Instructions to use leap-llm/Meta-Llama-3-8B-Instruct-sft-intercode-python-iter0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leap-llm/Meta-Llama-3-8B-Instruct-sft-intercode-python-iter0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("leap-llm/Meta-Llama-3-8B-Instruct-sft-intercode-python-iter0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d80546470b655466b44836fb79832c92c8b6cbe1db2db6b0a214b9e288a50bc9
- Size of remote file:
- 109 MB
- SHA256:
- 20130fc236577fc74d10b9369c6f7c8dbb475406d2250859f76dcd5cde9ded85
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