jastorj/couchmind-v5.7.8.1_arctic_stage_2-cw-12K-16bit

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jun 26, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

jastorj/couchmind-v5.7.8.1_arctic_stage_2-cw-12K-16bit is a 7.6 billion parameter model fine-tuned by jastorj from Snowflake's Arctic-Text2SQL-R1-7B. Optimized for Text-to-SQL generation, it excels at converting natural language questions into syntactically valid Couchbase SQL++ queries. This model leverages a 32768-token context window and incorporates code-with-thought reasoning from the NL2SQL++ v5.7.8.1_arctic_stage_2 dataset, making it highly effective for complex database interactions.

Loading preview...

Couchmind v5.7.8.1 Arctic Stage 2: Advanced Text-to-SQL for Couchbase

This model, developed by jastorj, is a specialized fine-tune of the Snowflake/Arctic-Text2SQL-R1-7B base model, specifically engineered for Text-to-SQL generation within Couchbase environments. With 7.6 billion parameters and a 32768-token context length, it's designed to accurately translate natural language questions into Couchbase SQL++ queries.

Key Capabilities

  • Couchbase SQL++ Generation: Converts natural language into precise SQL++ queries, adhering to specified bucket, scope, and collection names.
  • Code-with-Thought Reasoning: Fine-tuned on the NL2SQL++ v5.7.8.1_arctic_stage_2 dataset, which includes code-with-thought reasoning, enhancing its ability to handle complex query logic.
  • Schema Awareness: Utilizes provided database schemas to generate contextually accurate and syntactically valid queries.
  • Optimized for Accuracy: Emphasizes selecting only the explicitly requested columns, ensuring lean and relevant query outputs.

When to Use This Model

  • Automating Database Interactions: Ideal for applications requiring automated generation of Couchbase SQL++ queries from user input.
  • Developers and Data Analysts: Useful for quickly generating complex queries without deep manual SQL++ construction.
  • Couchbase Environments: Specifically tailored for Couchbase databases, ensuring compatibility and optimal performance with its unique query language.
  • Complex Query Scenarios: Benefits from its code-with-thought training for handling intricate natural language requests that require multi-table joins or conditional logic.

Finetunes
1 models