| --- |
| license: apache-2.0 |
| language: |
| - fa |
| - en |
| - multilingual |
| tags: |
| - aethermind |
| - decoder-only |
| - rope |
| - gqa |
| - swiglu |
| - moe |
| - safetensors |
| library_name: transformers |
| --- |
| |
| # Aether |
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| Aether-492B |
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| Aether-492B — A Large-Scale General Intelligence Foundation Model |
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| Aether-492B is a large-scale, general-purpose artificial intelligence model designed to provide a broad spectrum of advanced capabilities across reasoning, programming, mathematics, knowledge synthesis, multilingual communication, creative generation, analysis, instruction following, and agentic workflows. |
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| Aether-492B is designed around a simple objective: |
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| «Build one highly capable model that can understand complex problems, reason through them, create useful solutions, and communicate naturally across a wide range of tasks.» |
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| With a target scale of 492 billion parameters, Aether-492B is intended to operate as a powerful foundation model rather than a narrow task-specific system. |
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| --- |
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| 🧠 Core Intelligence |
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| Aether-492B is designed to combine multiple forms of intelligence within a single model. |
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| Advanced Reasoning |
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| Aether-492B is designed to work through difficult multi-step problems instead of relying only on simple pattern matching. |
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| Capabilities include: |
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| - Multi-step logical reasoning |
| - Structured problem solving |
| - Hypothesis generation |
| - Deductive reasoning |
| - Inductive reasoning |
| - Comparative reasoning |
| - Constraint-based reasoning |
| - Planning |
| - Error detection |
| - Self-checking |
| - Complex instruction interpretation |
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| The model is intended to break complicated problems into manageable components, analyze relationships between them, and produce coherent solutions. |
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| --- |
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| 💻 Advanced Programming |
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| Aether-492B is designed as a capable software-engineering assistant. |
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| It can be used for: |
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| - Code generation |
| - Code explanation |
| - Debugging |
| - Refactoring |
| - Code review |
| - Architecture design |
| - API design |
| - Algorithm development |
| - Database design |
| - Testing |
| - Documentation |
| - Performance optimization |
| - Security-oriented code review |
| - Frontend development |
| - Backend development |
| - Mobile development |
| - Game development |
| - Machine-learning development |
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| Supported programming workflows can include languages such as: |
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| Python · JavaScript · TypeScript · Java · Kotlin · C · C++ · C# · Rust · Go · Swift · PHP · SQL · HTML · CSS · Bash |
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| The model can also assist with larger multi-file software projects by reasoning about relationships between components rather than treating every code fragment independently. |
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| --- |
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| 🔬 Mathematics & Science |
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| Aether-492B is designed for mathematical and scientific reasoning across multiple levels of difficulty. |
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| Potential applications include: |
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| - Arithmetic |
| - Algebra |
| - Geometry |
| - Probability |
| - Statistics |
| - Calculus |
| - Discrete mathematics |
| - Linear algebra |
| - Optimization |
| - Mathematical proofs |
| - Physics |
| - Chemistry |
| - Computer science |
| - Scientific explanation |
| - Research assistance |
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| The model is designed to explain solutions step-by-step when requested rather than returning only a final result. |
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| --- |
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| 📚 Knowledge & Analysis |
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| Aether-492B can function as a general knowledge and analysis assistant. |
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| It can: |
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| - Summarize long documents |
| - Extract important information |
| - Compare concepts |
| - Explain difficult subjects |
| - Organize information |
| - Identify relationships |
| - Transform unstructured information into structured formats |
| - Generate reports |
| - Analyze arguments |
| - Create outlines |
| - Answer questions about provided context |
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| When connected to external retrieval systems, Aether-492B can also be used as the reasoning layer of a retrieval-augmented generation (RAG) system. |
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| 🌍 Multilingual Intelligence |
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| Aether-492B is intended to support multilingual interaction and cross-language reasoning. |
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| Potential applications include: |
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| - Translation |
| - Multilingual conversations |
| - Cross-language summarization |
| - Bilingual document analysis |
| - Language learning |
| - Multilingual programming assistance |
| - Localization |
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| The model can maintain the requested language and style throughout a conversation. |
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| --- |
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| ✍️ Creative Intelligence |
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| Aether-492B is not limited to technical tasks. |
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| It can assist with: |
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| - Fiction |
| - Stories |
| - Screenplays |
| - Dialogues |
| - Worldbuilding |
| - Character development |
| - Game narratives |
| - Concept development |
| - Brainstorming |
| - Marketing copy |
| - Articles |
| - Essays |
| - Technical writing |
| - Documentation |
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| The model is designed to adapt its writing style according to the requested audience, tone, format, and purpose. |
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| --- |
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| 🧩 Complex Instruction Following |
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| Aether-492B is designed to understand detailed instructions containing multiple constraints. |
