Instructions to use Katochh/falcon-code-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Katochh/falcon-code-generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("petals-team/falcon-rw-1b") model = PeftModel.from_pretrained(base_model, "Katochh/falcon-code-generation") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - code_search_net | |
| base_model: petals-team/falcon-rw-1b | |
| model-index: | |
| - name: falcon-code-generation | |
| 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. --> | |
| # falcon-code-generation | |
| This model is a fine-tuned version of [petals-team/falcon-rw-1b](https://huggingface.co/petals-team/falcon-rw-1b) on the code_search_net dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0068 | |
| ## 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: 0.0002 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - training_steps: 320 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.231 | 0.01 | 20 | 1.2339 | | |
| | 1.2932 | 0.02 | 40 | 1.1486 | | |
| | 1.231 | 0.03 | 60 | 1.1240 | | |
| | 1.0344 | 0.04 | 80 | 1.0872 | | |
| | 1.3396 | 0.04 | 100 | 1.0973 | | |
| | 0.9727 | 0.05 | 120 | 1.0608 | | |
| | 1.1138 | 0.06 | 140 | 1.0520 | | |
| | 1.1591 | 0.07 | 160 | 1.0442 | | |
| | 0.9822 | 0.08 | 180 | 1.0286 | | |
| | 1.1891 | 0.09 | 200 | 1.0345 | | |
| | 1.0183 | 0.1 | 220 | 1.0194 | | |
| | 1.0012 | 0.11 | 240 | 1.0142 | | |
| | 1.1396 | 0.12 | 260 | 1.0116 | | |
| | 1.0058 | 0.12 | 280 | 1.0074 | | |
| | 1.1884 | 0.13 | 300 | 1.0072 | | |
| | 0.9587 | 0.14 | 320 | 1.0068 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.38.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 |