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judicial-summarization-llama-3-finetuned – AI Model by Hiranmai49 | AlphaNeural AI
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judicial-summarization-llama-3-finetuned
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peft
safetensors
trl
sft
unsloth
generated_from_trainer
unsloth/llama-3-8b-bnb-4bit
adapter
llama3
us
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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
