HuggingFaceH4/starchat2-15b-sft-v0.1

Model

5

stars

3

commits

Mar 12, 2024

updated

alignment-handbook
conversational
endpoints_compatible
generated_from_trainer
safetensors
starcoder2
tensorboard
text-generation
text-generation-inference
transformers

README

Model Card for starchat2-15b-sft-v0.1

This model is a fine-tuned version of bigcode/starcoder2-15b on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets. It achieves the following results on the evaluation set:

  • Loss: 0.6614

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • 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 LossEpochStepValidation Loss
0.64221.09100.6910
0.57012.018200.6639
0.52273.027300.6614

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1

Contributors

lewtun

3 commits

HuggingFaceH4/starchat2-15b-sft-v0.1

Model

5

stars

3

commits

Mar 12, 2024

updated

alignment-handbook
conversational
endpoints_compatible
generated_from_trainer
safetensors
starcoder2
tensorboard
text-generation
text-generation-inference
transformers

README

Model Card for starchat2-15b-sft-v0.1

This model is a fine-tuned version of bigcode/starcoder2-15b on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets. It achieves the following results on the evaluation set:

  • Loss: 0.6614

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • 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 LossEpochStepValidation Loss
0.64221.09100.6910
0.57012.018200.6639
0.52273.027300.6614

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1

Contributors

lewtun

3 commits