package: pyproject, Apache-2.0, install docs

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GkgLsACEF6CCP7EUfA5fZe

1 files changed, 9 insertions, 0 deletions

diff --git a/README.md b/README.md
index c89bb5c..d42f713 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,11 @@ Scaling study for **ZBP (zeroth-order backpropagation)** vs BP on decoder-only l
 forward queries only (score-space attention core + per-token SwiGLU FFN); linear maps and the score
 product are digital. See the main zobp repo for the method, theory (NSR ≈ c·d/n) and part-1/3 results.
 
+## Install
+```
+pip install -e .            # or: pip install -e .[dev] && pytest tests/
+```
+
 ## Layout
 - `src/zbp_scaling/zbp/` — vendored ZBP package (probes, estimators, ZBPBlock autograd)
 - `src/zbp_scaling/model.py` — OLMo2-style transformer (RMSNorm, SwiGLU, untied embeddings; learned
@@ -28,3 +33,7 @@ BP 312 ms/step; ZBP n=16 **8.7x**, n=64 **29x** (FLOPs-bound: probe batching is
 probe_chunk 8; the score core uses its own chunk <= 4 since its memory goes as chunk*B*H*T^2).
 Ladder arms: **bp / zbp_n16 / zbp_n64** per size (n=4 optional).  Headroom if needed: torch.compile on the
 query path; forward differences (n+1 instead of 2n queries) as a cheaper biased arm.
+
+## Citation
+Paper in preparation ("Backpropagation Without Jacobians"); see the main zobp research repo for the
+method, theory and part-1/part-3 results. License: Apache-2.0.