Instructions to use Sayan18/finetune_starcoder2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sayan18/finetune_starcoder2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-3b") model = PeftModel.from_pretrained(base_model, "Sayan18/finetune_starcoder2") - Notebooks
- Google Colab
- Kaggle
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## Intended uses & limitations
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## Intended Uses of the Text-to-SQL Generator
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This Text-to-SQL generator is designed to bridge the gap between users and databases. Here are some of its key intended uses:
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* **Non-programmers interacting with databases:** Users who are unfamiliar with writing SQL queries can leverage this tool to ask questions about the database in natural language and get the corresponding SQL query generated. This allows them to access and analyze data without needing programming expertise.
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## Intended uses & limitations
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This Text-to-SQL generator is designed to bridge the gap between users and databases. Here are some of its key intended uses:
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* **Non-programmers interacting with databases:** Users who are unfamiliar with writing SQL queries can leverage this tool to ask questions about the database in natural language and get the corresponding SQL query generated. This allows them to access and analyze data without needing programming expertise.
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