Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ninagroot/GPT2-705M-RUN2")
model = AutoModelForCausalLM.from_pretrained("ninagroot/GPT2-705M-RUN2")Quick Links
GPT2-705M
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.5041
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.6866 | 0.98 | 7 | 8.0542 |
| 6.8762 | 1.96 | 14 | 7.3416 |
| 6.1904 | 2.95 | 21 | 6.8861 |
| 5.7933 | 3.93 | 28 | 6.6310 |
| 5.3583 | 4.91 | 35 | 6.3709 |
| 4.8424 | 5.89 | 42 | 6.1468 |
| 4.4728 | 6.88 | 49 | 5.8255 |
| 3.8852 | 8.0 | 57 | 5.6175 |
| 3.4495 | 8.98 | 64 | 5.5436 |
| 3.2287 | 9.82 | 70 | 5.5041 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ninagroot/GPT2-705M-RUN2")