You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is derived in part from AI2's WildChat corpus, which is released under the AI2 ImpACT License and is itself gated. By requesting access you confirm that you accept WildChat's terms and will not redistribute the conversation text contained here.
Log in or Sign Up to review the conditions and access this dataset content.
NLA warmstart data (Qwen3-8B, layer 24)
Supervised-finetuning data for the two active natural-language-autoencoder (NLA) families. An NLA is an encoder/decoder pair over a language model's residual stream: a verbalizer (AV) turns one activation vector into English, and a reconstructor (AR) maps that English back to the activation. These are the warmstart sets the AV and AR are trained on before RL.
Companion weights (public, ungated):
asher577/nla-warmstarts ·
asher577/nla-rl-ckpts
Why this repo is gated
Roughly half the 8-bullet data and all of the focus data derive from AI2's
WildChat, which is gated and licensed under the AI2 ImpACT License. The
detokenized_text_truncated column contains that conversation text verbatim. Access
here is gated to mirror WildChat's own terms — please honour them.
The remaining ~51% of the 8-bullet data derives from FineFineWeb (public web text).
Contents
| path | rows | what |
|---|---|---|
free8/av_sft.parquet |
131,312 | 8-bullet verbalizer SFT (~49% WildChat / 51% FineFineWeb) |
free8/ar_sft.parquet |
66,047 | 8-bullet reconstructor SFT |
focus/av_sft.parquet |
249,746 | focus 4-aspect verbalizer SFT (100% WildChat) |
focus/ar_sft.parquet |
— | focus reconstructor SFT |
Every parquet ships with its <name>.nla_meta.yaml sidecar, which is required —
it carries the injection token ids, prompt templates, injection_scale and
mse_scale that the loaders assert against the live tokenizer at startup. Keep the
sidecar next to the parquet.
Schema
| column | type | meaning |
|---|---|---|
prompt |
list of chat messages | the instruction given to the model (contains the injection marker; not the source text) |
response |
string | the gold explanation to be learned |
activation_vector |
fixed_size_list[4096] | the Qwen3-8B layer-24 residual-stream activation, raw / unnormalized |
n_raw_tokens |
int | tokens of context behind the activation |
activation_layer |
int | 24 throughout |
doc_id |
string | <corpus parquet>:<split>:<row> — the source row |
detokenized_text_truncated |
string | the source text the activation was taken from |
Activations are stored raw: normalization happens at injection time
(injection_scale) and at loss time (mse_scale), both read from the sidecar.
The two families
- free8 (8-bullet) — a free-form compositional verbalizer: 8 bullets whose per-bullet reconstructions sum to the activation. Half web text, half chat.
- focus (4-aspect) — a WildChat verbalizer with four designated components: provenance/context, persona, user information, and model goal.
Both target Qwen3-8B at layer 24.
- Downloads last month
- 8