Instructions to use Sakil/llama2-fine-tuned-summarization-testmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sakil/llama2-fine-tuned-summarization-testmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "Sakil/llama2-fine-tuned-summarization-testmodel") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from Sakil/llama2-fine-tuned-summarization-testmodel: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/Sakil/llama2-fine-tuned-summarization-testmodel/resolve/main/adapter_model.bin
- Command line
-
hf download hf://Sakil/llama2-fine-tuned-summarization-testmodel/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/Sakil/llama2-fine-tuned-summarization-testmodel/resolve/main/adapter_model.bin
134 MB
- Xet hash:
- ebadd328407cdf924e3b512eaf37b09e3df1a562d5cce1eb43f96d4864523bb7
- Size of remote file:
- 134 MB
- SHA256:
- ad5b702201124453cf3e04c2f9276b8fe00aaec1a6a5631148b1ccf47f945cd5
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