# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("eliwill/alan-watts-8e")
model = AutoModelForCausalLM.from_pretrained("eliwill/alan-watts-8e")Quick Links
eliwill/alan-watts-8e
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 2.9844
- Validation Loss: 3.3608
- Epoch: 7
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 3.3770 | 3.5178 | 0 |
| 3.2458 | 3.4955 | 1 |
| 3.1798 | 3.4681 | 2 |
| 3.1305 | 3.4412 | 3 |
| 3.0881 | 3.4115 | 4 |
| 3.0500 | 3.3940 | 5 |
| 3.0153 | 3.3692 | 6 |
| 2.9844 | 3.3608 | 7 |
Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="eliwill/alan-watts-8e")