llama-3.2-3b-sft-2
This model is a fine-tuned version of tanliboy/llama-3.2-3b on the tanliboy/OpenHermes-2.5-reformat dataset. It achieves the following results on the evaluation set:
- Loss: 0.6744
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.7792 | 0.0673 | 500 | 0.7726 |
0.7496 | 0.1345 | 1000 | 0.7444 |
0.7243 | 0.2018 | 1500 | 0.7296 |
0.7178 | 0.2691 | 2000 | 0.7197 |
0.7077 | 0.3363 | 2500 | 0.7127 |
0.6992 | 0.4036 | 3000 | 0.7066 |
0.6992 | 0.4708 | 3500 | 0.7012 |
0.6945 | 0.5381 | 4000 | 0.6965 |
0.6879 | 0.6054 | 4500 | 0.6920 |
0.6901 | 0.6726 | 5000 | 0.6879 |
0.6759 | 0.7399 | 5500 | 0.6844 |
0.6752 | 0.8072 | 6000 | 0.6812 |
0.6826 | 0.8744 | 6500 | 0.6783 |
0.6804 | 0.9417 | 7000 | 0.6758 |
0.6131 | 1.0089 | 7500 | 0.6764 |
0.6012 | 1.0762 | 8000 | 0.6758 |
0.6136 | 1.1435 | 8500 | 0.6751 |
0.6127 | 1.2107 | 9000 | 0.6747 |
0.6076 | 1.2780 | 9500 | 0.6745 |
0.6033 | 1.3453 | 10000 | 0.6744 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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