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README.md

@@ -18,8 +18,10 @@ Given a Commander (and optionally a partial decklist), suggests which other
 cards belong in the deck. Trained on 122,571 real public Commander decklists
 from [Moxfield](https://moxfield.com) ([dataset](https://huggingface.co/datasets/nsaroiu/moxfield-dump)).
 
-Full code, architecture writeup, and training history:
-[github.com/nsaroiu/mtg-card-embeddings](https://github.com/nsaroiu/mtg-card-embeddings).
+Training/evaluation code: [github.com/nsaroiu/mtg-deck-completion](https://github.com/nsaroiu/mtg-deck-completion)
+(the tokenizer + model + training/eval pipeline that produced these weights — not the
+Moxfield data-collection pipeline or the web UI built on top of this model, which live
+elsewhere and aren't public).
 
 ## Architecture
 
@@ -81,16 +83,17 @@ generalization via card content rather than memorized co-occurrence):
 Recall@50 0.27–0.41 across the same ratio range — meaningfully above chance,
 confirming the hybrid content embeddings carry real signal for unfamiliar
 commanders. Full breakdown by card-popularity tier, plus the synergy/pruning
-checkpoints' own numbers, in the GitHub repo.
+checkpoints' own numbers, in the GitHub repo's eval output.
 
 ## Usage
 
-There's no standalone pip package yet — loading these checkpoints currently
-requires the training code from the GitHub repo above:
+There's no standalone pip package yet — loading these checkpoints requires
+the tokenizer + model code from the GitHub repo above (`tokenizer/` and
+`training/`; no other part of that repo is needed to run inference):
 
 ```bash
-git clone https://github.com/nsaroiu/mtg-card-embeddings
-cd mtg-card-embeddings
+git clone https://github.com/nsaroiu/mtg-deck-completion
+cd mtg-deck-completion
 pip install -r requirements.txt
 ```
 
@@ -121,8 +124,12 @@ for name, score, source in results:
     print(f"{score:.3f}  [{source:7}]  {name}")
 ```
 
-See `training/complete_deck.py` for the CLI, or `ui/` for a full web UI
-built on this model.
+`training/ensemble.py` is also a CLI covering the same flow shown above
+(`--commander` — repeatable, for partner commanders — `--card`,
+`--chunk-size`/`--temperature` for the iterative synergy phase,
+`--prune-checkpoint` to enable the pruning pass). `training/complete_deck.py`
+exposes just one specialist checkpoint at a time, without the ensemble
+fusion, if that's all you need.
 
 ## Limitations