pyrecall
Forgetting detection and skill rollback for fine-tuned LLMs.
Fine-tune on new data and your model quietly loses what it already knew — coding ability, reasoning, safety guardrails. pyrecall catches it before it ships.
Install
pip install pyrecall # Python 3.11–3.14 · CUDA, MPS, and CPU
Quickstart
from pyrecall import Model
model = Model("meta-llama/Llama-3.2-1B")
model.snapshot("before")
model.learn("data.jsonl", epochs=3)
if not model.check().is_healthy:
model.rollback(to="before")
pyrecall init --model meta-llama/Llama-3.2-1B
pyrecall snapshot before_v1 # baseline before training
pyrecall learn train.jsonl --snapshot-after after_v1 # train + snapshot in one step
pyrecall check # compares last two snapshots
# exit 0 → ship exit 2 → pyrecall rollback before_v1
How it works
Benchmarks 180 prompts across 9 skill categories (reasoning, coding, safety, math, multilingual, and more) using log-likelihood scoring. After training, check diffs the scores and flags any category that drops past your threshold (default 10%). Only the LoRA adapter is stored per snapshot — a few hundred MB, not the full model.
Any causal LM on HuggingFace Hub is supported — Llama, Mistral, Phi, Gemma, Qwen, Falcon, GPT-2/Neo/J, and more. LoRA targets are auto-detected.
Docs
Full CLI reference, Python API, experiment tracker integrations (W&B, MLflow, Neptune), custom benchmarks, per-category thresholds, and more:
Contributing
git clone https://github.com/Arths17/Pyrecall
pip install -e ".[dev]"
pytest
Open an issue before large changes. MIT — LICENSE.
2 maintainers
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