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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- Allanatrix/Scientific_Research_Tokenized
+language:
+- en
+base_model:
+- Allanatrix/NexaMOE_Mini
+pipeline_tag: text-generation
+tags:
+- Science
+- Hypothesis
+- Methodology
+---
+
+# NexaMOE Family of Models
+
+## Welcome to the NexaMOE Repository!
+
+Get ready to supercharge your scientific research with the **NexaMOE family of models**! This Hugging Face repository hosts a powerful suite of Mixture-of-Experts (MoE) models designed to generate hypotheses and methodologies across **physics**, **biology**, and **materials science**. Built with efficiency and scalability in mind, the NexaMOE family includes the baseline **NexaMOE**, the reasoning-enhanced **NEXA-CoT**, and the long-context powerhouse **NEXA-Ultramax**. Whether you’re a researcher tackling complex STEM problems, a data scientist exploring scientific ML, or a student learning about domain-specific AI, this repository is your go-to resource for cutting-edge scientific computation.
+
+## Model Overview
+
+The NexaMOE family is a 110 million to 2.2 billion parameter architecture that uses a **Semantic Router** to direct queries to domain-specific expert modules (Physics, Biology, Materials Science). It’s optimized for resource-constrained environments, leveraging advanced training strategies, hardware optimizations, and techniques like reinforcement learning and sparse attention. Below are the current and planned models:
+
+### 1. NexaMOE_Mini (Still working on this)
+- **Parameters**: ~110 million
+- **Purpose**: Generates hypotheses and methodological scaffolding for scientific tasks in physics, biology, and materials science.
+- **Architecture**:
+  - **Semantic Router**: BERT-based classifier routes queries to domain-specific experts.
+  - **Expert Modules**: T5-based submodules for Physics, Biology, and Materials Science.
+  - **Inference & Validation Pipeline**: Aggregates expert outputs and ensures consistency.
+  - **Knowledge Feedback Loop**: Refines routing using reinforcement learning.
+- **Training**:
+  - Pretrained on ~325M tokens from arXiv, PubMed, and other scientific corpora.
+  - Fine-tuned with QLoRA on 300k instruction-style samples.
+  - Uses AzureSky Optimizer (Stochastic Approximation + Adam hybrid).
+- **Use Cases**:
+  - Generate plausible hypotheses (e.g., new material properties).
+  - Suggest experimental methods (e.g., protein folding protocols).
+  - Summarize scientific texts with domain-specific insights.
+
+### 2. NEXA-CoT (Coming Soon)
+- **Parameters**: ~110 million
+- **Purpose**: Enhances step-by-step logical reasoning for complex STEM tasks, like physics problem-solving or interdisciplinary hypothesis generation.
+- **Architecture**:
+  - Adds a **Chain of Thought (CoT) Processor** with sparse attention (Longformer-style) for multi-step reasoning.
+  - Includes **Conditional Routing** to engage the CoT Processor based on a “reasoning_required” flag.
+  - Integrates with expert modules for structured, logical outputs.
+- **Training**:
+  - Trained in three stages: Easy (basic logic), Moderate (complex tasks), Hard (advanced reasoning).
+  - Uses ~425-500M tokens, including a Reasoning Curriculum Dataset (50-75M tokens) for CoT optimization.
+  - Employs AzureSky Optimizer with reinforcement learning fine-tuning.
+- **Use Cases**:
+  - Solve multi-step physics problems (e.g., astrophysics simulations).
+  - Generate detailed, logical methodologies (e.g., combining CFD and alloy modeling).
+  - Teach scientific reasoning in educational settings.
+
+### 3. NEXA-Ultramax (Coming soon)
+- **Parameters**: ~2.2 billion
+- **Purpose**: Processes large scientific documents (up to 20,000 tokens) with deep contextual understanding.
+- **Architecture**:
+  - Features a **Long Context Attention Layer** with two Flash Attention v2 layers for efficient long-sequence processing.
+  - Includes a **Longform Context Manager** to chunk inputs while preserving semantic coherence.
+  - Scales parameters using mixed precision training and gradient checkpointing.
+- **Training**:
+  - Trained on ~600-650M tokens, including a Long-Context Corpus (100-150M tokens) of full arXiv papers and NIH grants.
+  - Uses AzureSky Optimizer with mixed precision (FP16/BF16) and gradient checkpointing.
+- **Use Cases**:
+  - Summarize or analyze long scientific papers (e.g., 20K-token preprints).
+  - Generate hypotheses from extended contexts (e.g., patent methods).
+  - Support multi-query tasks requiring deep document understanding.
+
+### Future Models (Planned)
+- **NEXA-MOE-Mini**: A lightweight version (~50M parameters) optimized for edge devices, using ~325M tokens. Planned for low-resource environments.
+- **NEXA-MOE-Super**: A larger-scale model (~10B parameters) for advanced scientific tasks, using ~1B tokens. Planned for high-performance computing clusters.
+- **NEXA-MOE-MultiModal**: Integrates text, images, and graphs for scientific data analysis (e.g., protein structures, simulation plots). Planned for future research.
+
+## Dataset and Training Details
+
+The NexaMOE family is trained on a **tiered token strategy** to maximize efficiency and domain specificity, as outlined in the architecture document:
+
+- **Warm Start Corpus** (100M tokens): General language understanding from FineWeb-Edu, OpenWebMath, Wikipedia, and Aristo Science Questions.
+- **Scientific Pretraining Corpus** (200-300M tokens): Domain-specific data from arXiv (physics), PubMed/BioRxiv (biology), and Materials Project/ChemRxiv (materials science).
