Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- continual-learning
+- task-arithmetic
+- kfac
+- clip
+- mammoth
+pipeline_tag: image-classification
+library_name: mammoth
+---
+
+# TAK
+
+This repository hosts artifacts for **TAK** in Mammoth (`--model tak`).
+
+TAK v2 applies Task Arithmetic in a continual-learning setup and regularizes task-vector interactions with a **dataless** approximation based on **Kronecker-Factored Approximate Curvature (KFAC)** to reduce representation drift and interference.
+
+## Paper
+
+- **Title**: Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature
+- **Venue**: ICLR 2026
+- **arXiv**: https://arxiv.org/abs/2602.17385
+
+## What is stored here
+
+This repository is intended to store artifacts needed to reproduce or run TAK v2, such as:
+
+- Fisher/KFAC cache files,
+- task vectors,
+- classifier heads and metadata,
+- optional checkpoints and run notes.
+
+For Fisher loading via Mammoth, keep naming consistent with the loader expectations, e.g.:
+
+- `<dataset>_task_<task_id>_aaT.pt`
+- `<dataset>_task_<task_id>_ggT.pt`
+- `<dataset>_task_<task_id>_ffT.pt`
+- `<dataset>_task_<task_id>_num_aaT.pt`
+- `<dataset>_task_<task_id>_num_ggT.pt`
+
+## How to use with Mammoth
+
+Example command with Fisher cache hosted on this repo:
+
+```bash
+uv run python main.py \
+  --model tak \
+  --dataset=seq-8visio \
+  --load_fisher 1 \
+  --fisher_cache hf://aimagelab-ta/TAK/vitb16/fisher_8vision/kfac/mc_full@main \
+  --alpha_merging 8.0 \
+  --batch_size 32 --virtual_bs_n 4
+```
+
+If you need to upload artifacts from local storage:
+
+```bash
+uv run python scripts/upload_to_hf.py \
+  --repo-id aimagelab-ta/TAK \
+  --repo-type model \
+  --local-dir /path/to/local/fisher \
+  --remote-dir fisher \
+  --pattern "**/*"
+```
+
+## Method overview
+
+- Continual adaptation is built from per-task deltas (task vectors).
+- During/after task training, KFAC statistics are used to approximate curvature terms for drift-aware regularization.
+- At inference, merged vectors are applied over the visual backbone under the selected merging strategy.
+
+## Limitations
+
+- Artifact compatibility depends on matching dataset split/order and preprocessing assumptions.
+- Fisher files are backend- and run-dependent; mixing incompatible runs can degrade results.
+- This repository may contain research artifacts, not production-hardened models.
+
+## Citation
+
+```bibtex
+@inproceedings{porrello2026dataless,
+  title={Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature},
+  author={Porrello, Angelo and Buzzega, Pietro and Dangel, Felix and Sommariva, Thomas and Salami, Riccardo and Bonicelli, Lorenzo and Calderara, Simone},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026}
+}
+```
+
+## Resources
+
+- Mammoth framework: https://github.com/aimagelab/mammoth
+- TAK v2 implementation: `models/tak.py`