Instructions to use ysr/hyperparam-rust-lora-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ysr/hyperparam-rust-lora-32 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") model = PeftModel.from_pretrained(base_model, "ysr/hyperparam-rust-lora-32") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: deepseek-ai/deepseek-coder-1.3b-base | |
| datasets: | |
| - generator | |
| model-index: | |
| - name: hyperparam-rust-lora-32 | |
| 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. --> | |
| # hyperparam-rust-lora-32 | |
| This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4306 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 32 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.05 | |
| - lr_scheduler_warmup_steps: 3 | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.7347 | 0.3 | 25 | 0.5297 | | |
| | 0.4878 | 0.59 | 50 | 0.4810 | | |
| | 0.4589 | 0.89 | 75 | 0.4646 | | |
| | 0.4459 | 1.18 | 100 | 0.4549 | | |
| | 0.4273 | 1.48 | 125 | 0.4481 | | |
| | 0.429 | 1.77 | 150 | 0.4428 | | |
| | 0.423 | 2.07 | 175 | 0.4387 | | |
| | 0.4131 | 2.37 | 200 | 0.4363 | | |
| | 0.4027 | 2.66 | 225 | 0.4345 | | |
| | 0.4085 | 2.96 | 250 | 0.4326 | | |
| | 0.3993 | 3.25 | 275 | 0.4322 | | |
| | 0.4024 | 3.55 | 300 | 0.4313 | | |
| | 0.4011 | 3.84 | 325 | 0.4308 | | |
| | 0.3982 | 4.14 | 350 | 0.4307 | | |
| | 0.3982 | 4.43 | 375 | 0.4307 | | |
| | 0.3934 | 4.73 | 400 | 0.4306 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.39.3 | |
| - Pytorch 2.2.1 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 |