Instructions to use diegoakel/llama3.2-1B-PythonInstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diegoakel/llama3.2-1B-PythonInstruct with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("diegoakel/llama3.2-1B-PythonInstruct", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use diegoakel/llama3.2-1B-PythonInstruct with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for diegoakel/llama3.2-1B-PythonInstruct to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for diegoakel/llama3.2-1B-PythonInstruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for diegoakel/llama3.2-1B-PythonInstruct to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="diegoakel/llama3.2-1B-PythonInstruct", max_seq_length=2048, )
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit
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The notebook to train the model is available [here](https://colab.research.google.com/drive/1kSalFdHaZTcvsnUGNoQcsGVORBVhCmdx#scrollTo=EfYtGx97f0ar). it is the Llama 3.2 1B base model (the unsloth Version) finetuned to write Python code with the
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I wrote about the process on my blog, [here](https://diegoakel.me/finetuning-llama-32-1b-base-to-an-instruct-model-with-unsloth).
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit
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The notebook to train the model is available [here](https://colab.research.google.com/drive/1kSalFdHaZTcvsnUGNoQcsGVORBVhCmdx#scrollTo=EfYtGx97f0ar). it is the Llama 3.2 1B base model (the unsloth Version) finetuned to write Python code with the [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca/viewer/default/train?row=0) dataset.
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I wrote about the process on my blog, [here](https://diegoakel.me/finetuning-llama-32-1b-base-to-an-instruct-model-with-unsloth).
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