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RichardErkhov/
defog_-_sqlcoder-7b-2-gguf

Name: defog_-_sqlcoder-7b-2-gguf
Availability: InStock

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gguf

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sqlcoder-7b-2 - GGUF

Model creator: https://huggingface.co/defog/
Original model: https://huggingface.co/defog/sqlcoder-7b-2/

Name	Quant method	Size
sqlcoder-7b-2.Q2_K.gguf	Q2_K	2.36GB
sqlcoder-7b-2.IQ3_XS.gguf	IQ3_XS	2.6GB
sqlcoder-7b-2.IQ3_S.gguf	IQ3_S	2.75GB
sqlcoder-7b-2.Q3_K_S.gguf	Q3_K_S	2.75GB
sqlcoder-7b-2.IQ3_M.gguf	IQ3_M	2.9GB
sqlcoder-7b-2.Q3_K.gguf	Q3_K	3.07GB
sqlcoder-7b-2.Q3_K_M.gguf	Q3_K_M	3.07GB
sqlcoder-7b-2.Q3_K_L.gguf	Q3_K_L	3.35GB
sqlcoder-7b-2.IQ4_XS.gguf	IQ4_XS	3.4GB
sqlcoder-7b-2.Q4_0.gguf	Q4_0	3.56GB
sqlcoder-7b-2.IQ4_NL.gguf	IQ4_NL	3.58GB
sqlcoder-7b-2.Q4_K_S.gguf	Q4_K_S	3.59GB
sqlcoder-7b-2.Q4_K.gguf	Q4_K	3.8GB
sqlcoder-7b-2.Q4_K_M.gguf	Q4_K_M	3.8GB
sqlcoder-7b-2.Q4_1.gguf	Q4_1	3.95GB
sqlcoder-7b-2.Q5_0.gguf	Q5_0	4.33GB
sqlcoder-7b-2.Q5_K_S.gguf	Q5_K_S	4.33GB
sqlcoder-7b-2.Q5_K.gguf	Q5_K	4.45GB
sqlcoder-7b-2.Q5_K_M.gguf	Q5_K_M	4.45GB
sqlcoder-7b-2.Q5_1.gguf	Q5_1	4.72GB
sqlcoder-7b-2.Q6_K.gguf	Q6_K	5.15GB

Original model description:

license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation

Update notice

The model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins.

If you downloaded the model before that, please redownload the weights for best performance.

Model Card for SQLCoder-7B-2

A capable large language model for natural language to SQL generation.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Defog, Inc
Model type: [Text to SQL]
License: [CC-by-SA-4.0]
Finetuned from model: [CodeLlama-7B]

Model Sources [optional]

Uses

This model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool.

This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access.

How to Get Started with the Model

Use the code here to get started with the model.

Prompt

Please use the following prompt for optimal results. Please remember to use do_sample=False and num_beams=4 for optimal results.

### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]

### Database Schema
The query will run on a database with the following schema:
{table_metadata_string_DDL_statements}

### Answer
Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
[SQL]

Evaluation

This model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities.

You can read more about the methodology behind SQLEval here.

Results

We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.

	date	group_by	order_by	ratio	join	where
sqlcoder-70b	96	91.4	97.1	85.7	97.1	91.4
sqlcoder-7b-2	96	91.4	94.3	91.4	94.3	77.1
sqlcoder-34b	80	94.3	85.7	77.1	85.7	80
gpt-4	72	94.3	97.1	80	91.4	80
gpt-4-turbo	76	91.4	91.4	62.8	88.6	77.1
natural-sql-7b	56	88.6	85.7	60	88.6	80
sqlcoder-7b	64	82.9	74.3	54.3	74.3	74.3
gpt-3.5	72	77.1	82.8	34.3	65.7	71.4
claude-2	52	71.4	74.3	57.1	65.7	62.9

RichardErkhov/defog_-_sqlcoder-7b-2-gguf