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chesspythia-70m/README.md
ModelHub XC 145084b7d5 初始化项目,由ModelHub XC社区提供模型
Model: mlabonne/chesspythia-70m
Source: Original Platform
2026-04-17 19:25:02 +08:00

3.7 KiB

license, base_model, tags, model-index
license base_model tags model-index
apache-2.0 EleutherAI/pythia-70m-deduped
generated_from_trainer
name results
results

results

This model is a fine-tuned version of EleutherAI/pythia-70m-deduped on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2691

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: 100
  • eval_batch_size: 100
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss
2.852 0.1 1 3.1074
3.0923 0.2 2 2.3879
2.3371 0.3 3 2.1025
2.1166 0.4 4 1.9761
2.0538 0.5 5 1.8446
1.8972 0.6 6 1.7470
1.8356 0.7 7 1.6615
1.702 0.8 8 1.6187
1.6907 0.9 9 1.6626
1.5877 1.0 10 1.6192
1.6332 1.1 11 1.5464
1.4906 1.2 12 1.5091
1.5267 1.3 13 1.4850
1.4857 1.4 14 1.4572
1.4247 1.5 15 1.4319
1.4815 1.6 16 1.4207
1.3584 1.7 17 1.4092
1.4812 1.8 18 1.4196
1.4381 1.9 19 1.4021
1.453 2.0 20 1.4013
1.3468 2.1 21 1.3781
1.3327 2.2 22 1.3598
1.3623 2.3 23 1.3516
1.2876 2.4 24 1.3384
1.374 2.5 25 1.3366
1.3863 2.6 26 1.3265
1.3327 2.7 27 1.3186
1.2886 2.8 28 1.3130
1.3842 2.9 29 1.3024
1.3105 3.0 30 1.2986
1.2331 3.1 31 1.2966
1.3227 3.2 32 1.2954
1.2923 3.3 33 1.2928
1.2976 3.4 34 1.2901
1.3207 3.5 35 1.2879
1.2455 3.6 36 1.2834
1.2546 3.7 37 1.2779
1.2999 3.8 38 1.2744
1.2484 3.9 39 1.2723
1.281 4.0 40 1.2720
1.2134 4.1 41 1.2722
1.214 4.2 42 1.2721
1.3031 4.3 43 1.2715
1.2174 4.4 44 1.2708
1.2359 4.5 45 1.2703
1.2578 4.6 46 1.2699
1.2815 4.7 47 1.2695
1.2866 4.8 48 1.2693
1.2878 4.9 49 1.2691
1.2214 5.0 50 1.2691

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0