Instructions to use twanghcmut/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twanghcmut/results with Transformers:
# Load model directly from transformers import AutoTokenizer, EnergyLLM tokenizer = AutoTokenizer.from_pretrained("twanghcmut/results") model = EnergyLLM.from_pretrained("twanghcmut/results") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1679
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1909 | 1.0 | 1000 | 0.2057 |
| 0.1533 | 2.0 | 2000 | 0.1679 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.2.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
meta-llama/Llama-3.2-1B-Instruct