11258-104x9-se

Ignore the source, just grab the weights.

11258-104x9-se.pb.gz

Ender128-90l

Ignore the source, just grab the weights.
ender128-90l.pb.gz

Ender128-88

Ignore sources, just get the weights.

ender128-88.pb.gz

LeelaChess bootstrap network

Bootstrap LeelaChess Network built from CCRL using CLR. This is not protobuf, so you may need to unzip to get it to load quickly enough. Next net will be protobuf.

Ender 5

Ender 5 net.

Ender trained on batch 4

Ender net trained on batch1-4.

ender4.txt.gz

Ender CLR 200k

ender-0.1

Add files via upload

Ender62

Ender62 endgame network.
ender62.txt.gz

Ender38

Ignore the assets. Go for the weights file below.

This is a 64x6 nn trained to play 12 man endgames and below. Leela sucks on fen’s because they have no history. I use @dje’s no history PR to solve that problem. LeelaChessZero/lc0#305

Batch 38 (plus maybe 5 self-play batches) had best performance so far on my 149 position endgame test suite.

Ender 38 - 5 sec, 2 thr, no prune
Success rate: 62.42% (93/149)

And versus two networks head to head 1min+1sec, it was even with a late test10 256x20, and soundly beat a late test9 64x6. All 12 man positions are played both ways.

Score of Ender38 vs T11258: 33 - 33 - 42  [0.500] 108
Score of Ender38 vs T9149: 36 - 23 - 41  [0.565] 100

ender38.txt.gz

Ender128-80

Ignore the source, just download the weights.

ender128-80.pb.gz