99-v9

This model is a fine-tuned version of Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7495

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: 0.002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.005
• lr_scheduler_warmup_steps: 89
• training_steps: 17894

Training results

Training Loss	Epoch	Step	Validation Loss
0.6331	0.0500	894	0.6004
0.5667	0.0999	1788	0.5463
0.5423	0.1499	2682	0.5138
0.5749	0.1998	3576	0.7377
0.5378	0.2498	4470	0.7542
0.506	0.2998	5364	0.7902
0.5561	0.3497	6258	0.7810
0.5259	0.3997	7152	0.7914
0.5516	0.4496	8046	0.7611
0.5131	0.4996	8940	0.6860
0.5069	0.5496	9834	0.7247
0.4977	0.5995	10728	0.7375
0.4976	0.6495	11622	0.7436
0.5018	0.6995	12516	0.7520
0.537	0.7494	13410	0.7613
0.5018	0.7994	14304	0.6922
0.4891	0.8493	15198	0.7322
0.4808	0.8993	16092	0.7430
0.5231	0.9493	16986	0.7546
0.5103	0.9992	17880	0.7495

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1