aphoticshaman/elle-72b-ultimate

TEXT GENERATIONConcurrency Cost:4Model Size:72.7BQuant:FP8Ctx Length:32kLicense:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Elle-72B-Ultimate is a 72.7 billion parameter geopolitical intelligence model developed by aphoticshaman, fine-tuned on Qwen2.5-72B-Instruct. Specialized for real-time geopolitical risk assessment, multi-source intelligence synthesis, and causal chain analysis, it excels at predicting global event cascades and regime stability. With a context length of 32,768 tokens, this model is designed for enterprise geopolitical risk dashboards and intelligence briefing generation.

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Elle-72B-Ultimate: Geopolitical Intelligence Model

Elle-72B-Ultimate is a specialized 72.7 billion parameter language model, fine-tuned by aphoticshaman on the Qwen2.5-72B-Instruct base. It is engineered for advanced geopolitical analysis, focusing on synthesizing complex information and predicting global events.

Key Capabilities

  • Geopolitical Risk Assessment: Provides real-time analysis of global risks.
  • Intelligence Synthesis: Integrates data from multiple sources to form comprehensive intelligence briefings.
  • Causal Chain Analysis: Identifies and analyzes the sequence of events leading to geopolitical outcomes.
  • Regime Stability & Cascade Risk: Detects indicators of regime stability and predicts cascading risks from global events.
  • Specialized Training Data: Trained on curated datasets including GDELT Event Data, World Bank Indicators, USGS Seismic Data, and expert-verified intelligence briefings.

Intended Use Cases

Elle is designed for applications requiring deep geopolitical insight, such as:

  • Enterprise geopolitical risk dashboards
  • Automated intelligence briefing generation
  • Supply chain and investment risk assessment
  • Policy impact modeling

Technical Details

The model utilizes a 32,768-token context length and was fine-tuned using LoRA (r=64, alpha=128) with Unsloth + PEFT. It operates in FP16 precision, requiring significant hardware resources (e.g., 4x H100/H200 80GB GPUs for inference) or quantization for smaller deployments. Its knowledge cutoff is December 2024, aligned with its training data.

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