Instructions to use jbenbudd/ADPrLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jbenbudd/ADPrLlama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("GreatCaptainNemo/ProLLaMA_Stage_1") model = PeftModel.from_pretrained(base_model, "jbenbudd/ADPrLlama") - Notebooks
- Google Colab
- Kaggle
train_1_epoch_test
This model is a fine-tuned version of GreatCaptainNemo/ProLLaMA_Stage_1 on the adpr_train dataset. It achieves the following results on the evaluation set:
- Loss: 0.3777
- Num Input Tokens Seen: 2691984
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for jbenbudd/ADPrLlama
Base model
GreatCaptainNemo/ProLLaMA_Stage_1