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
Download tokenizer_config.json from codegenstudio/codegen-350M-text2sql-lora: direct link, hf CLI and curl.
- Browser
- Download file 277 Bytes
-
https://huggingface.co/codegenstudio/codegen-350M-text2sql-lora/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://codegenstudio/codegen-350M-text2sql-lora/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/codegenstudio/codegen-350M-text2sql-lora/resolve/main/tokenizer_config.json
277 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "is_local": true, | |
| "model_max_length": 2048, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<|endoftext|>" | |
| } | |