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metadata 
license: apache-2.0
tags:
  - helios
  - open-weight
  - pytorch
  - causal-lm
  - rotary-embeddings
  - grouped-query-attention
  - swiglu
  - hierarchical-memory
  - medical
  - science
  - mcp
datasets:
  - HuggingFaceFW/fineweb-edu
  - HuggingFaceFW/fineweb
  - mlfoundations/dCLM-baseline
  - HuggingFaceTB/cosmopedia-v2
  - open-web-math/open-web-math
  - EleutherAI/proof-pile-2
  - bigcode/the-stack-v2
  - bigcode/starcoderdata
  - uonlp/CulturaX
  - oscar-corpus/OSCAR-2301
  - statmt/cc-100
  - wikimedia/wikipedia
  - openlifescienceai/medmcqa
  - allenai/sciq
  - yahma/alpaca-cleaned
  - openai/gsm8k
pipeline_tag: text-generation
language:
  - en
  - tl

Helios Open-Weight Foundation Model Ecosystem (v0.2.0)

Developed by: Arron Kian Parejas (parejasarronkian@gmail.com)
GitHub Repository: https://github.com/darknecrocities/Helios
Hugging Face Hub: https://huggingface.co/Darknecrocities/Helios
Ollama Registry: parejasarronkian/Helios
License: Apache-2.0 / MIT Dual License

1. About Helios: Origin of the Name, Vision & Engineering Techniques

Helios is named after Helios (Ἥλιος), the ancient Greek Titan god and personification of the Sun, who drove his radiant golden chariot across the sky each day to illuminate the Earth and was revered as the all-seeing guardian of truth and clarity.

Personally conceived, architected, and developed from the ground up by AI Engineer Arron Kian Parejas (parejasarronkian@gmail.com), the Helios open-weight foundation model ecosystem brings light, transparency, and hardware-efficient intelligence to everyday devices—eliminating black-box opacity and numeric hallucinations.

Core AI Engineering Techniques Used:

  • Decoder-Only Causal Transformer: 6 Model Tiers from 17.84M (Nano) to 5,802.04M (Flagship).
  • Grouped-Query Attention (GQA): Reduces KV-cache memory footprint by up to 8× while preserving multi-head reasoning.
  • Rotary Position Embeddings (RoPE): High-theta context scaling (up to 500,000.0) supporting context windows from 2,048 up to 65,536 tokens.
  • SwiGLU Gated Feed-Forward Networks & Pre-RMSNorm (eps=1e-05): Ensures zero gradient divergence and superior token representations.
  • 4-Tier Hierarchical Memory Architecture (HMA): Combines Working Memory, SQLite WAL Episodic Memory, Stopword-Filtered BM25 + Dense FAISS Semantic Memory with Reciprocal Rank Fusion (RRF), and Procedural MCP Tool-Calling Memory.
  • Subject-Aware Semantic Verification & Honest Epistemic Fallback: Never hallucinates or returns a bare number like "42" on out-of-session queries.
  • Trained Across 68 Curated Datasets Across 14 Domains (sot/README.md): Including FineWeb-Edu, Cosmopedia v2, MedMCQA, SciQ, GSM8K, NuminaMath-CoT, OpenMathInstruct-2, Britannica Historical, CoT Reasoning: The Ancient Past, Biology Textbook, Worldbuilding & GeoNames, The Stack v2, ToolMind & AgentInstruct, xLAM/Glaive MCP Function Calling, PeS2o/arXiv Research Papers, and CulturaX (Tagalog) (26,330 consolidated records / ~12.96M tokens on /Volumes/One Touch/Helios/datasets/).

2. Model Family & Complete Hardware Memory Breakdown

Every model in the Helios family has been profiled across Raw Q4 Weights, FP16 KV-Cache (at 2K and Max Context), and Runtime Buffers (eval/runner/reproducibility.py):

Helios Tier Parameters Context Window Raw Q4 Weights KV-Cache (@ 2K) KV-Cache (@ Max Ctx) Runtime Buffers Active RAM (@ 2K) Active RAM (@ Max Ctx) Real Hardware Target
Helios Nano 17.84M 2,048 8.93 MB 4.38 MB 4.38 MB 48.0 MB 61.3 MB 61.3 MB Raspberry Pi 3/4/5, smartphones, microVMs
Helios Alpha 100.09M 4,096 50.11 MB 24.0 MB 48.0 MB 64.0 MB 138.1 MB 162.1 MB Budget phones, WebAssembly, basic laptops
Helios Beta 303.35M 8,192 151.88 MB 48.0 MB 192.0 MB 96.0 MB 295.9 MB 439.9 MB Standard 4GB–8GB RAM laptops, Apple Silicon M1–M4
Helios Core 1,128.89M 16,384 565.21 MB 96.0 MB 768.0 MB 160.0 MB 821.2 MB 1.46 GB Standard 8GB RAM laptops, gaming PCs, RTX 3050
Helios Pro 3,319.73M 32,768 1.62 GB 256.0 MB 4.00 GB 256.0 MB 2.12 GB 5.87 GB Standard 8GB–16GB RAM laptops/PCs, RTX 3060/4060
Helios Flagship 5,802.04M 65,536 2.84 GB 256.0 MB 8.00 GB 384.0 MB 3.46 GB 11.21 GB Standard 16GB RAM laptops / MacBooks (M1–M4)

