---
license: apache-2.0
datasets:
- Allanatrix/Scientific_Research_Tokenized
language:
- en
base_model:
- Allanatrix/NexaSci
pipeline_tag: text-generation
tags:
- Science
- Hypothesis
- Methodology
---

# NexaSci Family of Models

## Welcome to the NexaSci Repository!

Get ready to supercharge your scientific research with the **Nexasci 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 NexaSci family includes the baseline **NexaSci**, the reasoning-enhanced **NEXASci-1-CoT**, and the long-context powerhouse **NEXA-1-Max**. 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 NexaSci 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. NexaSci-1-Mini (Still working on this Indefinite timeline)
- **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 ~2B tokens from arXiv, PubMed, and other scientific corpora.
  - Fine-tuned with QLoRA on 500k 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. NEXASci-1-COT (Coming Soon)
- **Parameters**: 756 million to 1.1 Billion 
- **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 ~2B tokens
  - 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. NEXASci-1-Max (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 ~2B tokens, including a Long-Context Corpus 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., 120K-token preprints).
  - Generate hypotheses from extended contexts (e.g., patent methods).
  - Support multi-query tasks requiring deep document understanding.

### Future Models (Planned)
- **NEXASci-1-Scout**: A lightweight version (~50M parameters) optimized for distilling and curating datasets and maaking the corpa for the model family
- **NEXASci-1-Super**: A larger-scale model (~10B parameters) for advanced scientific tasks, using ~1B tokens. Planned for high-performance computing clusters.
- **NEXASci-1-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 NexaSci 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** (1-2B tokens): Domain-specific data from arXiv (physics), PubMed/BioRxiv (biology), and Materials Project/ChemRxiv (materials science).
- **Instruction Fine-Tune Dataset** (500K tokens): 5k high-quality instruction-style samples for hypothesis and method generation.

**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**:
For all models ~2B tokens 

**Hardware**:
Currently limited here still looking and hunting

**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/nexasci-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] Hypothesise a new superconductor."

Opens a web interface to interact with the model.

# Performance Metrics

Extreme Specialisation: Modular experts improve response fidelity and interpretability.
Distributed Training: Full hardware saturation stabilises runtimes and reduces crashes.
Generalisability: Robust across physics, biology, and materials science tasks.
Optimiser Efficiency: AzureSky Optimiser 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 summarisation. 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 the LICENSE file for details).

Have questions or ideas? Open an issue on GitHub or join the discussion on Hugging Face. Happy researching!