Instructions to use bugdaryan/MistralSQL-7b-QLoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bugdaryan/MistralSQL-7b-QLoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "bugdaryan/MistralSQL-7b-QLoRA") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from bugdaryan/MistralSQL-7b-QLoRA: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/bugdaryan/MistralSQL-7b-QLoRA/resolve/main/adapter_model.bin
- Command line
-
hf download hf://bugdaryan/MistralSQL-7b-QLoRA/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/bugdaryan/MistralSQL-7b-QLoRA/resolve/main/adapter_model.bin
151 MB
- Xet hash:
- 51b83dca8e5ea4a094a2196fc5d83f9df510d9d4869aff1a204e94a7363bf21b
- Size of remote file:
- 151 MB
- SHA256:
- d4fbffa0b5207b0912e1f9428699e58a8393078dfb48b161ab5b924228f4bb32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.