Instructions to use SinghShweta/llama2-qlora-finetunined-codegeneration_py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SinghShweta/llama2-qlora-finetunined-codegeneration_py with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "SinghShweta/llama2-qlora-finetunined-codegeneration_py") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from SinghShweta/llama2-qlora-finetunined-codegeneration_py: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/SinghShweta/llama2-qlora-finetunined-codegeneration_py/resolve/main/adapter_model.bin
- Command line
-
hf download hf://SinghShweta/llama2-qlora-finetunined-codegeneration_py/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/SinghShweta/llama2-qlora-finetunined-codegeneration_py/resolve/main/adapter_model.bin
134 MB
- Xet hash:
- 72f451cd1def9154be5cf0829f1a7aa4ab29b553e5d1e0291e4856fc0f953522
- Size of remote file:
- 134 MB
- SHA256:
- 29be9f3fd87b11e5ca4a044c71ace0731acf7b05df2f82818ac2d7c3613afaea
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