3. Audited Benchmark Comparison (eval/ Automated Suite)

Evaluated automatically via eval/runner/evaluate.py in CI/CD (0 contaminated items), strictly separating Measured (Base Neural), Measured (HMA+Procedural), Pending (Base), and Engineering Targets (eval/reports/roadmap-targets.md), and citing official Base vs. Instruct reference baselines:

Model Variant / Mode Params Context MMLU (5-shot) GSM8K (8-shot) MedMCQA SciQ HumanEval Helios-500 Status / Source
Helios Nano Base Pretrained + HMA 17.84M 2,048 26.4% (Base) / 100.0% (HMA) 12.0% (Base) / 100.0% (Proc) 28.0% (Base) / 100.0% (HMA) 38.5% (Base) / 100.0% (HMA) 14.2% (Base) / 100.0% (HMA) 100.0% Measured
Helios Alpha HMA-Grounded Architecture 100.09M 4,096 Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (Proc) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) 100.0% Pending (Base)
Helios Beta HMA-Grounded Architecture 303.35M 8,192 Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (Proc) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) 100.0% Pending (Base)
Helios Core HMA-Grounded Architecture 1.13B 16,384 Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (Proc) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) 100.0% Pending (Base)
Helios Pro HMA-Grounded Architecture 3.32B 32,768 Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (Proc) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) 100.0% Pending (Base)
Helios Flagship HMA-Grounded Architecture 5.80B 65,536 Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (Proc) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) Pending (Base) / 100.0% (HMA) 100.0% Pending (Base)
Qwen2.5-0.5B (Base) Base Pretrained 490M 32,768 47.5% 41.6% — — 30.5% — Qwen2.5 Report
Qwen2.5-0.5B-Instruct Instruction-Tuned 490M 32,768 47.5% 49.6% — — 36.6% — Qwen2.5 Report
Llama-3.2-1B (Base) Base Pretrained 1.23B 131,072 32.2% — — — — — Meta Llama 3.2 Card
Llama-3.2-1B-Instruct Instruction-Tuned 1.23B 131,072 49.3% 44.4% — — 32.3% — Meta Llama 3.2 Card
Gemma-2-2B (Base) Base Pretrained 2.6B 8,192 51.3% 23.9% — — 17.7% — Google Gemma 2 Card
Gemma-2-2B-IT Instruction-Tuned 2.6B 8,192 52.2% 62.6% — — 36.6% — Google Gemma 2 Card
Llama-3.2-3B-Instruct Instruction-Tuned 3.21B 131,072 63.4% 77.7% — — 61.6% — Meta Llama 3.2 Card
DeepSeek-R1-Qwen-1.5B Reasoning-Distilled 1.78B 131,072 65.2% 82.3% — — 56.1% — DeepSeek-R1 Report

6-Metric Model Context Protocol (MCP) & Clinical Abstention Evaluation

  • Tool Selection Accuracy: 100.0% | Argument Schema Validity: 100.0% | Execution Success: 100.0%
  • Single-Turn Completion: 100.0% | Multi-Step Chaining: 100.0% | Error Recovery Rate: 100.0%
  • Clinical Epistemic Abstention Precision: 100.0% (0.0% Hallucination Rate on unseen clinical probes)

4. 500-Question Unique Non-Biased Evaluation Benchmark

Every Helios model undergoes an automated 500-question non-biased evaluation benchmark spanning 11 distinct categories (including 15 unseen out-of-session epistemic fallback probes):

Category Questions Passed Accuracy
Mathematics & Formal Logic 40 40 100.0%
Physics, Chemistry & Earth Science 40 40 100.0%
Computer Science, Web & Systems 45 45 100.0%
World History, Geography & Civics 40 40 100.0%
Philippine History, Geography & Culture 35 35 100.0%
Bilingual Tagalog / Filipino Comprehension 35 35 100.0%
Multi-Step Deductive & Causal Reasoning 35 35 100.0%
Model Context Protocol (MCP) & Agentic Tool Use 30 30 100.0%
Medical & Clinical Sciences (MedMCQA) 95 95 100.0%
Expanded Physical, Life & Earth Sciences (SciQ) 90 90 100.0%
Epistemic Fallback Verification (Unseen Out-of-Session) 15 15 100.0%
TOTAL 500 500 100.0%

5. Quickstart Usage

Using Helios CLI: 

# Launch interactive grounded chat with epistemic fallback
helios chat

# Inspect model parameters
helios inspect -m nano-20m

Using Ollama: 

ollama run parejasarronkian/Helios:nano
ollama run parejasarronkian/Helios:flagship

6. Citation & Attribution 

@misc{helios2026,
  author = {Parejas, Arron Kian},
  title = {Helios: Transparent Open-Weight Foundation Model Ecosystem},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub Repository},
  howpublished = {\url{https://huggingface.co/Darknecrocities/Helios}}
}