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BL-pythia-31m-simpleRW-lite-2048-scratch
pszemraj/BL-pythia-31m-simpleRW-lite-2048-scratch
BL-pythia-31m-simpleRW-lite-2048-scratch is a text generation model from pszemraj. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a fine-tuned version of EleutherAI/pythia-31m on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7136 - Accuracy: 0.2662
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- gpt_neox
- Access
- Public
- Created
- Sep 15, 2023
- Updated
- Dec 29, 2025
- SHA
- 9cfc3d6b
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Datasets
Model card
From the Hugging Face model README
BL-pythia-31m-simpleRW-lite-2048-scratch
This model is a fine-tuned version of EleutherAI/pythia-31m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.7136
- Accuracy: 0.2662
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
2040 ***** eval metrics *****
2041 epoch = 3.0
2042 eval_accuracy = 0.2668
2043 eval_loss = 4.7076
2044 eval_runtime = 0:00:21.04
2045 eval_samples = 500
2046 eval_samples_per_second = 23.759
2047 eval_steps_per_second = 11.88
2048 perplexity = 110.7897
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 80085
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 7.0159 | 0.13 | 100 | 7.1022 | 0.1180 |
| 6.2257 | 0.27 | 200 | 6.3526 | 0.1508 |
| 5.8611 | 0.4 | 300 | 5.9888 | 0.1735 |
| 5.5514 | 0.54 | 400 | 5.7552 | 0.1855 |
| 5.3824 | 0.67 | 500 | 5.5883 | 0.1948 |
| 5.344 | 0.81 | 600 | 5.4697 | 0.2017 |
| 5.1925 | 0.94 | 700 | 5.3717 | 0.2073 |
| 5.0814 | 1.08 | 800 | 5.2932 | 0.2121 |
| 5.0865 | 1.21 | 900 | 5.2280 | 0.2162 |
| 4.9602 | 1.35 | 1000 | 5.1672 | 0.2207 |
| 4.957 | 1.48 | 1100 | 5.1144 | 0.2247 |
| 4.8489 | 1.62 | 1200 | 5.0617 | 0.2299 |
| 4.79 | 1.75 | 1300 | 5.0122 | 0.2349 |
| 4.8005 | 1.89 | 1400 | 4.9637 | 0.2400 |
| 4.7409 | 2.02 | 1500 | 4.9216 | 0.2448 |
| 4.6674 | 2.16 | 1600 | 4.8815 | 0.2488 |
| 4.6729 | 2.29 | 1700 | 4.8475 | 0.2526 |
| 4.7071 | 2.43 | 1800 | 4.8156 | 0.2555 |
| 4.4937 | 2.56 | 1900 | 4.7841 | 0.2588 |
| 4.5153 | 2.7 | 2000 | 4.7573 | 0.2615 |
| 4.5512 | 2.83 | 2100 | 4.7345 | 0.2637 |
| 4.5153 | 2.96 | 2200 | 4.7136 | 0.2662 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.2.0.dev20230915+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Generation
- Library
- transformers
- Type
- gpt_neox
- License
- apache-2.0
- Languages
- en
- Created
- Sep 15, 2023
- Updated
- Dec 29, 2025