QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

This is quantized version of Vishaltiwari2019/distilgpt2-finetuned-python_code_instructions_18k_alpaca created using llama.cpp

Original Model Card

distilgpt2-finetuned-python_code_instructions_18k_alpaca

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.5063

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.7264	1.0	3861	1.5890
1.6046	2.0	7722	1.5214
1.5359	3.0	11583	1.5063

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2