This model is a QLoRA fine-tuned variant of Qwen3-4B-Instruct, specialized for explaining statistical test pipeline outputs in a clear, structured, and technically correct way.
It is designed to transform structured tool_json outputs (e.g. T-Test, ANOVA, correlation, clustering, chi-square results) into high-quality natural language explanations following a strict five-section analytical format.
Its' goal is to serve my personal project app available on spaces.
The fine-tuning process focused on reducing hallucinations, improving methodological correctness, and strengthening interpretability, while preserving the general instruction-following abilities of the base model.
Model Details
Model Description
Developed by: João Vaz, Independent research project
Shared by: Ozymandias2
Model type: Instruction-tuned causal language model with LoRA adapters
Language(s) (NLP): English
License: Same as base model (Qwen/Qwen3-4B-Instruct-2507)
Finetuned from model: Qwen/Qwen3-4B-Instruct-2507
This model was trained to generate structured statistical explanations using the following fixed template:
-Missing Data Analysis
-Pre-Test Diagnostics
-Test Selection Rationale
-Test Results
-Interpretation
The model explicitly avoids:
-causal language for observational analyses,
-hallucinated preprocessing steps,
-incorrect test naming or directionality.
