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license: apache-2.0
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# Trained Sparse Autoencoders on Pythia 2.8B
I trained SAEs on the MLP_out activations of the Pythia 2.8B dataset. I trained using github.com/magikarp01/facts-sae, a fork of github.com/saprmarks/dictionary_learning designed for efficient multi-GPU (not yet multinode) training. I have checkpoints saved every 10k steps, but I have not uploaded them all: message me if you want more intermediate checkpoints.

The goal was originally to analyze these SAEs specifically to determine how well they contribute to performance on a [Sports Facts](https://www.lesswrong.com/posts/iGuwZTHWb6DFY3sKB/fact-finding-attempting-to-reverse-engineer-factual-recall) dataset. 
I'm currently working on some other projects so I haven't actually had time to do this, but hopefully in the future some results might come out of these SAEs.

## SAE Setup
- **Training Dataset**: Uncopyrighted Pile, at monology/pile-uncopyrighted
- **Model**: 32-layer Pythia 2.8B
- **Activation**: MLP_out, so d_model of 2560
- **Layers Trained**: 0, 1, 2, 15
- **Batch Size**: 2048 for layer 15, 2560 for layers 0, 1, 2
- **Training Tokens**: 1e9 for layers 15, 0, 2, slightly less than 2e9 for layer 1. 
- **Training Steps**: 4e5 for layers 0, 2, 5e5 for layer 15, 7.5e5 for layer 1
- **Dictionary Size**: 16x activation, so 40960

## Training Hyperparamaters
- **Learning Rate**: 3e-4
- **Sparsity Penalty**: 1e-3
- **Warmup Steps**: 5000
- **Resample Steps**: 50000
- **Optimizer**: Constrained Adam
- **Scheduler**: LambdaLR, linear warmup lr between 0 and warmup_steps

## SAE Metrics
![2.8b Layer 0 390k Steps](results/2.8b_l0_390k.png)
![2.8b Layer 1 390k Steps](results/2.8b_l1_390k.png)
![2.8b Layer 1 740k Steps](results/2.8b_l1_740k.png)
![2.8b Layer 2 390k Steps](results/2.8b_l2_390k.png)
![2.8b Layer 15 490k Steps](results/2.8b_l15_490k.png)

## Thanks
Thanks to Nat Friedman/NFDG for letting me use H100s from the Andromeda Cluster during downtime, and thanks to Sam Marks/NDIF for the original SAE training repo and for helping me distribute the SAEs. Work done as a late part of my MATS training phase with Neel Nanda.