Shankarblr/Qwen2.5-1.5B-QA360

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 8, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Shankarblr/Qwen2.5-1.5B-QA360 is a 1.54 billion parameter Qwen2 architecture model developed by Shankar Subramanyam, specifically fine-tuned for requirements-to-test-design. It takes a software requirement and outputs a structured JSON object containing a 360-degree test analysis, including risk level, automation candidacy, affected modules, and various test types. This model excels at generating draft test ideas for QA engineers and business analysts, providing a domain-specialized solution for test-case writing and regression scope identification.

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Overview

Shankarblr/Qwen2.5-1.5B-QA360 is a specialized 1.54 billion parameter language model, built on the Qwen2 architecture and developed by Shankar Subramanyam. It is instruction-tuned to transform a single software requirement into a comprehensive QA360 test-analysis JSON object. This model is not a general-purpose chatbot but a domain-specific tool designed to assist QA engineers, SDETs, and business analysts in drafting test designs.

Key Capabilities

  • Requirements-to-Test-Design: Converts short software requirements into a structured JSON output.
  • Comprehensive Test Analysis: Generates fields for risk_level, automation_candidate, affected_modules, functional_tests, negative_tests, security_tests, accessibility_tests, api_tests, and regression_scope.
  • Domain-Specialized Output: Emits only a JSON object following a predefined schema, making it suitable for automated workflows.
  • Small Footprint: At 1.54B parameters, it offers a compact solution for specialized test analysis tasks.

Good For

  • Drafting 360-degree test analyses from software requirements.
  • Seeding test-case writing in ALM tools like Xray, TestRail, or Azure DevOps.
  • Suggesting regression scope for new features or change requests.
  • Producing initial JSON payloads for QA agents or RAG pipelines in an SDLC stack.
  • Further fine-tuning or DPO on private requirements corpora.

Limitations

  • Not a General Chatbot: Not intended for general conversation or code generation.
  • Draft Outputs: Outputs are draft ideas and require human review and validation.
  • Schema Fidelity: Best performance within a 2048-token prompt + completion budget; long requirements should be summarized.
  • Bias in Risk Labels: Risk levels are learned from the SFT corpus and may over-index on certain patterns (e.g., authentication often being 'High').

Finetunes
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