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| For example, a single request can specify: |
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| - Output format |
| - Programming language |
| - Writing style |
| - Length |
| - Structure |
| - Required sections |
| - Forbidden elements |
| - Technical constraints |
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| The model attempts to satisfy these constraints simultaneously rather than focusing exclusively on the first instruction. |
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| --- |
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| 🤖 Agentic Workflows |
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| Aether-492B can serve as the reasoning engine behind AI agents. |
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| Possible agent architectures include: |
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| Aether-492B → Planner → Tool Selection → Execution → Observation → Verification → Final Response |
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| This enables applications such as: |
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| - Coding agents |
| - Research agents |
| - Data-analysis agents |
| - Automation assistants |
| - Developer copilots |
| - Document-processing systems |
| - Knowledge assistants |
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| When appropriate tools are available, the model can reason about which tool should be used and how its results should influence the next step. |
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| 🧠 Long-Context Understanding |
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| Aether-492B is intended for applications involving large amounts of contextual information. |
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| Potential use cases include: |
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| - Large documents |
| - Software repositories |
| - Technical specifications |
| - Research papers |
| - Conversation histories |
| - Documentation |
| - Contracts and structured text |
| - Large datasets represented as text |
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| The actual supported context length depends on the final architecture and configuration of the released model. |
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| 🗣️ Conversational Intelligence |
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| Aether-492B is designed for natural multi-turn conversations. |
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| It can maintain conversational context and adapt its responses based on: |
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| - Previous messages |
| - User instructions |
| - Conversation goals |
| - Requested tone |
| - Available context |
| - Output requirements |
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| The objective is to make interaction feel less like querying a database and more like collaborating with an intelligent assistant. |
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| --- |
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| 🎯 Model Philosophy |
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| Aether-492B follows several design principles: |
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| 1. Capability |
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| The model should be useful across many different domains. |
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| 2. Adaptability |
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| The model should adapt to different users, tasks, formats, and workflows. |
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| 3. Reasoning |
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| The model should be able to approach complex problems systematically. |
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| 4. Precision |
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| The model should distinguish between known information, uncertainty, assumptions, and conclusions. |
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| 5. Practicality |
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| The model should prioritize useful outputs that can actually be applied. |
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| 6. Generality |
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| The same foundation should support programming, research, education, creativity, analysis, and everyday assistance. |
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| --- |
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| 🏗️ Model Specifications |
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| Property| Aether-492B |
| Model Family| Aether |
| Model Name| Aether-492B |
| Target Parameters| 492B |
| Model Category| Large Language Model |
| Primary Modality| Text |
| Primary Purpose| General-purpose AI |
| Reasoning| Advanced |
| Programming| Advanced |
| Mathematics| Advanced |
| Multilingual| Yes |
| Creative Generation| Yes |
| Long Context| Architecture dependent |
| Tool Use| Integration dependent |
| Agentic Workflows| Supported by external orchestration |
| Deployment| Hardware/configuration dependent |
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| Important: Values such as parameter count, context length, architecture, benchmark scores, and hardware requirements should represent the actual released model rather than aspirational targets. |
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| --- |
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| 🚀 Intended Applications |
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| Aether-492B can be used as a foundation for: |
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| Personal AI |
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| Personal assistants, knowledge assistants, productivity systems, and conversational applications. |
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| Software Engineering |
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| Coding copilots, debugging systems, code-review tools, and autonomous development workflows. |
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| Research |
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| Research assistants, literature analysis, scientific reasoning, and information synthesis. |
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| Education |
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| Tutoring systems, educational assistants, personalized explanations, and learning tools. |
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| Business |
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| Document analysis, report generation, internal knowledge systems, automation, and customer-support assistants. |
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| Creative Industries |
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| Story generation, game development, worldbuilding, script writing, and creative brainstorming. |
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| AI Agents |
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| Planning systems, tool-using agents, autonomous workflows, and multi-agent architectures. |
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| --- |
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| 🔧 Fine-Tuning & Adaptation |
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| Aether-492B can serve as a foundation for specialized models. |
