chessgpt2-medium-l – AI Model by dakwi | AlphaNeural AI

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dakwi/
chessgpt2-medium-l

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text-generation

generated_from_trainer

openai-community/gpt2-medium

autotrain_compatible

text-generation-inference

endpoints_compatible

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chessgpt2-medium-l

This model is a fine-tuned version of gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8574

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.025
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.1756	0.1280	2000	1.5178
1.4207	0.2560	4000	1.2728
1.265	0.3839	6000	1.1726
1.1852	0.5119	8000	1.1138
1.1321	0.6399	10000	1.0720
1.0931	0.7679	12000	1.0384
1.0621	0.8958	14000	1.0097
1.0319	1.0238	16000	0.9891
0.9981	1.1518	18000	0.9680
0.9816	1.2798	20000	0.9498
0.9655	1.4077	22000	0.9360
0.951	1.5357	24000	0.9219
0.9386	1.6637	26000	0.9094
0.9264	1.7917	28000	0.8974
0.9154	1.9196	30000	0.8876
0.8973	2.0476	32000	0.8804
0.8734	2.1756	34000	0.8743
0.8684	2.3036	36000	0.8683
0.8638	2.4315	38000	0.8647
0.86	2.5595	40000	0.8611
0.8581	2.6875	42000	0.8589
0.8557	2.8155	44000	0.8579
0.8556	2.9434	46000	0.8574

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1