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| license: apache-2.0 |
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| # SLIM-SQL-TOOL |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| **slim-sql-tool** is a 4_K_M quantized GGUF version of slim-sql-1b-v0, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. |
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| [**slim-sql**](https://huggingface.co/llmware/slim-sql-1b-v0) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. |
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| *Note: slim-sql is designed for small, fast, local prototyping and to be effective for 'one-table' lookups - it was not trained or optimized for complex joins and other sophisticated SQL queries.* |
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| To pull the model via API: |
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| from huggingface_hub import snapshot_download |
| snapshot_download("llmware/slim-sql-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) |
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| Load in your favorite GGUF inference engine, or try with llmware as follows: |
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| from llmware.models import ModelCatalog |
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| # this one line will download the model and run a series of tests |
| # includes two sample table schema - go to llmware github repo for end-to-end example |
| ModelCatalog().tool_test_run("slim-sql-tool", verbose=True) |
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| Slim models can also be orchestrated as part of multi-model, multi-step LLMfx calls: |
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| from llmware.agents import LLMfx |
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| llm_fx = LLMfx() |
| llm_fx.load_tool("sql") |
| response = llm_fx.sql(query, table_schema) |
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| Note: please review [**config.json**](https://huggingface.co/llmware/slim-sql-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. |
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| Note: two sample 'hello world' csv tables are included - this is fabricated data - any similarity with real people is coincidental. |
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| ## Model Card Contact |
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| Darren Oberst & llmware team |
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| [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
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