llm3br256
This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the ftni-oneshot-train dataset. It achieves the following results on the evaluation set:
- Loss: 0.0169
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 0.1
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0269 | 1.5385 | 5 | 0.0240 |
| 0.0264 | 3.0769 | 10 | 0.0179 |
| 0.0123 | 4.6154 | 15 | 0.0169 |
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for sizhkhy/ftni-sample
Base model
meta-llama/Llama-3.2-3B-Instruct Finetuned
unsloth/Llama-3.2-3B-Instruct