Hypereum/HivemindEval

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:May 15, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

Hypereum/HivemindEval is an 8 billion parameter AI output quality evaluator, fine-tuned on Qwen3-8B by Hypereum Ltd. It specializes in assessing multi-agent AI outputs across six compliance-focused quality dimensions, providing structured JSON scores and reasoning. With a 32,768 token context length, this model is optimized for evaluating AI agent performance in regulated industries, particularly within UK/EU compliance frameworks.

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HivemindEval v2.0: AI Output Quality Evaluator

HivemindEval v2.0, developed by Hypereum Ltd, is an open-source AI output quality evaluator. Fine-tuned on the Qwen3-8B base model, it is designed to assess the quality of multi-agent AI outputs, particularly in compliance-related scenarios. This version features improved structured output generation and instruction following, building on its predecessor.

Key Capabilities & Features

  • Compliance Evaluation: Scores AI agent outputs across six critical dimensions: Accuracy, Completeness, Regulatory Alignment, Actionability, Coherence, and Evidence Quality.
  • Structured JSON Output: Provides scores (0-100) and reasoning for each dimension in a structured JSON format.
  • Enhanced Context Window: Supports a context length of 32,768 tokens, an upgrade from previous versions.
  • Robust Inference: Achieves 100% valid JSON output rate in internal benchmarks when using the recommended best-of-N sampling strategy with progressive temperature relaxation and robust parsing.
  • Specialized Training: Fine-tuned on approximately 20,000 synthetic compliance evaluation pairs, with training conducted on the Cambridge Dawn HPC.

Ideal Use Cases

  • Automated Compliance Assessment: Evaluating AI agent responses against regulatory standards in UK/EU regulated industries (e.g., PSD2, GDPR, NHS DSPT, EU AI Act).
  • Multi-Agent System Orchestration: As an integral component for assessing the quality of outputs from complex multi-agent AI systems.
  • Structured Data Generation: Generating reliable, structured evaluations for AI-driven processes where output quality is paramount.

Limitations

It's important to note that greedy single-shot inference is unreliable (50% valid JSON rate); the best-of-N strategy is essential for production. The model is English-only, and its compliance focus is primarily on UK/EU regulated industries. Validation loss is higher than some peer models due to the complexity of its structured output schema.