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BEE-spoke-data
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smol_llama-101M-midjourney-messages

Text Generation
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llama
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smol_llama-101M-midjourney-messages / README.md
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metadata
license: apache-2.0
base_model: BEE-spoke-data/smol_llama-101M-GQA
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: smol_llama-101M-GQA-midjourney-messages-cleaned-1024-vN
    results: []

smol_llama-101M-GQA-midjourney-messages-cleaned-1024-vN

This model is a fine-tuned version of BEE-spoke-data/smol_llama-101M-GQA on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.8431
  • Accuracy: 0.4682

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: 0.00025
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 17056
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
  • lr_scheduler_type: inverse_sqrt
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.3031 0.03 200 3.2643 0.4169
3.1762 0.06 400 3.1674 0.4247
3.0914 0.08 600 3.0850 0.4359
3.0384 0.11 800 3.0371 0.4419
3.0235 0.14 1000 3.0057 0.4467
2.9874 0.17 1200 2.9816 0.4496
2.9708 0.19 1400 2.9650 0.4518
2.9796 0.22 1600 2.9487 0.4541
2.9371 0.25 1800 2.9364 0.4560
2.932 0.28 2000 2.9265 0.4571
2.9272 0.3 2200 2.9175 0.4580
2.935 0.33 2400 2.9115 0.4591
2.9074 0.36 2600 2.9038 0.4600
2.9404 0.39 2800 2.8986 0.4611
2.8896 0.41 3000 2.8938 0.4617
2.8946 0.44 3200 2.8893 0.4624
2.9183 0.47 3400 2.8855 0.4623
2.887 0.5 3600 2.8813 0.4638
2.8823 0.52 3800 2.8780 0.4638
2.9171 0.55 4000 2.8744 0.4642
2.8884 0.58 4200 2.8718 0.4646
2.8875 0.61 4400 2.8700 0.4651
2.9121 0.63 4600 2.8668 0.4653
2.8653 0.66 4800 2.8639 0.4658
2.8603 0.69 5000 2.8625 0.4659
2.8489 0.72 5200 2.8598 0.4661
2.8674 0.74 5400 2.8577 0.4666
2.884 0.77 5600 2.8554 0.4669
2.857 0.8 5800 2.8535 0.4672
2.8747 0.83 6000 2.8516 0.4673
2.8809 0.86 6200 2.8501 0.4672
2.8832 0.88 6400 2.8482 0.4679
2.8817 0.91 6600 2.8472 0.4681
2.8813 0.94 6800 2.8457 0.4684
2.8493 0.97 7000 2.8444 0.4677
2.8455 0.99 7200 2.8431 0.4682

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.15.0
  • Tokenizers 0.15.0