README.md

metadata

license: odc-by
configs:
  - config_name: av_sft
    data_files:
      - split: train
        path: av_sft_train.parquet
      - split: validation
        path: av_sft_val.parquet
  - config_name: ar_sft
    data_files:
      - split: train
        path: ar_sft_train.parquet
      - split: validation
        path: ar_sft_val.parquet
tags:
  - interpretability
  - activations

EasyNLA warmstart data (non-compositional, Qwen3-8B layer 24)

Supervised warm-start data for EasyNLA — train a natural-language autoencoder on Qwen3-8B: an activation-verbalizer (AV) that explains a layer-24 residual activation in natural language, and an activation-reconstructor (AR) that maps the explanation back to the activation.

Each row carries one raw (unnormalized) layer-24 activation captured while Qwen3-8B read a FineFineWeb document (position ≥ 50 tokens), plus a Claude-written "gold" explanation of what the model was representing there.

config	split	rows	docs
av_sft	train	363,961	36,734
av_sft	validation	7,307	737
ar_sft	train	363,988	36,734
ar_sft	validation	7,405	747

Columns

• prompt — chat messages for the AV (with the <INJECT> marker placeholder) in av_sft; the AR critic prompt text in ar_sft
• response (av_sft only) — the gold explanation the AV imitates
• activation_vector — raw layer-24 residual, list<float32>[4096]
• doc_id, n_raw_tokens, activation_layer, detokenized_text_truncated (the source text up to the extraction position)

Each parquet has a matching <name>.parquet.nla_meta.yaml sidecar — the contract EasyNLA trainers assert at startup (marker token IDs, prompt templates, d_model, scales). Keep them next to the parquets.

Splits

Document-level and deterministic: a doc goes to validation iff zlib.crc32(doc_id) % 1000 < 20 (nla/val_split.py::is_val_doc(doc_id, 20), ~2% of docs). No document's rows are split across train/validation, and the av_sft / ar_sft configs come from disjoint document sets by construction (stage-1 of the EasyNLA datagen pipeline).

Use with EasyNLA

huggingface-cli download asher577/easynla-warmstart-data --repo-type dataset \
    --local-dir <data>

python -m nla.train_sft --mode av --base-ckpt Qwen/Qwen3-8B \
    --parquet <data>/av_sft_train.parquet --sidecar <data>/av_sft_train.parquet \
    --heldout-parquet <data>/av_sft_val.parquet --save-dir <ckpts>/av
python -m nla.train_sft --mode ar --base-ckpt Qwen/Qwen3-8B \
    --parquet <data>/ar_sft_train.parquet --sidecar <data>/ar_sft_train.parquet \
    --heldout-parquet <data>/ar_sft_val.parquet --save-dir <ckpts>/ar

Provenance: source text from FineFineWeb (ODC-By); explanations generated with Claude (claude-sonnet-4-6); activations from Qwen/Qwen3-8B. The doc_id strings are opaque keys from the generating pipeline.