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| Possible adaptation techniques include: |
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| - Supervised fine-tuning |
| - Instruction tuning |
| - Preference optimization |
| - Domain adaptation |
| - Parameter-efficient fine-tuning |
| - LoRA |
| - QLoRA |
| - Adapter-based training |
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| Specialized versions could target areas such as: |
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| Aether-Code · Aether-Math · Aether-Research · Aether-Medical · Aether-Vision · Aether-Reasoning |
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| These names represent possible derivatives and should only be used for models that actually exist. |
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| --- |
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| 📊 Evaluation |
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| Aether-492B should be evaluated using standardized benchmarks covering multiple capabilities. |
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| Potential evaluation categories include: |
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| - General knowledge |
| - Mathematical reasoning |
| - Coding |
| - Logical reasoning |
| - Instruction following |
| - Multilingual understanding |
| - Long-context tasks |
| - Scientific reasoning |
| - Factuality |
| - Robustness |
| - Safety |
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| Benchmark numbers should only be published after reproducible evaluation using documented configurations. |
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| --- |
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| ⚠️ Limitations |
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| Despite its intended capabilities, Aether-492B may: |
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| - Generate incorrect information |
| - Produce hallucinations |
| - Misinterpret ambiguous instructions |
| - Make reasoning mistakes |
| - Generate incorrect code |
| - Produce unreliable conclusions when context is incomplete |
| - Reflect limitations or biases present in its training data |
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| Aether-492B should therefore not be treated as an infallible source of truth. |
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| For high-impact applications, outputs should be independently verified. |
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| --- |
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| 🔐 Safety |
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| Aether-492B should be deployed with appropriate safety controls depending on the application. |
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| Recommended production systems may include: |
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| - Input validation |
| - Output filtering |
| - Abuse monitoring |
| - Rate limiting |
| - Human review |
| - Tool permission boundaries |
| - Logging and auditing |
| - Prompt-injection defenses |
| - Data privacy controls |
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| The base model should not be assumed to provide complete application-level safety by itself. |
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| --- |
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| ⚙️ Hardware & Inference |
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| A 492B-parameter model represents a very large computational workload. |
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| Actual inference requirements depend on: |
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| - Precision |
| - Quantization |
| - Architecture |
| - Context length |
| - Batch size |
| - KV-cache configuration |
| - Parallelism strategy |
| - Hardware |
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| A full-precision deployment can require extremely large amounts of accelerator memory. Quantized variants can substantially reduce memory requirements, but still require serious compute resources. |
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| For this reason, practical deployment may involve: |
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| Tensor Parallelism + Pipeline Parallelism + Quantization + Distributed Inference |
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| rather than a single consumer GPU. |
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| --- |
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| 📦 Repository Contents |
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| A production release may contain: |
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| Aether-492B/ |
| ├── config.json |
| ├── generation_config.json |
| ├── tokenizer.json |
| ├── tokenizer_config.json |
| ├── special_tokens_map.json |
| ├── model.safetensors |
| ├── model-00001-of-XXXXX.safetensors |
| ├── model-00002-of-XXXXX.safetensors |
| ├── ... |
| ├── model.safetensors.index.json |
| ├── README.md |
| ├── LICENSE |
| └── NOTICE |
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| For a model of this scale, weights would normally be sharded into multiple files rather than stored as one enormous file. |
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| --- |
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| 🤗 Hugging Face |
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| Aether-492B is designed to be distributed as an open model repository suitable for research, experimentation, and downstream development. |
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| The repository can provide: |
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| - Model weights |
| - Configuration |
| - Tokenizer |
| - Model card |
| - Inference examples |
| - Quantized variants |
| - Evaluation results |
| - License information |
| - Training information |
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| --- |
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| 🌌 Aether |
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| The name Aether represents the central idea behind the project: |
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| A general intelligence foundation capable of moving between different domains without being restricted to a single task. |
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| Aether-492B aims to combine: |
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| Reasoning + Knowledge + Code + Mathematics + Creativity + Language + Planning + Tool Use |
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| into one general-purpose AI system. |
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| --- |
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| Status |
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| Development / Experimental |
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| Aether-492B is a large-scale AI research project. Technical specifications and performance claims should be updated as the model progresses through training, evaluation, optimization, and deployment. |
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| --- |
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| Aether-492B |
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| One model. Many domains. |
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| Reason deeply. Create freely. Build anything. |
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