judicial-summarization-Mistral-finetuned_mildsum_TR
This model is a fine-tuned version of unsloth/mistral-7b-v0.3-bnb-4bit on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1717
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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3063 | 1.0 | 273 | 1.4469 |
| 1.1546 | 2.0 | 546 | 1.4614 |
| 1.0607 | 3.0 | 819 | 1.5200 |
| 0.7531 | 4.0 | 1092 | 1.6634 |
| 0.5051 | 5.0 | 1365 | 1.8932 |
| 0.2262 | 6.0 | 1638 | 2.1717 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for Hiranmai49/judicial-summarization-Mistral-finetuned_mildsum_TR
Base model
mistralai/Mistral-7B-v0.3
Quantized
unsloth/mistral-7b-v0.3-bnb-4bit