Tracking Universal Features Through Fine-Tuning and Model Merging
We study how features emerge, disappear, and persist across models fine-tuned
on different domains of text. More specifically, we start from a base one-layer
Transformer language model that is trained on a combination of the BabyLM
corpus, and a collection of Python code from The Stack. This base model is
adapted to two new domains of text: TinyStories, and the Lua programming
language, respectively; and then these two models are merged using these two
models using spherical linear interpolation. Our exploration aims to provide
deeper insights into the stability and transformation of features across
typical transfer-learning scenarios using small-scale models and sparse
auto-encoders.
6710bd1963f0a6c38b3f9c53
Niels Horn
claimed_verified
2024-10-17T11:23:56.379Z
63ca9515145e6c9716fbf376
https://cdn-avatars.huggingface.co/v1/production/uploads/63ca9515145e6c9716fbf376/BXt9rJ5nIisijzFVLymai.png
Niels Horn
user
nilq
6710bd1963f0a6c38b3f9c54
Desmond Elliott
Tracking Universal Features Through Fine-Tuning and Model Merging
We study how features emerge, disappear, and persist across models fine-tuned
on different domains of text. More specifically, we start from a base one-layer
Transformer language model that is trained on a combination of the BabyLM
corpus, and a collection of Python code from The Stack. This base model is
adapted to two new domains of text: TinyStories, and the Lua programming
language, respectively; and then these two models are merged using these two
models using spherical linear interpolation. Our exploration aims to provide
deeper insights into the stability and transformation of features across
typical transfer-learning scenarios using small-scale models and sparse
auto-encoders.
Base Transformer models fine-tuned on different domains and merged using spherical linear interpolation reveal insights into feature stability and transformation in transfer learning.
Transformer language model spherical linear interpolation transfer-learning sparse auto-encoders