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chess_model – AI Model by 2pp | AlphaNeural AI

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2pp/
chess_model

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text-generation

generated_from_trainer

HuggingFaceTB/SmolLM2-135M

text-generation-inference

endpoints_compatible

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chess_model

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 0.7382
eval_runtime: 133.8351
eval_samples_per_second: 149.438
eval_steps_per_second: 18.68
epoch: 0.0842
step: 2000

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 3

Framework versions

Transformers 5.2.0
Pytorch 2.10.0+cu128
Datasets 4.6.0
Tokenizers 0.22.2