+- **Instruction Fine-Tune Dataset** (25-30M tokens): 300k high-quality instruction-style samples for hypothesis and method generation.
+- **Reasoning Curriculum Dataset** (50-75M tokens, CoT only): SciBench, OpenBookQA, and others for step-by-step reasoning.
+- **Long-Context Corpus** (100-150M tokens, UltraMAX only): Full arXiv papers, NIH grants, and USPTO patents for long-context alignment.
+
+**Token Efficiency Strategies**:
+- Entropy scoring to remove low-information samples.
+- Semantic tagging (e.g., [PHYS], [BIO], [MTH]) for domain routing.
+- Distillation using larger models (e.g., GPT-4) to summarize and structure data.
+- Routing and filtering to activate only relevant expert paths.
+
+**Total Token Budget**:
+- NexaMOE-Mini: ~325M tokens
+- NEXA-CoT: ~425-500M tokens
+- NEXA-Ultramax: ~600-650M tokens
+
+**Hardware**:
+- CPU: Intel i5 vPro 8th Gen (overclocked to 6.0 GHz) with 16 GB RAM.
+- GPUs: Dual NVIDIA T4 GPUs (cloud-hosted) at 90%+ capacity.
+- Performance: 47-50 petaflops with an optimized CPU-GPU pipeline.
+
+**Optimization Techniques**:
+- Sparse attention, mixed precision training, gradient checkpointing.
+- Hyperparameter tuning with Optuna, Just-in-Time (JIT) compilation, multi-threading.
+- AzureSky Optimizer for efficient convergence.
+
+
+# Download Models:
+
+Model weights are hosted on Hugging Face. Download them using the transformers library or directly from the repository’s model card.
+Example:huggingface-cli download your-username/nexamoe-base
+
+
+# Usage
+
+Load a Model:Use the transformers library to load NexaMOE models:
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your-username/nexamoe-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+
+Generate Hypotheses or Methods:Provide a prompt with optional domain tags:
+prompt = "[PHYS] Suggest a hypothesis for dark matter detection."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+Use NEXA-CoT for Reasoning:Enable the CoT Processor for step-by-step logic:
+prompt = "[BIO] [reasoning_required] Propose a method to predict protein folding."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=500)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+Process Long Documents with NEXA-Ultramax:Handle large inputs (up to 20,000 tokens):
+with open("arxiv_paper.txt", "r") as f:
+    document = f.read()
+prompt = f"[MAT] Summarize this document: {document}"
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=20000).to("cuda")
+outputs = model.generate(**inputs, max_length=1000)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+Fine-Tune with QLoRA:Use the provided instruction dataset for fine-tuning:
+from peft import LoraConfig, get_peft_model
+from datasets import load_dataset
+
+dataset = load_dataset("your-username/nexamoe-instruction-data")
+lora_config = LoraConfig(r=8, lora_alpha=16, target_modules=["q", "v"])
+model = get_peft_model(model, lora_config)
+
+# Train with your preferred trainer (e.g., Hugging Face Trainer)
+
+Run Inference via CLI or GUI:
+
+Command-Line:python inference.py --model your-username/nexamoe-base --prompt "[PHYS] Hypothesize a new superconductor."
+
+
+Gradio GUI:python app.py
+
+Opens a web interface to interact with the model.
+
+
+Model Weights and Datasets
+
+Models:
+your-username/nexamoe-base: Baseline NexaMOE (110M parameters).
+your-username/nexamoe-cot: NEXA-CoT (110M parameters).
+your-username/nexamoe-ultramax: NEXA-Ultramax (2.2B parameters).
+
+
+Datasets:
+your-username/nexamoe-instruction-data: 300k instruction-style samples for QLoRA fine-tuning.
+your-username/nexamoe-reasoning-data: Reasoning Curriculum Dataset for CoT training.
+your-username/nexamoe-long-context-data: Long-Context Corpus for UltraMAX training.
+
+
+# Requirements
+
+Hardware: NVIDIA GPU with 16-24GB VRAM (e.g., T4, A100) for training/inference. CPU fallback supported for preprocessing.
+Software: Python 3.10, PyTorch, Transformers, Accelerate, PEFT, Optuna, Gradio.
+
+# Performance Metrics
+
+Extreme Specialization: Modular experts improve response fidelity and interpretability.
+Distributed Training: Full hardware saturation stabilizes runtimes and reduces crashes.
+Generalizability: Robust across physics, biology, and materials science tasks.
+Optimizer Efficiency: AzureSky Optimizer enhances convergence speed and precision.
+
+See the architecture document for detailed loss curves and metrics.
+Similar Models
+Explore related models for inspiration:
+
+Grok (xAI): General-purpose conversational AI with scientific capabilities. Link
+LLaMA (Meta AI): Efficient research models for NLP tasks. Link
+SciBERT: BERT variant for scientific text processing. Link
+Galactica (Meta AI): Scientific language model for paper summarization. Link
+BioBERT: BERT variant for biomedical text. Link
+
+For the models, cite:
+
+Allanatrix. (2025). NexaMOE Family of Models. Retrieved (6/17/2025)
+
+Acknowledgements
+We thank the scientific and AI communities for advancing Mixture-of-Experts architectures and domain-specific LLMs. Special thanks to the authors of the datasets used (arXiv, PubMed, Materials Project) and the developers of tools like Transformers, PEFT, and Optuna.
+For more information, see: https://materialsproject.org/, https://arxiv.org/, https://pubmed.ncbi.nlm.nih.gov/
+License
+MIT License (see LICENSE file for details).
+
+Have questions or ideas? Open an issue on GitHub or join the discussion on Hugging Face. Happy researching!```