# Run 2024-12-25

You could get the weights from [here](https://drive.google.com/drive/folders/1l2GI5lYHinpfp7oxK5r36PwSyr9RBHX2?usp=drive_link).

* 2024-12-25-16:46
* Start the training.
* The version is v0.8.1.
* learning rate = 5e-3
* batch size = 256
* The network size is 6bx96c.
* Set the visits as 16.

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* 2024-12-26-03:58
* Played 155k games.
* Accumulate around $0.0808\times 10^{8}$ 20bx256c eval queries.
* Set the visits as 50 (from 16).

</br>

* 2024-12-27-14:57
* Played 365k games.
* Accumulate around $0.4184\times 10^{8}$ 20bx256c eval queries.
* The strengh is better than Leela Zero with [91](https://zero.sjeng.org/networks/b3b00c6d75b4e74946a97b88949307c9eae2355a88f518ebf770c7758f90e357.gz) weights. Elo different is +62.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 091.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 635 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          203             171          374  (58.90%)
        Leela Zero 0.17        146             115          261  (41.10%)

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* 2024-12-28-14:48
* Played 500k games.
* Accumulate around $0.6396\times 10^{8}$ 20bx256c eval queries.
* The strengh is better than Leela Zero with [92]() weights. Elo different is +89.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 092.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 729 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          213             243          456  (62.55%)
        Leela Zero 0.17        122             151          273  (37.45%)

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* 2024-12-29-19:46
* Played 665k games.
* Accumulate around $0.9077\times 10^{8}$ 20bx256c eval queries.
* The strengh is better than Leela Zero with [105]() weights. Elo different is +19.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 105.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 535 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          131             151          282  (52.71%)
        Leela Zero 0.17        116             137          253  (47.29%)

</br>

* 2024-12-30-16:04
* Played 780k games.
* Accumulate around $1.0931x10\times 10^{8}$ 20bx256c eval queries.
* Halt 6bx96c training.
* The strengh is better than Leela Zero with [110]() weights. Elo different is +14.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 110.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 590 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          161             146          307  (52.03%)
        Leela Zero 0.17        149             134          283  (47.97%)

* The strengh is as same as Leela Zero with [116]() weights. Elo different is +2.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 110.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 590 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          202             241          443  (50.23%)
        Leela Zero 0.17        201             238          439  (49.77%)

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* 2025-1-2-03:46
* Start 10bx128c training
* Soft weights = 1.0

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* 2025-1-3-00:01
* Played 865k games.
* Accumulate around $1.4977\times 10^{8}$ 20bx256c eval queries.
* The strengh is as same as Leela Zero with [117]() weights. Elo different is +13.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 117.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 632 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          160             168          328  (51.90%)
        Leela Zero 0.17        148             156          304  (48.10%)

</br>

* 2025-1-4-20:42
* Played 1055k games.
* learning rate = 2.5e-3
* Set the visits as 150 (from 50).

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* 2025-1-5-14:49
* Played 1095k games.
* Accumulate around $2.8773\times 10^{8}$ 20bx256c eval queries.
* The strengh is as same as Leela Zero with [130]() weights. Elo different is +14.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 130.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 600 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          136             176          312  (52.00%)
        Leela Zero 0.17        124             164          288  (48.00%)

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* 2025-1-8-03:02
* Played 1230k games.
* Accumulate around $5.0774\times 10^{8}$ 20bx256c eval queries.
* The strengh is as same as Leela Zero with [140]() weights. Elo different is +5.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 140.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 527 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          125             142          267  (50.66%)
        Leela Zero 0.17        120             140          260  (49.34%)

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* 2025-1-13-16:04
* Played 1530k games.
* Accumulate around $7.9209x10\times 10^{8}$ 20bx256c eval queries.
* Halt 10bx128c training.
* The strengh is better than Leela Zero with [145]() weights. Elo different is +77.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 145.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 681 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          192             223          415  (60.94%)
        Leela Zero 0.17        117             149          266  (39.06%)

* The strengh is worse than Leela Zero with [150]() weights. Elo different is -25.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w 150.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 656 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          147             157          304  (60.94%)
        Leela Zero 0.17        171             181          352  (39.06%)

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* 2025-1-18-14:00
* Start 15bx192c training
* Soft weights = 4.0
* learning rate = 2.5e-3

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* 2025-2-1-7:00
* Played 2100k games.
* learning rate = 1.25e-3 (from 2.5e-3)

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* 2025-2-1-21:42
* Played 2030k games.
* Accumulate around $2.70146\times 10^{9}$ 20bx256c eval queries.
* The strengh is as same as Leela Zero with [ELFv0]() weights. Elo different is +9.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w elfv0.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 814 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          182             236          267  (51.35%)
        Leela Zero 0.17        171             225          260  (48.65%)

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* 2024-2-10-14:15
* Played 2330k games
* Full Visits = 400 (from 150)
* Fast Visits = 125 (from 50)
* Gumbel Visits = 55 (from 32)
* Resign Visits = 75 (from 55)

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* 2024-2-17-11:20
* Played 2480k games.
* Accumulate around $4.9194\times 10^{9}$ 20bx256c eval queries.
* Strength should be between ELFv0 and ELFv1.
* Halt 15bx192c training.

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* 2024-2-25-1:36
* Start 20bx256c training.
* learning rate = 6.25e-4
* The 20bx256c pretrain model is slightly stronger than ELFv2. Win-rate is 53.85% (played 882 games).

</br>

* 2024-3-2 2 15:45
* Played 2570k games.
* Accumulate around $6.4240\times 10^{9}$ 20bx256c eval queries.
* The strengh is better than Leela Zero with [ELFv2]() weights. Elo different is +48.
* Sayuri: ```-w current_weights -t 1 -b 1 -p 800 --cpuct-init 0.5 --use-optimistic-policy --random-moves-factor 0.1 --random-moves-temp 0.8```
* Leela Zero: ```-w elfv2.gz --noponder -v 800 -g -t 1 -b 1 --timemanage off --randomcnt 30 --randomtemp 0.8```
* Game result (played 709 games with Leela Zero):

        Name             black won       white won       total (win-rate)
        
        Sayrui v0.8.1          173             230          403  (56.84%)
        Leela Zero 0.17        124             182          306  (43.16%)