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gsm8k_training_synthetic_positive_vs_multi – Dataset by EleanorZzz | AlphaNeural AI
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gsm8k_training_synthetic_positive_vs_multi
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gsm8k_training_synthetic_positive_vs_multi
This model is a fine-tuned version of
meta-llama/Llama-3.2-1B-Instruct
on the gsm8k_training_synthetic_positive_vs_multi 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: 0.001
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use adamw_torch_fused 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: 4.0
Training results
Framework versions
PEFT 0.15.2
Transformers 4.55.0
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1
