Instructions to use mtzig/joint_debug_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mtzig/joint_debug_test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/small-llama2") model = PeftModel.from_pretrained(base_model, "mtzig/joint_debug_test") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: TinyPixel/small-llama2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: joint_debug_test | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # joint_debug_test | |
| This model is a fine-tuned version of [TinyPixel/small-llama2](https://huggingface.co/TinyPixel/small-llama2) on an unknown dataset. | |
| ## 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: 2 | |
| - eval_batch_size: 5 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 4 | |
| - 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_ratio: 0.1 | |
| - num_epochs: 1 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.1 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.1 |