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EN
Hyperion 3.0 Mistral 7B DPO
A DPO-optimized model based on Mistral-7B, excelling in Q&A, code generation, and multi-domain reasoning tasks
Downloads 15
Release Time : 3/24/2024
Model Overview
A high-performance language model fine-tuned with Direct Preference Optimization (DPO) technology, focusing on complex reasoning, programming assistance, and professional domain problem-solving
Model Features
DPO Optimization
Direct Preference Optimization using 20,000 high-quality preference pairs generated by GPT-4
Multi-domain Capability
Demonstrates outstanding performance in STEM, social sciences, and humanities
Professional Reasoning
Specifically enhanced for mathematical derivation and logical reasoning, capable of handling complex scientific problems
Model Capabilities
Text generation
Technical Q&A
Code generation
Medical text analysis
Mathematical problem solving
Logical reasoning
Multi-turn dialogue
Use Cases
Education
Physics teaching assistance
Analyzing mechanics problems and establishing differential equations
As shown in the example, can fully derive projectile motion equations
Software Development
Code generation
Generating executable code from natural language descriptions
Healthcare
Medical text analysis
Parsing professional medical literature and extracting key information
🚀 Hyperion-3.0-Mistral-7B-DPO
This is an advanced language model that excels in a wide range of complex tasks, including question answering, conversational AI, and code generation. It is fine - tuned with high - quality preference pairs to ensure excellent performance.
🚀 Quick Start
This section is not provided in the original README, so it is skipped.
✨ Features
- Multi - task Capability: Capable of handling question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.
- High - Quality Training: Fine - tuned with 20,000 carefully curated high - quality preference pairs generated by GPT - 4.
- Human - Aligned Output: Optimized using Direct Preference Optimization (DPO) to align outputs with human preferences.
📦 Installation
This section is not provided in the original README, so it is skipped.
📚 Documentation
Model Details
| Property | Details |
|---|---|
| Model Name | Locutusque/Hyperion - 3.0 - Mistral - 7B - DPO |
| Base Model | mistralai/Mistral - 7B - v0.1 |
| Publisher | Locutusque |
| Model Type | Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning |
| Language | Multi - domain, English language |
| License | Apache - 2.0 |
Model Description
Locutusque/Hyperion-3.0-Mistral-7B-DPO is an advanced language model fine - tuned with a dataset of 20,000 meticulously curated high - quality preference pairs using Direct Preference Optimization (DPO). The examples were generated by GPT - 4 to ensure exceptional quality and relevance. This model is designed to provide superior performance across a wide range of complex tasks, including question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.
Intended Use
This model is intended for researchers, developers, and organizations seeking a highly capable and reliable language model for tackling challenging problems across various domains. Potential use cases include:
- Intelligent tutoring systems and educational applications in science, medicine, mathematics, and computer science.
- Advanced conversational AI for technical support, customer service, and domain - specific chatbots.
- Code generation and analysis tools for software development and programming assistance.
- Medical text analysis and information retrieval for healthcare professionals and researchers.
- Mathematical problem - solving and logical reasoning applications for academia and industry.
Training Data
The Locutusque/Hyperion-3.0-Mistral-7B-DPO model was fine - tuned on a carefully curated dataset of 20,000 preference pairs, where 4,000 examples were used to fine - tune. These examples were generated by GPT - 4 to ensure the highest quality and relevance across various domains, including programming, medical texts, mathematical problems, and reasoning tasks. The training data was further optimized using Direct Preference Optimization (DPO) to align the model's outputs with human preferences and improve overall performance.
