Instructions to use ctemplin/Llama-3.2-1B-PythonProgrammer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ctemplin/Llama-3.2-1B-PythonProgrammer with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "ctemplin/Llama-3.2-1B-PythonProgrammer") - Notebooks
- Google Colab
- Kaggle
Llama-3.2-1B Python Programmer
Llama-3.2-1B-Instruct fine-tuned on a dataset of python code examples using 4-bit quantization and Low-Rank Adaption.
The dataset used can be found here: https://huggingface.co/datasets/Vezora/Tested-143k-Python-Alpaca
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Base model
meta-llama/Llama-3.2-1B-Instruct
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "ctemplin/Llama-3.2-1B-PythonProgrammer")