Instructions to use amztheory/code-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use amztheory/code-python with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b") model = PeftModel.from_pretrained(base_model, "amztheory/code-python") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: tiiuae/falcon-7b | |
| model-index: | |
| - name: code-python | |
| 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. --> | |
| # code-python | |
| This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1593 | |
| ## 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: 6 | |
| - eval_batch_size: 6 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 24 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 1.4825 | 0.3183 | 30 | 1.5136 | | |
| | 1.4361 | 0.6366 | 60 | 1.4485 | | |
| | 1.3649 | 0.9549 | 90 | 1.3628 | | |
| | 1.3254 | 1.2732 | 120 | 1.2862 | | |
| | 1.1969 | 1.5915 | 150 | 1.2227 | | |
| | 1.2245 | 1.9098 | 180 | 1.1776 | | |
| | 1.1604 | 2.2281 | 210 | 1.1626 | | |
| | 1.1737 | 2.5464 | 240 | 1.1594 | | |
| | 1.1012 | 2.8647 | 270 | 1.1593 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.41.0 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 |