shawgpt-ft-optuna-best
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3009
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: 0.00038969284138174674
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
4.5936 | 0.9231 | 3 | 3.9712 |
3.8763 | 1.8462 | 6 | 3.0448 |
2.9241 | 2.7692 | 9 | 2.4129 |
1.6745 | 4.0 | 13 | 1.7787 |
1.6857 | 4.9231 | 16 | 1.4898 |
1.3933 | 5.8462 | 19 | 1.3887 |
1.2851 | 6.7692 | 22 | 1.3504 |
0.9501 | 8.0 | 26 | 1.3214 |
1.221 | 8.9231 | 29 | 1.3124 |
1.167 | 9.8462 | 32 | 1.3072 |
1.1637 | 10.7692 | 35 | 1.3048 |
0.8262 | 12.0 | 39 | 1.3023 |
1.1126 | 12.9231 | 42 | 1.3012 |
0.9195 | 13.8462 | 45 | 1.3009 |
Framework versions
- PEFT 0.13.2
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
- Downloads last month
- 2
Model tree for Shaurya-Shsin/shawgpt-ft-optuna-best
Base model
mistralai/Mistral-7B-Instruct-v0.2
Quantized
TheBloke/Mistral-7B-Instruct-v0.2-GPTQ