Instructions to use care2achieve/codegen-350M-text2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use care2achieve/codegen-350M-text2sql-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi") model = PeftModel.from_pretrained(base_model, "care2achieve/codegen-350M-text2sql-lora") - Notebooks
- Google Colab
- Kaggle
File size: 783 Bytes
0a93afd 902472f 0a93afd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ---
library_name: peft
base_model: Salesforce/codegen-350M-multi
tags:
- lora
- peft
- code
- text2sql
- codegen
pipeline_tag: text-generation
---
# care2achieve/codegen-350M-text2sql-lora
LoRA adapter for **text2sql** on [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi).
- Checkpoint version: `v4`
- Task: `text2sql`
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Salesforce/codegen-350M-multi"
adapter = "care2achieve/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 `hf-deploy/` package.
|