Quants
- ExLlamaV2: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-DPO-exl2
- GGUF: https://huggingface.co/bartowski/Hyperion-3.0-Mistral-7B-DPO-GGUF
Evaluation Results
mmlu flan cot 5 - shot
| Tasks | Version | Filter | n - shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu_flan_cot_fewshot | N/A | get - answer | 0 | exact_match | 0.5833 | ± | 0.0118 |
| - mmlu_flan_cot_fewshot_humanities | N/A | get - answer | 0 | exact_match | 0.5039 | ± | 0.0205 |
| - mmlu_flan_cot_fewshot_formal_logic | 0 | get - answer | 0 | exact_match | 0.2143 | ± | 0.1138 |
| - mmlu_flan_cot_fewshot_high_school_european_history | 0 | get - answer | 0 | exact_match | 0.6667 | ± | 0.1143 |
| - mmlu_flan_cot_fewshot_high_school_us_history | 0 | get - answer | 0 | exact_match | 0.7727 | ± | 0.0914 |
| - mmlu_flan_cot_fewshot_high_school_world_history | 0 | get - answer | 0 | exact_match | 0.5385 | ± | 0.0997 |
| - mmlu_flan_cot_fewshot_international_law | 0 | get - answer | 0 | exact_match | 0.9231 | ± | 0.0769 |
| - mmlu_flan_cot_fewshot_jurisprudence | 0 | get - answer | 0 | exact_match | 0.5455 | ± | 0.1575 |
| - mmlu_flan_cot_fewshot_logical_fallacies | 0 | get - answer | 0 | exact_match | 0.7778 | ± | 0.1008 |
| - mmlu_flan_cot_fewshot_moral_disputes | 0 | get - answer | 0 | exact_match | 0.5526 | ± | 0.0817 |
| - mmlu_flan_cot_fewshot_moral_scenarios | 0 | get - answer | 0 | exact_match | 0.4000 | ± | 0.0492 |
| - mmlu_flan_cot_fewshot_philosophy | 0 | get - answer | 0 | exact_match | 0.7647 | ± | 0.0738 |
| - mmlu_flan_cot_fewshot_prehistory | 0 | get - answer | 0 | exact_match | 0.6571 | ± | 0.0814 |
| - mmlu_flan_cot_fewshot_professional_law | 0 | get - answer | 0 | exact_match | 0.3294 | ± | 0.0362 |
| - mmlu_flan_cot_fewshot_world_religions | 0 | get - answer | 0 | exact_match | 0.8947 | ± | 0.0723 |
| - mmlu_flan_cot_fewshot_other | N/A | get - answer | 0 | exact_match | 0.6833 | ± | 0.0244 |
| - mmlu_flan_cot_fewshot_business_ethics | 0 | get - answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_clinical_knowledge | 0 | get - answer | 0 | exact_match | 0.5862 | ± | 0.0931 |
| - mmlu_flan_cot_fewshot_college_medicine | 0 | get - answer | 0 | exact_match | 0.6364 | ± | 0.1050 |
| - mmlu_flan_cot_fewshot_global_facts | 0 | get - answer | 0 | exact_match | 0.6000 | ± | 0.1633 |
| - mmlu_flan_cot_fewshot_human_aging | 0 | get - answer | 0 | exact_match | 0.6087 | ± | 0.1041 |
| - mmlu_flan_cot_fewshot_management | 0 | get - answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_marketing | 0 | get - answer | 0 | exact_match | 0.8000 | ± | 0.0816 |
| - mmlu_flan_cot_fewshot_medical_genetics | 0 | get - answer | 0 | exact_match | 1.0000 | ± | 0.0000 |
| - mmlu_flan_cot_fewshot_miscellaneous | 0 | get - answer | 0 | exact_match | 0.8023 | ± | 0.0432 |
| - mmlu_flan_cot_fewshot_nutrition | 0 | get - answer | 0 | exact_match | 0.6667 | ± | 0.0833 |
| - mmlu_flan_cot_fewshot_professional_accounting | 0 | get - answer | 0 | exact_match | 0.4839 | ± | 0.0912 |
| - mmlu_flan_cot_fewshot_professional_medicine | 0 | get - answer | 0 | exact_match | 0.5806 | ± | 0.0901 |
