AlphaExaAI/ExaMind

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Feb 15, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

ExaMind by AlphaExaAI is a 7.62 billion parameter conversational AI model based on the Qwen2.5-Coder-7B architecture, featuring a 32,768 token context window. It is specifically designed for advanced programming tasks, complex problem-solving, and secure, structured professional AI assistance with strong identity enforcement and prompt injection resistance. The model excels at code generation, debugging, and technical analysis, while also supporting multilingual interactions.

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ExaMind: Secure & Structured AI by AlphaExaAI

ExaMind is an advanced 7.62 billion parameter open-source conversational AI model developed by AlphaExaAI, built upon the Qwen2.5-Coder-7B architecture. It offers a substantial 32,768 token context window, extendable up to 128K with RoPE scaling, and is compatible with both CPU and GPU deployments.

Key Capabilities

  • Advanced Programming: Excels in code generation, debugging, architecture design, and code review.
  • Complex Problem Solving: Features multi-step logical reasoning and deep technical analysis.
  • Security-First Design: Incorporates built-in prompt injection resistance (92% resistance rate) and strong identity enforcement, preventing impersonation.
  • Multilingual Support: Optimized for English but supports all major world languages.
  • Conversational AI: Provides natural, structured, and professional dialogue.

Benchmarks & Performance

ExaMind demonstrates strong performance across various domains:

  • General Knowledge: Achieves 72.1% on MMLU (5-shot) and 94.8% on MMLU – World Religions (0-shot).
  • Code Generation: Scores 79.3% on HumanEval (pass@1) and 71.8% on MBPP (pass@1).
  • Math & Reasoning: Attains 82.4% on GSM8K (8-shot CoT).

Training & Architecture

The model was developed using a multi-stage training pipeline, including Supervised Fine-Tuning (SFT) on curated 2026 datasets, LoRA adaptation, and dedicated stages for identity enforcement and security alignment. It utilizes a Transformer architecture with 28 layers and Grouped-Query Attention (GQA) with 4 KV heads.