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ondevicellm/tinyllama_moe_sft_ultrachat-slimorca

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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tinyllamamoesft_ultrachat-slimorca

This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k and the ondevicellm/SlimOrca datasets. It achieves the following results on the evaluation set:

  • —Loss: 1.1526

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: 2e-05
  • —trainbatchsize: 16
  • —evalbatchsize: 8
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 4
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 128
  • —totalevalbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 120
  • —num_epochs: 1

Training results

Training LossEpochStepValidation Loss
1.46010.051001.3361
1.33240.12001.2566
1.29460.143001.2279
1.27670.194001.2111
1.22980.245001.1995
1.22470.296001.1902
1.22080.347001.1833
1.23750.398001.1775
1.20380.439001.1726
1.19260.4810001.1683
1.19330.5311001.1649
1.18930.5812001.1618
1.20290.6313001.1593
1.22010.6814001.1572
1.17410.7215001.1557
1.18130.7716001.1545
1.16680.8217001.1536
1.14950.8718001.1530
1.15950.9219001.1527
1.16070.9720001.1526

Framework versions

  • —Transformers 4.36.2
  • —Pytorch 2.0.1+cu117
  • —Datasets 2.16.1
  • —Tokenizers 0.15.0