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initial upload: clean fineweb collection, math probes, evidence, v1 partial descriptions
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metadata
license: apache-2.0
tags:
  - interpretability
  - activations
  - qwen3
  - delta-nla
  - natural-language-autoencoder
pretty_name: Delta-NLA warm-start data for Qwen3-8B

Delta-NLA warm-start data (Qwen3-8B)

Per-token, per-layer records of what one transformer block of Qwen/Qwen3-8B changed, built for training a Delta natural-language autoencoder: a verbalizer that sees the residual stream before (X) and after (Y) a block and a reconstructor that must recover the update Δ = Y − X from the description alone.

Code: https://github.com/syvb/metamodelling

Files

  • fineweb/records.jsonl — one record per (document, position, layer): norms, cosines, causal effect of removing the update (all / attention part / MLP part) on final next-token log-probs, attention sources. Layers 6, 12, 18, 24, 30; 1,500 FineWeb documents × 3 positions.
  • fineweb/vec_*.npz — fp16 vectors per record: X (input to the block), d (= Y − X), d_attn, d_mlp; ids aligns with records.jsonl.
  • fineweb/docs.jsonl — token ids of each (randomly truncated, ≤512 tokens) document.
  • fineweb/evidence.jsonl — human-readable evidence per record: decoded tokens, fixed-scale logit-lens readings, lens probability shift, per-layer percentiles, source snippets (clipped at the current token), 1200-char context.
  • fineweb/mean_d.pt — per-layer mean update vectors (subtract before computing variance explained).
  • fineweb/descriptions_*.jsonl — natural-language descriptions of each update written by openai/gpt-5.6-luna from the evidence only (never the true next token). v34 = thought-style prompt (no token quotes); v1_partial = token-quoting prompt.
  • math/ — the same for 16 one-pass arithmetic / multi-hop prompts at all 36 layers, last two positions.

Conventions

Activations are Qwen3-8B residual stream, hidden_states[n] = input to block n. Lens of an update is W_U (g ⊙ Δ / rms(X)). Causal effect = final log-probs with the update at that position replaced by nothing (all), by the MLP part only (attention removed), or by the attention part only (MLP removed).