Instructions to use codegenstudio/codegen-350M-text2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use codegenstudio/codegen-350M-text2sql-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/kaggle/working/models/base/Salesforce__codegen-350M-multi") model = PeftModel.from_pretrained(base_model, "codegenstudio/codegen-350M-text2sql-lora") - Notebooks
- Google Colab
- Kaggle
codegenstudio/codegen-350M-text2sql-lora
LoRA adapter for text2sql on Salesforce/codegen-350M-multi.
- Checkpoint version:
v3 - Task:
text2sql
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Salesforce/codegen-350M-multi"
adapter = "codegenstudio/codegen-350M-text2sql-lora"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, adapter)
Or use the multi-adapter API in this project's fastapi-deploy/ package.
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Model tree for codegenstudio/codegen-350M-text2sql-lora
Base model
Salesforce/codegen-350M-multi