Instructions to use luna-code/llamaindex-codegen-350M-mono-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luna-code/llamaindex-codegen-350M-mono-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("luna-code/llamaindex-codegen-350M-mono-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from luna-code/llamaindex-codegen-350M-mono-lora: direct link, hf CLI and curl.
- Browser
- Download file 2.63 MB
-
https://huggingface.co/luna-code/llamaindex-codegen-350M-mono-lora/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://luna-code/llamaindex-codegen-350M-mono-lora/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/luna-code/llamaindex-codegen-350M-mono-lora/resolve/main/adapter_model.safetensors
2.63 MB
- Xet hash:
- 17ddeac814555121a2554ca8e9e524a3d7476dcac4aebc33592a0c6cb48ceab2
- Size of remote file:
- 2.63 MB
- SHA256:
- 6d300db29e356130530a39825d86343f3ff86a2c25538f5b05173e5c7e0e5da4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.