1b_distill_width_prune
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the JunxiongWang/sftdataset dataset. It achieves the following results on the evaluation set:
- Loss: 1.5712
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.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: 0.01
- training_steps: 20000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3657 | 0.0413 | 10000 | 1.7362 |
| 1.2947 | 0.0827 | 20000 | 1.5713 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3
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Base model
meta-llama/Llama-3.2-1B