Instructions to use ashwincv0112/code-llama-instruction-finetune2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ashwincv0112/code-llama-instruction-finetune2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Instruct-hf") model = PeftModel.from_pretrained(base_model, "ashwincv0112/code-llama-instruction-finetune2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from ashwincv0112/code-llama-instruction-finetune2: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/ashwincv0112/code-llama-instruction-finetune2/resolve/main/adapter_model.bin
- Command line
-
hf download hf://ashwincv0112/code-llama-instruction-finetune2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/ashwincv0112/code-llama-instruction-finetune2/resolve/main/adapter_model.bin
16.8 MB
- Xet hash:
- e70700b28ed20a6f1880a07f1e06bbd0332afcb7e40bc7e1c5e3c2b4abb52073
- Size of remote file:
- 16.8 MB
- SHA256:
- d2a96d3ab01b04349d69b92cc6c4aec140e99a96e3f1bb2cc1be3adeb32fdf7a
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