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URL: https://huggingface.co/trollek/Llama-3.1-8B-Danoia

⇱ trollek/Llama-3.1-8B-Danoia · Hugging Face

Llama 3.1 8B Danoia

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B-Instruct on the danoia_v03, the opus_writing_instruct, the creativegpt and the danoia_v02_no_system datasets + some private datasets related to evaluation.

It achieves the following results on the evaluation set:

  • Loss: 0.7108

Model description

This model can write stories in danish and english. It can do much more, I am sure of it, but not more than the vanilla model it is based on.

Intended uses & limitations

Danoia is intended to be private assistant able to write essays, summarise articles, and be a helpful assistant in general. It misspells danish words at times but it is rare though.

Training and evaluation data

I trained this using LLama-Factory with unsloth enabled on a 16GB 4060 Ti. It took 30 hours and peaked at 13GB VRAM usage.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1.5e-05
  • train_batch_size: 2
  • eval_batch_size: 1
  • seed: 192
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 1.5

Training results

Training Loss Epoch Step Validation Loss
0.2352 0.0719 500 0.8450
0.1742 0.1438 1000 0.8090
0.1667 0.2156 1500 0.7889
0.3791 0.2875 2000 0.7750
0.1989 0.3594 2500 0.7665
0.2347 0.4313 3000 0.7563
0.1694 0.5032 3500 0.7498
0.2351 0.5750 4000 0.7412
0.2322 0.6469 4500 0.7363
0.1689 0.7188 5000 0.7298
0.1953 0.7907 5500 0.7250
0.2099 0.8626 6000 0.7214
0.2368 0.9344 6500 0.7166
0.1632 1.0063 7000 0.7151
0.1558 1.0782 7500 0.7157
0.2854 1.1501 8000 0.7139
0.199 1.2220 8500 0.7127
0.1606 1.2938 9000 0.7117
0.1788 1.3657 9500 0.7112
0.2618 1.4376 10000 0.7109

Framework versions

  • PEFT 0.12.0
  • Transformers 4.46.1
  • Pytorch 2.5.1
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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