plaguss/stablelm-2-1_6-sft-disticoder-v01

stablelm-2-1.6-disticoder-v0.1

This model is a fine-tuned version of stabilityai/stablelm-2-1_6b on the argilla/DistiCoder-dpo-binarized dataset. It achieves the following results on the evaluation set:

• Loss: 1.1315

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.7319	0.44	5	1.5441
1.3425	0.89	10	1.2968
1.1709	1.33	15	1.2151
1.0994	1.78	20	1.1605
1.0287	2.22	25	1.1382
1.0303	2.67	30	1.1315

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.1+cu121
• Datasets 2.16.1
• Tokenizers 0.15.2