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
File size: 751 Bytes
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base_model: Salesforce/codegen-350M-multi
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- peft
- text2sql
---
# codegenstudio/codegen-350M-text2sql-lora
LoRA adapter for text2sql on Salesforce/codegen-350M-multi.
- Checkpoint version: `v3`
- Task: `text2sql`
## Usage
```python
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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