SmolLM-1.7B-Instruct-Summarization-Adapter_r32_alpha128_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: 1.7402

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
1.7158	1.0	1266	1.7402

Framework versions

• PEFT 0.13.2
• Transformers 4.45.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1