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tinyllama_mole_sft_router05_lr1e-4_ep3

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metadata
base_model: ondevicellm/tinyllama_mole_v1
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrachat_200k
model-index:
  - name: tinyllama_mole_sft_router05_lr1e-4_ep3
    results: []

tinyllama_mole_sft_router05_lr1e-4_ep3

This model is a fine-tuned version of ondevicellm/tinyllama_mole_v1 on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1035

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: 0.0001
  • 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: 120
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
2.2617 0.09 100 2.2410
2.2246 0.18 200 2.2165
2.1994 0.26 300 2.1994
2.1767 0.35 400 2.1869
2.1532 0.44 500 2.1792
2.171 0.53 600 2.1717
2.1588 0.61 700 2.1645
2.145 0.7 800 2.1567
2.1366 0.79 900 2.1507
2.1219 0.88 1000 2.1450
2.1415 0.96 1100 2.1387
1.9765 1.05 1200 2.1446
1.9837 1.14 1300 2.1430
1.9952 1.23 1400 2.1388
1.9868 1.31 1500 2.1351
1.9864 1.4 1600 2.1316
1.987 1.49 1700 2.1263
1.9678 1.58 1800 2.1230
1.9827 1.66 1900 2.1164
1.9846 1.75 2000 2.1134
1.9694 1.84 2100 2.1068
1.9429 1.93 2200 2.1035
1.8079 2.01 2300 2.1369
1.8132 2.1 2400 2.1375
1.8043 2.19 2500 2.1360
1.7927 2.28 2600 2.1334
1.7935 2.37 2700 2.1335
1.7982 2.45 2800 2.1321
1.8029 2.54 2900 2.1311
1.7919 2.63 3000 2.1298
1.7953 2.72 3100 2.1287
1.798 2.8 3200 2.1280
1.7947 2.89 3300 2.1282
1.8015 2.98 3400 2.1283

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0