Locutusque/NeuralHyperion-Medium-Preview

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Feb 26, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

Locutusque/NeuralHyperion-Medium-Preview is a 7 billion parameter language model developed by M4-ai, fine-tuned from Mistral-7B-v0.1. It is specifically optimized for advanced reasoning across scientific domains, including complex question answering, medical text comprehension, mathematical reasoning, and logical reasoning. This model excels in multi-domain applications, leveraging its training on the diverse Hyperion dataset and further DPO fine-tuning on Argilla's orca pairs. Its primary strength lies in handling complex inquiries and instructions within technical and scientific contexts.

Loading preview...

Locutusque/NeuralHyperion-Medium-Preview Overview

Locutusque/NeuralHyperion-Medium-Preview is a 7 billion parameter language model, built upon the mistralai/Mistral-7B-v0.1 base model and published by M4-ai. This model is distinguished by its specialized fine-tuning on the Hyperion dataset, which is rich in diverse and complex information spanning programming, medical texts, mathematical problems, and various reasoning tasks. Further refinement was achieved through DPO (Direct Preference Optimization) using Argilla's orca DPO pairs, enhancing its advanced reasoning capabilities.

Key Capabilities

  • Advanced Reasoning: Excels in complex question answering, mathematical reasoning, and logical problem-solving across scientific domains.
  • Multi-domain Comprehension: Strong performance in understanding and generating content related to medical texts, code, and scientific concepts.
  • Conversational AI: Designed to handle intricate conversations with a focus on technical and scientific topics.
  • Code Generation: Capable of understanding complex programming contexts and assisting in code generation.

Performance Highlights

Evaluated on the Open LLM Leaderboard, NeuralHyperion-Medium-Preview demonstrates solid performance for its size:

  • Average Score: 61.67
  • MMLU (5-Shot): 63.73
  • AI2 Reasoning Challenge (25-Shot): 60.67
  • GSM8k (5-Shot): 40.49

Intended Use Cases

This model is ideal for applications requiring robust scientific and technical reasoning, such as:

  • AI-driven tutoring systems for STEM fields (science, medicine, mathematics, computer science).
  • Assistive tools for professionals needing accurate, domain-specific information retrieval.
  • Platforms requiring conversational AI with a strong emphasis on technical and scientific understanding.

Popular Sampler Settings

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

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p