| --- |
| 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](https://github.com/asherps/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 |
|
|
| ```bash |
| 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. |
|
|
