substratusai/weaviate-gorilla-v4-api-split
The substratusai/weaviate-gorilla-v4-api-split is a 7 billion parameter causal language model developed by substratusai, fine-tuned for generating GraphQL queries from natural language, schema, and API references. This model excels at translating natural language requests into valid Weaviate GraphQL, making it highly specialized for database interaction and API integration tasks. With a context length of 4096 tokens, it efficiently processes complex schema and API documentation to produce accurate query outputs.
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Model Overview
The substratusai/weaviate-gorilla-v4-api-split is a 7 billion parameter language model specifically fine-tuned for generating GraphQL queries. Its primary function is to translate natural language queries into valid Weaviate GraphQL, leveraging provided schema and API reference documentation.
Key Capabilities
- Natural Language to GraphQL Translation: Converts user-friendly natural language requests into structured GraphQL queries.
- Schema-Aware Generation: Utilizes a given GraphQL schema to ensure the generated queries are syntactically correct and semantically aligned with the database structure.
- API Reference Integration: Incorporates API reference documentation to understand the available operations and parameters, enhancing the accuracy of the generated GraphQL.
- Weaviate Compatibility: Optimized for generating GraphQL queries specifically for the Weaviate vector database.
Training Details
The model was fine-tuned on the WeaviateGraphQLGorilla-APISplit-Train dataset, which is designed to teach the model the intricate patterns of GraphQL generation based on natural language, schema, and API references.
Use Cases
This model is ideal for applications requiring automated GraphQL query generation, such as:
- Building natural language interfaces for Weaviate databases.
- Automating data retrieval and manipulation tasks through GraphQL.
- Assisting developers in quickly constructing complex Weaviate queries.