SmolLM-1.7B-Instruct-Summarization-Adapter_r32_alpha64_lr3e-4_rslorafalse
This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7541
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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.7291 | 1.0 | 1266 | 1.7541 |
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for gabrielmbmb/SmolLM-1.7B-Instruct-Summarization-Adapter_r32_alpha64_lr3e-4_rslorafalse
Base model
HuggingFaceTB/SmolLM-1.7B
Quantized
HuggingFaceTB/SmolLM-1.7B-Instruct