Instructions to use Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test/resolve/main/tokenizer.model
- Command line
-
hf download hf://Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Nutanix/CodeLlama-7b-Instruct-hf_KTO_lora_cpp_unit_test/resolve/main/tokenizer.model
500 kB
- Xet hash:
- b1153bfcbd4ec2d1636fdb764e096b2cd0db84c109f8e521f2ea20af59ef283a
- Size of remote file:
- 500 kB
- SHA256:
- 45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.