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P4_multi_without_retrieval_2 – AI Model by AliHmlii | AlphaNeural AI
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AliHmlii
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P4_multi_without_retrieval_2
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peft
tensorboard
safetensors
trl
sft
generated_from_trainer
meta-llama/Meta-Llama-3-8B-Instruct
adapter
llama3
us
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P4_multi_without_retrieval_2
This model is a fine-tuned version of
meta-llama/Meta-Llama-3-8B-Instruct
on an unknown dataset.
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: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2
Training results
Framework versions
PEFT 0.13.1
Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 2.19.0
Tokenizers 0.21.0
