WPAI-INC
/

WPAIGPT-sql-01

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 ---
 base_model: unsloth/qwen2.5-coder-7b-bnb-4bit
 library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.13.0

 ---
 base_model: unsloth/qwen2.5-coder-7b-bnb-4bit
 library_name: peft
+license: apache-2.0
+datasets:
+- WPAI-INC/wp-sql-instruction-pairs
+tags:
+- wordpress
+- sql
+- wpaigpt
+- text2sql
 ---
+WPAIGPT-SQL-01 is a specialized text-to-SQL model designed for WordPress and WordPress plugins. It generates SQL queries based on natural language requests, with a focus on WordPress-specific database structures and popular plugins.
 ## Model Details
 ### Model Description
+WPAIGPT-SQL-01 is a fine-tuned version of the Qwen2.5-Coder-7B model, optimized for generating SQL queries for WordPress databases. It can handle queries related to core WordPress tables as well as tables added by various plugins.
+- **Developed by:** [WPAI Inc](https://wpai.co), James LePage
+- **Funded by:** WPAI Inc
+- **Model type:** Text-to-SQL Language Model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen2.5-Coder-7B-Instruct
 ## Uses
 ### Direct Use
+The model is designed for direct text-to-SQL generation for WordPress databases. Users can input natural language requests, optionally including plugin names, versions, and table descriptions, to generate SQL queries. This is particularly useful for:
+1. Retrieving information from WordPress databases
+2. Adding functionality to existing WordPress plugins by generating SQL queries
+3. Assisting developers in creating database queries for WordPress projects
+### Downstream Use
+1. Integration into WPAI products, primarily [AgentWP](https://agentwp.com), for real-time information retrieval from WordPress websites
+2. Use in code generation tools to create queries for more complete WordPress systems like plugins
+3. Incorporation into agent pipelines for WordPress-related tasks
 ### Out-of-Scope Use
+While there are no strict out-of-scope uses, users should be aware that as a Transformer-based model, it can potentially hallucinate or generate incorrect queries. All generated SQL should be verified before execution against a live WordPress database.
 ## Bias, Risks, and Limitations
+- The model may be biased towards more popular WordPress plugins and those with more extensive database interactions.
+- There's a bias towards SELECT and read-only operations over database-modifying queries.
+- The model's knowledge is limited to the training data, which may not cover all possible WordPress plugins or database structures.
+- As with any language model, there's a risk of generating syntactically correct but logically incorrect or potentially harmful SQL queries.
 ### Recommendations
+- Always verify and test generated SQL queries before executing them on a live WordPress database.
+- Use in conjunction with proper access controls and user authentication to prevent unauthorized database access.
+- Regularly update the model to include knowledge of new WordPress versions and popular plugins.
+- Implement additional safety checks and validations when using the model in automated systems.
 ## Training Details
 ### Training Data
+The training data consists of hundreds of thousands of instruction-to-SQL examples, structured as follows:
+- 25% include described tables that WordPress plugins may add, along with plugin name, version, and instruction
+- 25% include only the plugin name, version, and instruction
+- 50% include only the instruction
+The queries are derived from popular WordPress plugins, both from the official WordPress repository and premium plugins. The data generation process involves:
+1. Indexing plugin codebases
+2. Extracting code that manipulates the WordPress database
+3. Synthetically generating SQL queries
+4. Verifying queries by running them against a WordPress installation with the plugin installed
+There's a bias towards the most popular WordPress plugins and those with significant database interactions. Additional manual data has been included for specific plugins like WooCommerce, LearnDash, and Gravity Forms.
+### Training Procedure
+The training procedure details are available in the provided Python notebook. For specific information about hyperparameters, preprocessing steps, and other training details, please refer to the notebook.
 ## Evaluation
 ### Testing Data, Factors & Metrics
+Formal evaluations have not been conducted. The model's performance is primarily assessed through:
+1. A/B testing in WPAI products
+2. User rankings on end systems (AgentWP, [CodeWP](https://codewp.ai), and other WPAI products)
+## Technical Specifications
 ### Model Architecture and Objective
+The model is based on the Qwen2.5-Coder-7B architecture, fine-tuned for the specific task of WordPress SQL generation. It uses a causal language modeling objective to generate SQL queries based on natural language inputs.
+Key features of the base Qwen2.5-Coder-7B model include:
+- Number of Parameters: 7.61B
+- Number of Layers: 28
+- Number of Attention Heads: 28 for Q and 4 for KV (using Grouped-Query Attention)
+- Context Length: Full 131,072 tokens (with the ability to handle long contexts using YaRN technique)
+The model has been specifically fine-tuned to understand WordPress database structures and generate appropriate SQL queries, maintaining its coding capabilities while focusing on the WordPress ecosystem.