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;idsaligns withrecords.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 byopenai/gpt-5.6-lunafrom 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).