Instructions to use ananyarn/get_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ananyarn/get_python with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ") model = PeftModel.from_pretrained(base_model, "ananyarn/get_python") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ | |
| model-index: | |
| - name: get_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. --> | |
| # get_python | |
| This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5718 | |
| ## Model description | |
| This model can convert a given pseudo-code or algorithm to Python source code. | |
| ## Intended uses & limitations | |
| This model can be used by reasearchers, students and developers who are struggling to convert algorithms to code. | |
| ## Training and evaluation data | |
| The model was trained using ananyarn/Algorithm_and_Python_Source_Code. | |
| <!--## Training procedure--> | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - training_steps: 250 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.8326 | 0.09 | 50 | 0.7046 | | |
| | 0.6404 | 0.18 | 100 | 0.6080 | | |
| | 0.5771 | 0.27 | 150 | 0.5701 | | |
| | 0.5637 | 0.36 | 200 | 0.5662 | | |
| | 0.552 | 0.44 | 250 | 0.5718 | | |
| ### Framework versions | |
| - PEFT 0.8.2 | |
| - Transformers 4.37.2 | |
| - Pytorch 2.2.0 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 |