| - mmlu_flan_cot_fewshot_virology | 0 | get - answer | 0 | exact_match | 0.3889 | ± | 0.1182 |
| - mmlu_flan_cot_fewshot_social_sciences | N/A | get - answer | 0 | exact_match | 0.7003 | ± | 0.0239 |
| - mmlu_flan_cot_fewshot_econometrics | 0 | get - answer | 0 | exact_match | 0.4167 | ± | 0.1486 |
| - mmlu_flan_cot_fewshot_high_school_geography | 0 | get - answer | 0 | exact_match | 0.9091 | ± | 0.0627 |
| - mmlu_flan_cot_fewshot_high_school_government_and_politics | 0 | get - answer | 0 | exact_match | 0.8095 | ± | 0.0878 |
| - mmlu_flan_cot_fewshot_high_school_macroeconomics | 0 | get - answer | 0 | exact_match | 0.6512 | ± | 0.0735 |
| - mmlu_flan_cot_fewshot_high_school_microeconomics | 0 | get - answer | 0 | exact_match | 0.5769 | ± | 0.0988 |
| - mmlu_flan_cot_fewshot_high_school_psychology | 0 | get - answer | 0 | exact_match | 0.9000 | ± | 0.0391 |
| - mmlu_flan_cot_fewshot_human_sexuality | 0 | get - answer | 0 | exact_match | 0.6667 | ± | 0.1421 |
| - mmlu_flan_cot_fewshot_professional_psychology | 0 | get - answer | 0 | exact_match | 0.6522 | ± | 0.0578 |
| - mmlu_flan_cot_fewshot_public_relations | 0 | get - answer | 0 | exact_match | 0.5833 | ± | 0.1486 |
| - mmlu_flan_cot_fewshot_security_studies | 0 | get - answer | 0 | exact_match | 0.4074 | ± | 0.0964 |
| - mmlu_flan_cot_fewshot_sociology | 0 | get - answer | 0 | exact_match | 0.8182 | ± | 0.0842 |
| - mmlu_flan_cot_fewshot_us_foreign_policy | 0 | get - answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_stem | N/A | get - answer | 0 | exact_match | 0.4866 | ± | 0.0262 |
| - mmlu_flan_cot_fewshot_abstract_algebra | 0 | get - answer | 0 | exact_match | 0.0909 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_anatomy | 0 | get - answer | 0 | exact_match | 0.4286 | ± | 0.1373 |
| - mmlu_flan_cot_fewshot_astronomy | 0 | get - answer | 0 | exact_match | 0.5625 | ± | 0.1281 |
| - mmlu_flan_cot_fewshot_college_biology | 0 | get - answer | 0 | exact_match | 0.5000 | ± | 0.1291 |
| - mmlu_flan_cot_fewshot_college_chemistry | 0 | get - answer | 0 | exact_match | 0.5000 | ± | 0.1890 |
| - mmlu_flan_cot_fewshot_college_computer_science | 0 | get - answer | 0 | exact_match | 0.2727 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_college_mathematics | 0 | get - answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| - mmlu_flan_cot_fewshot_college_physics | 0 | get - answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| - mmlu_flan_cot_fewshot_computer_security | 0 | get - answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_conceptual_physics | 0 | get - answer | 0 | exact_match | 0.6538 | ± | 0.0951 |
| - mmlu_flan_cot_fewshot_electrical_engineering | 0 | get - answer | 0 | exact_match | 0.7500 | ± | 0.1118 |
| - mmlu_flan_cot_fewshot_elementary_mathematics | 0 | get - answer | 0 | exact_match | 0.7317 | ± | 0.0701 |
| - mmlu_flan_cot_fewshot_high_school_biology | 0 | get - answer | 0 | exact_match | 0.5938 | ± | 0.0882 |
📄 License
The model uses the Apache - 2.0 license.
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