layer-1
Datasets
All datasets matching “layer-1”sae-activations-llama-3.1-8b-layer19-lmsys-chat-1m
SAE Feature Activations — Llama 3.1 8B Instruct, Layer 19 (LMSYS-Chat-1M)
This dataset contains Sparse Autoencoder (SAE) feature activations extracted from layer 19 of Meta's Llama 3.1 8B Instruct on conversations from LMSYS-Chat-1M.
It also has natural language explainations of features generated by GPT OSS 120B. See subset 4 for details.
The SAE used is Goodfire/Llama-3.1-8B-Instruct-SAE-l19, which decomposes layer-19 residual stream activations into interpretable sparse features.… See the full description on the dataset page: https://huggingface.co/datasets/scaleinvariant/sae-activations-llama-3.1-8b-layer19-lmsys-chat-1m.sovits4.0-768vec-layer12sovits4.0-768vec-layer12底模，
新增large底模，由m4singer+vctk数据集训练，294k为loss14.75的，320k为最终训练步数。
91.2k的d和g模型为loss 16.04的，
100k的d和g模型为最终训练步数，
需要改名为D_0.pth和G_0.pth使用。
新增两组d&g底模
144k是在a10上训练，loss低至14.1的，
216k是在a10上训练的最终训练步数。
classify_layer18_split2classify_layer17_split2llama8b-layer15-sae-probes
Llama8B Sparse Probing Activations
This repository contains activation data accompanying the paper Learning a Generative Meta-Model of LLM Activations.
Project page: https://generative-latent-prior.github.io
Code: https://github.com/g-luo/generative_latent_prior
Quick Start
With this data, you can evaluate GLPs via sparse probing.
The activations are derived from the binary classification datasets from Kantamneni et. al., 2025.
The activations are taken only from… See the full description on the dataset page: https://huggingface.co/datasets/generative-latent-prior/llama8b-layer15-sae-probes.classify_layer19_split1