Since you’re here...

We hope you will consider supporting us today. We need your support to continue to exist, because good entries are more and more work time. Every reader contribution, however big or small, is so valuable. Support "Chess Engines Diary" even a small amount– and it only takes a minute. Thank you.
============================== My email: jotes@go2.pl



Chess engine: Lambergar 0.6.0 (Windows and Linux)

-

 


Lambergar - UCI chess engine  Rating CEDR=2758
Lambergar is a chess engine developed in the Zig programming language. 

The name "Lambergar" is a nod to the Slovenian folk romance, Pegam and Lambergar, which recounts the epic struggle between Jan Vitovec and Krištof Lambergar (Lamberg). This narrative of fortitude and rivalry provided a fitting namesake for this chess engine.

Lambergar is licensed under the MIT License. Check out LICENSE.txt for the full text. Feel free to use this program, but please credit this repository in your project if you use it.

v.0.6.0:
Builds
Currently there are three builds:

x86-64-v3: AVX2 support, best for using with NN evaluation and should be a preferred choice for best performance.
x86-64-v2: popcount support, suitable for modern computers.
x86-64-v1: vintage version is for really old computers.
Release Notes
This is quite a major release, if the testing of this release goes well, next release will be version v1.0.0. This release brings several important changes:

Introduction of NN for evaluation. It uses halfKP NNUE architecture, although half of the size typically used. While the architecture of NN is of NNUE type, the engine code for evaluation is actually missing the UE part. This is planned for next release.
Engine code can now be compiled with latest version of zig, which is version 0.13.0. The major problem in the past was that the engine code was written in such a manner that newer releases of zig when compiled introduce many calls of memcpy which significantly reduced the speed of the engine (see discussion on Ziggit). By using profiler I was able to identify the problematic parts of code and refactor it.
Engine code can now be compiled into Debug mode. While this seems obvious and trivial requirement and good coding practice, zig compiler is pretty robust and produces in most cases normally working executables, so this was not my main priority at the start of the programming the engine. However, later it become obvious that I need to fix the bugs to be able to keep the code in "good shape".
Several bug fixes, the major one being related how the time for a single move is calculated in time format with "moves to go". This has now been fixed and all time controls should work correctly. The rest of the bugs were mainly related to integer overflows, etc.
Estimated engine strength is around 3040-3060 ELO.

Lambergar 0.5.2 results:
Lynx 1.4.04/4+44 Games
Ciorap-Bot 0.34/4+44 Games
Fatalii 0.6.14/4+44 Games
Shallow 53.5/4+34 Games
Inanis 1.2.13.5/4+34 Games
Lux 4.23/4+24 Games
Devel 4.0.2.33/4+24 Games
Vengeance 3.0.03/4+24 Games
Supernova 2.42.5/4+14 Games
Journeyman 2.12.5/4+14 Games
Patricia 2.0.12.5/4+14 Games
OliThink 5.10.52/4+04 Games
Wize 1.01/4-24 Games
Jet 1.21/4-24 Games
Arasan 24.2.10/4-44 Games
Saturn 1.30/4-44 Games
Midnight 90/4-44 Games
Obsidian 12.00/4-44 Games
K2 0.992/2+22 Games
Rengar 1.3.02/2+22 Games
Clovis III2/2+22 Games


github:https://github.com/jabolcni/Lambergar/releases/

Comments

Post a Comment

Popular posts from this blog

New Strong Engines Test, by Chess Engines Diary, 2024.04.12

-
Image
  💾  552 (!) games from the tournament download   👍 @chessenginesdiary  Country - Poland, City -  Malbork 🕓 Time 3'+3" 💻HP Pavilion i5-1035G1 8GB RAM 🖬 GUI-Banksia All  CEDR  317.321 games download  (01.04.2024 - 3'+3")  HypnoS 030424, Yuli GM Pro 16, Stockfish 16.1 and ShashChess 35 . These four engines scored the same number of points and placed at the top of the table in this strong tournament. Tech table: Engine KN/move NPS dep/mov time/mov mov/game time/game fails Alexandria 6.1.0 3218 576429 30.2 5.6 56.7 316.2 Berserk 13 4779 841025 36.7 5.7 56.0 318.1 Brainlearn 28 2275 366977 31.4 6.2 43.5 269.4 Caissa 1.18 5492 923486 30.2 5.9 50.4 299.7 Clover 6.1.19 4803 854377 31.4 5.6 62.2 349.4 Cool Iris 12.10 2201 356730 27.6 6.2 51.2 315.5 CorChess 20240331 2253 372774 27.4 6.0 52.2 315.8 Crystal 8 3582 571994 20.3 6.3 47.7 298.8 Fire 9.3 5225 876565 22.0 6.0 55.5 331.1 Fisherov chess monk 1.2 3487 619728 36.0 5.6 60.4 340.0 Hyp
Read more

New chess opening book: M11.2 (bin and ctg)

-
Image
  Book - CTG format Customers who wish to use the CTG format book will need a GUI capable of reading this format, ie. Chessbase GUIs.  Please save the book in your book folder. We strongly recommend that you "Optimize" the book in the Book options dialog so the tournament moves are chosen. The book is very wide even in tournament mode unlike most other books. A type of book format mainly used by chess programs from Chessbase , such as Fritz, Chess Base, Komodo, FatFritz, Houdini , etc. Book - BIN format (  Used by chess GUIs Arena, Banksia and Droidfish) Binary books have some important advantages, especially for computer chess engines: space-efficient, fast on accessing and searching, more useful information.  At the most simple form, they are just a conversion of above text-opening books into binary, in the ready form for software and save text-binary converting steps. However, the major number of top books is built and worked with hash values from transpositions. Often ea
Read more

New version chess engine: Lc0 BT4

-
Image
LcZero - CEDR Rating=3683 vBT4: Big transformer 4. New network architecture which builds off of BT3 by adding two types of auxiliary heads, future heads and categorical value heads. The categorical value heads predict a distribution over values of q rather than a WDL outcome distribution, and the future heads predict the moves that will be played over the next two plies. The hope is that these heads will give additional information to the net to improve training speed. We've also fixed half-precision training, so this model will be larger. BT4 training started in mid-October and is expected to take a few months. It has 15 layers with 1024 embedding size, 32 heads per layer, and 1536 emb size, for roughly a doubling in size over BT3.  Individual statistics:  Lc0 0.30.0 Stockfish 16 5/13 -3 13 Games Booot 7.2 6/10 +2 10 Games Critter 1.6a 8/9 +7 9 Games Dragon 3.2 4/8 +0 8 Games Stockfish 20230729 3.5/8 -1 8 Games ShashChess 33.2 3.5/8 -1 8 Games Fisherov chess monk 1.2 3/
Read more

Dragon NNUE by Komodo Chess - it's free!

-
Image
    Dragon by Komodo Chess Larry Kaufman: We have released Dragon at komodochess.com. Dragon uses NNUE (Neural Network Updated Efficiently) technology, originally developed for the game of shogi. Komodo has a great deal of chess knowledge in its evaluation. Training an NNUE network based on this evaluation was both an advantage and a challenge, requiring experimentation with architectures and data generation of billions of positions. The reinforcement learning phase for Dragon is in its infancy, but is already showing great promise. Dragon is a huge strength improvement over 14.1, the last release, about 197 elo in MCTS mode and 189 elo in standard mode, at CCRL blitz time control on one thread based on direct matches. With four threads, the gains were 156 elo in MCTS and 170 in standard mode. The improvement is due to the embedded neural network providing much more accurate evaluation and also in some sense gaining an extra ply or so of search by “seeing” some tactics that a normal ev
Read more