jastorj/couchmind-v5.8_rl_cold_start-cw-26K-16bit

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 8, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The jastorj/couchmind-v5.8_rl_cold_start-cw-26K-16bit model is a fine-tuned version of Snowflake/Arctic-Text2SQL-R1-7B, specifically optimized for Text-to-SQL generation. It leverages the NL2SQL++ v5.8_rl_cold_start dataset with code-with-thought reasoning to enhance its ability to convert natural language questions into syntactically valid SQL++ queries. This 16-bit quantized model is designed for precise SQL generation, particularly for Couchbase SQL++ environments, and handles complex reasoning tasks to construct queries from schema-less or partially defined contexts.

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Model Overview

This model, jastorj/couchmind-v5.8_rl_cold_start-cw-26K-16bit, is a specialized fine-tune of the Snowflake/Arctic-Text2SQL-R1-7B base model. Its primary function is Text-to-SQL generation, specifically tailored for Couchbase SQL++ queries.

Key Capabilities

  • Natural Language to SQL++ Conversion: Translates natural language questions into syntactically valid SQL++ queries.
  • Code-with-Thought Reasoning: Incorporates a "think" process to generate well-reasoned and detailed SQL queries, even in scenarios with limited schema information.
  • Couchbase SQL++ Expertise: Designed to understand and generate queries for Couchbase environments, including handling bucket, scope, and collection names.
  • Fine-tuned Performance: Utilizes LoRA (Low-Rank Adaptation) with Unsloth and 16-bit quantization for efficient and accurate performance.
  • Schema-Agnostic Query Construction: Demonstrates the ability to construct queries for scenarios where the database schema is not explicitly provided, by inferring and building data inline.

Ideal Use Cases

  • Automated SQL++ Generation: Developers and data analysts needing to programmatically generate Couchbase SQL++ queries from natural language inputs.
  • Complex Query Construction: Scenarios requiring the model to reason about query structure and data relationships, especially when schema details are minimal.
  • Couchbase Application Development: Integrating natural language interfaces for data querying within Couchbase-backed applications.

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