Instructions to use Shibyan/llama3.1_Odata2SQL-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shibyan/llama3.1_Odata2SQL-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Shibyan/llama3.1_Odata2SQL-lora") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-30/scheduler.pt from Shibyan/llama3.1_Odata2SQL-lora: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/checkpoint-30/scheduler.pt
- Command line
-
hf download hf://Shibyan/llama3.1_Odata2SQL-lora/checkpoint-30/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/checkpoint-30/scheduler.pt
1.06 kB
- Xet hash:
- 2b3d984351c4485812a36f42f32d2c9ba29e6245faf190a0e93dc829be1c130b
- Size of remote file:
- 1.06 kB
- SHA256:
- 7323ccbdc545d145f622ade12862f19673eaed00b52a28fcda0899f377736539
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