Heretic is a command-line tool that automatically removes censorship (safety alignment) from transformer-based language models without expensive post-training. It implements an advanced form of directional ablation (abliteration) combined with a TPE-based parameter optimizer (Optuna) to find high-quality ablation parameters that minimize refusals while preserving the original model's capabilities. The tool supports dense, MoE, hybrid, and multimodal architectures, and can produce decensored models that rival human-crafted abliterations. The architecture is a monolithic Python application built on PyTorch, Hugging Face Transformers, and PEFT. It features a plugin system for extensible scorers (e.g., keyword rate, KL divergence, benchmark scores) and modifiers (e.g., standard abliteration, Arbitrary-Rank Ablation). The workflow involves loading a model, computing residual directions from good/bad prompt datasets, running multi-objective optimization trials to tune ablation parameters, and exporting the modified model as a merged full model or LoRA adapter. The tool also includes comprehensive reproducibility support, allowing others to exactly reproduce generated models. Key entry points are the `heretic` CLI command (defined in `src/heretic/main.py`), which orchestrates the entire pipeline. The codebase demonstrates strong engineering practices: extensive type hints, pydantic-based configuration with CLI/env/TOML sources, a well-designed plugin contract, and thorough testing for cross-platform reproducibility. External dependencies include PyTorch, Transformers, Accelerate, bitsandbytes, Optuna, PEFT, Hugging Face Hub, and several others, all pinned via uv.lock.
HyperView is a CLI-driven toolkit for downloading TradingView market data, backtesting Python trading strategies with TradingView-parity fill assumptions, and optimizing parameters via Bayesian search (Optuna). The system is organized as a monolith with subcommands: download-data, backtest, hyperopt, pine-optimize, pine-batch-optimize, list-data, list-strategies. It features a pluggable strategy framework with auto-discovery, 20+ TA-Lib indicator wrappers, a backtester that simulates intrabar SL/TP paths, and a two-stage Pine input optimizer that exports optimized Pine scripts with injected best parameters. The codebase is well-structured with dataclasses, type hints, and a rich CLI output using the `rich` library. It includes unit tests for the Pine optimization pipeline and CLI parser. The repository is primarily an application, though it contains data engineering patterns in its downloader and caching layers.