llama3_8b_instruct BERT baseline
Collection
Collection: llama3_8b_instruct BERT baseline • 5 items • Updated • 1
Meta-Llama-3-8B-Instruct_e1_bert_cluster_4
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the bert_cluster_4 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.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: 1.0
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Adanato/llama3_8b_instruct_bert_baseline-bert_cluster_4
Base model
meta-llama/Meta-Llama-3-8B-Instruct