From Logit Lens to Tuned Lens: Reading the Intermediate Thoughts of Transformers
What happens inside an LLM between input and output? Logit Lens and Tuned Lens let us observe how Transformers build predictions layer by layer.
From Logit Lens to Tuned Lens: Reading the Intermediate Thoughts of Transformers
You type "The capital of France is" into an LLM and get back "Paris." But *where* inside the model did that answer actually form?
TL;DR
- Logit Lens projects intermediate hidden states to vocabulary space using the model's final unembedding matrix
- This reveals how Transformers build predictions incrementally, layer by layer
Related Posts
Claude Sonnet 4.6: Opus-Level Performance, 40% Cheaper — Benchmark Deep Dive
Claude Sonnet 4.6 scores 79.6% on SWE-bench, 72.5% on OSWorld, and 1633 Elo on GDPval-AA — matching or beating Opus 4.6 on production tasks. $3/$15 vs $5/$25 per M tokens. Analysis of Adaptive Thinking, Context Compaction, and OSWorld growth trajectory.
MiniMax M2.5: Opus-Level Performance at $1 per Hour
MiniMax M2.5 achieves SWE-bench 80.2% using only 10B active parameters from a 230B MoE architecture. 1/20th the cost of Claude Opus with comparable coding performance. Forge RL framework, benchmark analysis, pricing comparison.
Backpropagation From Scratch: Chain Rule, Computation Graphs, and Topological Sort
How microgpt.py's 15-line backward() works. From high school calculus to chain rule, computation graphs, topological sort, and backpropagation.