Instructions to use Sreenath/sqlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sreenath/sqlm with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "Sreenath/sqlm") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Sreenath/sqlm: direct link, hf CLI and curl.
- Browser
- Download file 1.8 MB
-
https://huggingface.co/Sreenath/sqlm/resolve/684947e5a881cb2e6bb798b60dce7208a4cf3dcd/tokenizer.json
- Command line
-
hf download hf://Sreenath/sqlm@684947e5a881cb2e6bb798b60dce7208a4cf3dcd/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Sreenath/sqlm/resolve/684947e5a881cb2e6bb798b60dce7208a4cf3dcd/tokenizer.json
1.8 MB
File too large to display, you can check the raw version instead.