Instructions to use Tanmay09516/llama-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tanmay09516/llama-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Trelis/Llama-2-7b-chat-hf-sharded-bf16") model = PeftModel.from_pretrained(base_model, "Tanmay09516/llama-sql") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from Tanmay09516/llama-sql: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/Tanmay09516/llama-sql/resolve/refs%2Fpr%2F1/adapter_model.bin
- Command line
-
hf download hf://Tanmay09516/llama-sql@refs/pr/1/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/Tanmay09516/llama-sql/resolve/refs%2Fpr%2F1/adapter_model.bin
134 MB
- Xet hash:
- d0ae5ab0ab180b0599215646eeeef9a470d6bef2ef1f24e127552dfb6009abed
- Size of remote file:
- 134 MB
- SHA256:
- 3b5f1c3e28d1040bdf52b499248c7deaa0b63aa8e7fd7ebcd93597b990da76c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.