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, )
metadata
base_model: unsloth/Llama-3.2-1B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
Uploaded model
- Developed by: diegoakel
- License: apache-2.0
- Finetuned from model : unsloth/Llama-3.2-1B-bnb-4bit
The notebook to train the model is available here. 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 dataset.
I wrote about the process on my blog, here.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
