wehub-resource-sync
27 lines
1.3 KiB
Markdown
27 lines
1.3 KiB
Markdown
# Tune Llama 3 for text-to-SQL and improve accuracy from 30% to 95%
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This repo and notebook `meta_lamini.ipynb` demonstrate how to tune Llama 3 to generate valid SQL queries and improve accuracy from 30% to 95%.
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In this notebook we'll be using Lamini, and more specifically, Lamini Memory Tuning.
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Lamini is an integrated platform for LLM inference and tuning for the enterprise. Lamini Memory Tuning is a new tool you can use to embed facts into LLMs that improves factual accuracy and reduces hallucinations. Inspired by information retrieval, this method has set a new standard of accuracy for LLMs with less developer effort.
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Learn more about Lamini Memory Tuning: https://www.lamini.ai/blog/lamini-memory-tuning
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Please head over to https://app.lamini.ai/account to get your free api key.
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You can authenticate by writing the following to a file `~/.lamini/configure.yaml`
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```
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production:
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key: <YOUR-LAMINI-API-KEY>
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```
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This tuning tutorial uses the `nba_roster` sqlite database to tune a Llama 3 model.
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## Additional resources
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▫️ Fortune 500 case study: http://www.lamini.ai/blog/llm-text-to-sql <br>
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▫️ Technical paper: https://github.com/lamini-ai/Lamini-Memory-Tuning/blob/main/research-paper.pdf <br>
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▫️ Model weights: https://huggingface.co/engineering-lamini/lamini-1-random
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