vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Mar 22, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Qwen3-8B-UnBias-Plus-SFT-Instruct is an 8 billion parameter instruction-tuned causal language model developed by the Vector Institute, based on Qwen3-8B. It is specifically fine-tuned for news media bias detection, classification, and neutral rewriting, providing structured JSON responses. This variant is optimized for production inference with lower latency, directly outputting JSON without intermediate thinking steps, and supports vLLM deployment.

Loading preview...

Overview

This model, Qwen3-8B-UnBias-Plus-SFT-Instruct, is an 8 billion parameter instruction-tuned variant of Qwen3-8B, developed by the Vector Institute as part of the UnBias-Plus project. It is specialized in analyzing news articles to detect biased language, classify bias types and severity, suggest neutral replacements, and generate a fully rewritten unbiased version of the article. The model is fine-tuned using Supervised Fine-Tuning (SFT) with LoRA on the vector-institute/Unbias-plus dataset, which contains expert-annotated news articles.

Key Capabilities

  • Comprehensive Bias Detection: Identifies biased language segments within news articles.
  • Bias Classification: Classifies detected bias by type (e.g., loaded language, dehumanizing framing, sensationalism) and severity (Neutral, Recurring biased framing, Strong persuasive tone, Inflammatory rhetoric).
  • Neutral Rewriting: Provides neutral alternative phrases for biased segments and generates a complete unbiased version of the input article.
  • Structured JSON Output: Delivers all analysis and rewritten content in a single, structured JSON response, making it easy for programmatic integration.
  • Production Optimized: Unlike its SFT counterpart, this Instruct variant disables chain-of-thought thinking (enable_thinking=False), leading to lower latency and direct JSON output, making it suitable for production APIs and vLLM deployment.

Good For

  • News Media Analysis: Automating the detection and mitigation of bias in news content.
  • Content Moderation: Identifying and addressing biased language in various textual data.
  • API Deployment: Ideal for integration into applications requiring fast, reliable, and structured bias analysis via vLLM or other OpenAI-compatible serving backends.
  • Research: Studying and understanding linguistic bias patterns, particularly in English-language news.

Finetunes
1 models