llama-160m-boolq
This model is a fine-tuned version of JackFram/llama-160m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6795
- Accuracy: 0.5957
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: 16
- eval_batch_size: 32
- 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: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.99 | 73 | 0.6870 | 0.5731 |
| No log | 1.99 | 147 | 0.6825 | 0.5957 |
| No log | 3.0 | 221 | 0.6809 | 0.6012 |
| No log | 3.96 | 292 | 0.6795 | 0.5957 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
- Downloads last month
- 104
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the model is not deployed on the HF Inference API.
Model tree for Cheng98/llama-160m-boolq
Base model
JackFram/llama-160m