What is the InternLM large model
The InternLM large model is a series of AI models launched by Shanghai AI Laboratory, driving the development of AI technology based on powerful reasoning capabilities, multimodal understanding, and efficient information processing. The InternLM large model system includes multiple sub-models, such asInternLM, InternVL, FengWu, etc., covering multiple fields such as language processing, multimodal understanding, and meteorological forecasting. The InternLM large model has excellent performance and promotes technology sharing and innovation based on an open-source strategy, providing strong support for fields such as academic research, industrial applications, and education, and empowering the intelligent development of various industries.
Sub-models included in the InternLM large model
- InternLM: Multilingual large model
- Ultra-long text processing: Supports text input of up to one million tokens, suitable for long document understanding and complex interaction scenarios.
- Enhanced reasoning capability: Performs excellently on multiple reasoning evaluation sets, especially in mathematical ability, with significant performance improvement.
- Autonomous information search and integration: Online search filters and integrates information from a large number of web pages to solve complex problems.
- Open source, free for commercial use: Provides free commercial authorization, promoting technology sharing and innovation.
- Diverse parameter versions: Provides model versions of different scales, adapting to diverse application needs from lightweight to ultra-large.
- InternVL 2.5: Open-source multimodal large model
- Multimodal understanding: Processes and understands multiple modal information such as text, images, and videos.
- Multidisciplinary reasoning: Conducts complex reasoning and problem-solving within multiple disciplinary fields.
- Real-world understanding: Understands and analyzes real-world scenarios and events.
- Multimodal hallucination detection: Identifies and distinguishes real and fictional visual information.
- Visual grounding: Matches text descriptions with actual objects in images.
- Multilingual processing: Supports understanding and generation in multiple languages.
- FengWu: Meteorological and oceanic forecasting large model system
- Nowcasting precipitation forecasting: Accurately predicts convective-scale nowcasting precipitation, balancing spatial fineness and accuracy, and effectively improving the warning capability for sudden meteorological events.
- Global medium-range weather forecasting: The effective forecast period for medium-range forecasting exceeds 10 days, and the resolution is improved to 10 kilometers, significantly improving forecast accuracy and timeliness
- Global interannual ocean forecasting: The first AI model for interannual ocean forecasting, with the forecast lead time extended to the interannual scale, providing long-term forecasting support for marine scientific research and related industries.
- Airfoil: Airfoil generative AI system
- Intelligent generation and editing: Enables intelligent generation and editing of supercritical airfoils, meeting the complex design needs of next-generation civil aircraft.
- Controllable design parameters: The system supports flexible adjustment of design parameters, and the output results are interpretable, enabling better understanding and adjustment of airfoils and reducing design iteration cycles.
- Embedded Professional Constraints: The system embeds professional constraints such as flow field characteristics, aerodynamic performance requirements, structural limitations, and material limitations to ensure that the generated airfoils meet practical application needs.
- InternSky: 3D spatial large model
- Efficient Training and Rendering: Based on NeRF technology, it has extremely high training and rendering efficiency.
- Large-Scale Urban Modeling: Covers large urban areas, balancing broad and detailed real-world 3D modeling.
- Integrated System: Integrates algorithms, operators, computing systems, and user interaction to achieve integrated training, rendering, and interaction.
- InternJishi: Financial domain large language model
- Focused on the Financial Domain: Improves the efficiency of financial text recognition, analysis, and generation, and optimizes information acquisition and analysis efficiency.
- Introduces Retrieval Augmentation: Based on vector data updates, it provides auxiliary information for financial reasoning and enhances the model's practicality.
- Security and Compliance Checks: Integrates risk warnings and content security checks to ensure the accuracy and compliance of financial information.
- InternYanpu: Spectroscopy professional model
- Spectral Analysis: Quickly analyzes different types of spectra such as free radicals, multi-species, and metals, and is widely used in multiple disciplines.
- Literature Linking: Links to global literature databases, provides systematic knowledge references, and supports interdisciplinary innovation.
- Professional Model: The world's first large model applied to the analysis of electron paramagnetic resonance (EPR) spectra, enhancing analytical capabilities in the professional field.
- InternThinker: Strong reasoning model
- Handling Complex Reasoning Tasks: Performs excellently in various complex reasoning tasks such as mathematics, programming, and logic puzzles.
- Long Thinking Capability: Performs long-chain logical reasoning and handles multi-step reasoning problems.
- Meta-Action Thinking Capability: Has metacognitive ability and can self-reflect and adjust strategies.
- General-Professional Integration: Improves model performance based on data synthesis and distillation, and large-scale sandbox environment feedback.
- InternPuxiang: Ultra-high dynamic imaging algorithm
- Ultra-High Dynamic Range Imaging: Under extreme conditions with exposure differences of up to 9 stops, it restores image details and generates high-quality images.
- Dynamic Scene Processing: Effectively solves the problem of motion artifacts from dynamic objects.
- Multi-Exposure Fusion: Supports arbitrary exposure input and fuses images with different exposure levels.
- MindSearch: AI search framework
- Complex Query Processing: Decomposes complex queries into multiple sub-questions and accurately searches for relevant information.
- Dynamic Graph Construction: Simulates the human thought process of solving problems based on a directed acyclic graph (DAG).
- Parallel Information Retrieval: Based on a multi-agent architecture, it enables parallel search for multiple sub-questions.
- Hierarchical Retrieval Strategy: A coarse-to-fine retrieval strategy that improves the efficiency of information retrieval.
- Context management: Ensures coherence and completeness in the process of information retrieval and integration.
Official website address of Shusheng LLM
- Official website address：https://intern-ai.org.cn/
Application scenarios of Shusheng LLM
- Education and academic research: Assists teaching, automatically generates exam questions and answers, and supports literature reviews and data analysis in academic research.
- Financial sector: Enhances the effectiveness of financial text analysis, risk assessment, and compliance checks.
- Meteorological and ocean forecasting: Provides accurate nowcasting precipitation, medium-term weather, and interannual ocean predictions, supporting meteorological disaster warning and scientific research.
- Aerospace design: Generates and edits supercritical airfoils, optimizes engineering design, and improves design efficiency.
- Urban planning and cultural heritage protection: Creates large-scale real-scene 3D models of cities, supporting urban planning and digital protection of cultural heritage.
