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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: EleutherAI/pythia-160m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: pythia_160m_sft |
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results: [] |
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datasets: |
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- tatsu-lab/alpaca_farm |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pythia_160m_sft |
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This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9831 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.2935 | 0.0889 | 100 | 2.1426 | |
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| 2.153 | 0.1778 | 200 | 2.0977 | |
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| 2.1432 | 0.2667 | 300 | 2.0771 | |
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| 2.1131 | 0.3556 | 400 | 2.0633 | |
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| 2.0885 | 0.4444 | 500 | 2.0510 | |
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| 2.0956 | 0.5333 | 600 | 2.0403 | |
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| 2.0647 | 0.6222 | 700 | 2.0354 | |
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| 2.0498 | 0.7111 | 800 | 2.0273 | |
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| 2.0317 | 0.8 | 900 | 2.0202 | |
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| 2.0226 | 0.8889 | 1000 | 2.0150 | |
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| 1.992 | 0.9778 | 1100 | 2.0114 | |
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| 1.9639 | 1.0667 | 1200 | 2.0088 | |
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| 1.9302 | 1.1556 | 1300 | 2.0051 | |
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| 1.9381 | 1.2444 | 1400 | 2.0028 | |
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| 1.9595 | 1.3333 | 1500 | 2.0009 | |
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| 1.9325 | 1.4222 | 1600 | 1.9998 | |
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| 1.9481 | 1.5111 | 1700 | 1.9981 | |
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| 1.9572 | 1.6 | 1800 | 1.9956 | |
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| 1.9456 | 1.6889 | 1900 | 1.9944 | |
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| 1.9565 | 1.7778 | 2000 | 1.9922 | |
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| 1.9507 | 1.8667 | 2100 | 1.9905 | |
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| 1.9247 | 1.9556 | 2200 | 1.9881 | |
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| 1.8998 | 2.0444 | 2300 | 1.9874 | |
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| 1.9102 | 2.1333 | 2400 | 1.9873 | |
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| 1.8842 | 2.2222 | 2500 | 1.9876 | |
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| 1.876 | 2.3111 | 2600 | 1.9863 | |
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| 1.9001 | 2.4 | 2700 | 1.9856 | |
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| 1.8725 | 2.4889 | 2800 | 1.9859 | |
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| 1.868 | 2.5778 | 2900 | 1.9845 | |
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| 1.8803 | 2.6667 | 3000 | 1.9844 | |
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| 1.9002 | 2.7556 | 3100 | 1.9838 | |
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| 1.8941 | 2.8444 | 3200 | 1.9839 | |
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| 1.8548 | 2.9333 | 3300 | 1.9831 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
