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lapp0/distily_bench_gpt2_linear_objectives

sourceHugging Facemitupdated 2y agoView on Hugging Face
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distilybenchgpt2_optim

This student model is distilled from the teacher model gpt2 using the dataset (unspecified).

The Distily library was used for this distillation.

It achieves the following results on the evaluation set:

  • eval_enwikippl: 524.7870
  • eval_frwikippl: 3705.5625
  • eval_zhwikippl: 6035.2861
  • eval_loss: 2370.7361
  • eval_runtime: 21.6322
  • evalsamplesper_second: 46.227
  • evalstepsper_second: 11.557

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Model description

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Intended uses & limitations

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Training and evaluation data

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • distillationobjective: LinearObjective(logitsweight=1, logitslossfn=<function kldivergenceloss at 0x7f57c4b07910>, activationsweight=10, activationslossfn=<function kldivergenceloss at 0x7f57c4b07910>, attentionsweight=0, attentionslossfn=<function mse_loss at 0x7f57c4b07880>)
  • train_embeddings: True
  • learning_rate: 4e-05
  • trainbatchsize: 4
  • evalbatchsize: 4
  • seed: 42
  • gradientaccumulationsteps: 4
  • totaltrainbatch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: constant
  • num_epochs: 1.0

Resource Usage

Peak GPU Memory: 4.5067 GB

Eval-Phase Metrics

stepepochenwikipplfrwikippllossruntimesamples_per_secondsteps_per_secondzhwikippl
teacher eval30.238557.272818.1772
0055339.367257682.574231197.183621.439846.64211.66157080.2930
5000.08081545.69347685.42973209.936021.484746.54511.63663830.4023
10000.16161108.68475659.87012933.136021.455946.60711.65231166.1797
15000.2424913.35654893.86232798.016121.595646.30611.57623215.4258
20000.3232813.53104763.64362700.016121.63546.22111.55522568.9238
25000.4040747.36084565.68512631.072021.544246.41611.60418090.1602
30000.4848711.60944255.01272579.263921.711646.05811.51516199.8096
35000.5657666.46654117.33692530.944121.588646.32111.5816435.1426
40000.6465638.01924058.82622500.080121.471246.57411.64316069.4648
45000.7273597.09234013.01252459.424121.709346.06311.51612965.0762
50000.8081567.69123822.99632424.480021.530946.44511.61110275.5850
55000.8889548.51593864.86742399.535921.640846.20911.5528114.6914
60000.9697539.38173793.86062379.360121.563646.37411.5946467.9736
61870.9999524.78703705.56252370.736121.632246.22711.5576035.2861

Framework versions

  • Distily 0.2.0
  • Transformers 4.44.0
  • Pytorch 2.3.0
  • Datasets 2.20.0