judicial-summarization-llama-3-finetuned

This model is a fine-tuned version of unsloth/llama-3-8b-bnb-4bit on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0076

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
1.6197	0.9993	696	1.6757
1.5115	2.0	1393	1.6700
1.583	2.9993	2089	1.7025
1.3133	4.0	2786	1.7708
0.9935	4.9993	3482	1.8802
1.0666	5.9957	4176	2.0076

Framework versions

• PEFT 0.12.0
• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1