Locutusque/Hyperion-1.5-Mistral-7B

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Mar 2, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Locutusque/Hyperion-1.5-Mistral-7B is a 7 billion parameter language model developed by M4-ai, fine-tuned on the Hyperion-v1.5 dataset. This model excels in advanced reasoning across scientific domains, including complex question answering, code generation, medical text comprehension, and mathematical and logical reasoning. With an 8192-token context length, it is designed for researchers and practitioners tackling challenging problems in science, medicine, mathematics, and computer science.

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Locutusque/Hyperion-1.5-Mistral-7B: Advanced Reasoning Across Scientific Domains

Locutusque/Hyperion-1.5-Mistral-7B is a 7 billion parameter language model, built upon the mistralai/Mistral-7B-v0.1 base model and developed by M4-ai. It has been fine-tuned on the comprehensive Hyperion-v1.5 dataset, which integrates diverse and complex information from programming, medical texts, mathematical problems, and various reasoning tasks. This specialized training enables the model to handle intricate inquiries and instructions effectively.

Key Capabilities

  • Complex Question Answering: Designed for advanced understanding and response generation across scientific subjects.
  • Conversational AI: Capable of engaging in technical and scientific discussions.
  • Code Generation: Proficient in generating and understanding complex programming contexts.
  • Medical Text Comprehension: Specialized in interpreting medical information.
  • Mathematical and Logical Reasoning: Excels in solving mathematical problems and applying logical deduction.

Intended Use Cases

  • AI-driven Tutoring Systems: Ideal for educational platforms in science, medicine, mathematics, and computer science.
  • Assistive Tools for Professionals: Supports fast and accurate domain-specific information retrieval.
  • Technical & Scientific Conversational AI: Powers platforms requiring deep technical understanding.

Performance Highlights

Evaluations on the Open LLM Leaderboard show an average score of 61.43, with notable results including 83.64 on HellaSwag (10-Shot) and 63.57 on MMLU (5-Shot). While strong in many areas, the model's diversity in training data may lead to some inconsistencies in responses due to variations in data formatting and annotation quality. The model is released under the Apache-2.0 license.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

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