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tinyllama_moe_sft_ultrachat200k_v2_epochs3

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metadata
license: apache-2.0
base_model: ondevicellm/tinyllama_moe
tags:
  - alignment-handbook
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrachat_200k
model-index:
  - name: tinyllama_moe_sft_ultrachat200k_v2_epochs3
    results: []

tinyllama_moe_sft_ultrachat200k_v2_epochs3

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

  • Loss: 1.1178

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
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 115
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.336 0.09 100 1.3129
1.2424 0.18 200 1.2363
1.2079 0.26 300 1.2084
1.185 0.35 400 1.1911
1.1546 0.44 500 1.1787
1.1741 0.53 600 1.1692
1.1612 0.61 700 1.1613
1.1453 0.7 800 1.1547
1.141 0.79 900 1.1489
1.1247 0.88 1000 1.1438
1.1485 0.96 1100 1.1392
1.067 1.05 1200 1.1387
1.0694 1.14 1300 1.1368
1.0814 1.23 1400 1.1341
1.0727 1.31 1500 1.1316
1.0769 1.4 1600 1.1292
1.0728 1.49 1700 1.1270
1.0558 1.58 1800 1.1247
1.0753 1.66 1900 1.1229
1.0799 1.75 2000 1.1209
1.066 1.84 2100 1.1192
1.0406 1.93 2200 1.1178
1.0193 2.01 2300 1.1222
1.0276 2.1 2400 1.1220
1.0171 2.19 2500 1.1215
1.0112 2.28 2600 1.1211
1.0087 2.37 2700 1.1207
1.0158 2.45 2800 1.1204
1.0219 2.54 2900 1.1199
1.0024 2.63 3000 1.1197
1.019 2.72 3100 1.1197
1.0135 2.8 3200 1.1194
1.0094 2.89 3300 1.1194
1.0284 2.98 3400 1.1194

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.14.6
  • Tokenizers 0.15.0