed`),
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mypy-strict codebase; an optional `[gpu]` extra (torch / transformers / peft);
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and a dependency on `posix-sdc[hub]`. Released under GPL-2.0.
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[1.0.3]: https://git.code.tiararodney.com/tiara/sekft/compare/v1.0.2...v1.0.3
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[1.0.2]: https://git.code.tiararodney.com/tiara/sekft/compare/v1.0.1...v1.0.2
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[1.0.1]: https://git.code.tiararodney.com/tiara/sekft/compare/v1.0.0...v1.0.1
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[1.0.0]: https://git.code.tiararodney.com/tiara/sekft/releases/tag/v1.0.0
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