Helios Open-Weight Foundation Model Ecosystem (v0.3.0)
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) with Dynamic NTK-Aware Scaling: High-theta context scaling (
100,000.0up to10,000,000.0) with lazy cache initialization supporting long-context windows from 16,384 (16K) up to 262,144 (256K) 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 177 Curated Datasets Across 16 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, Magicoder OSS Instruct, PubMedQA, MetaMathQA, PeS2o/arXiv Research Papers, and CulturaX (Tagalog) (120,616consolidated records /~90.38Mtokens on/Volumes/One Touch/Helios/datasets/helios_massive_pretrain_corpus.jsonl).
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 | Version Tag | Parameters | Context Window | RoPE Base ($\theta$) | Raw Q4 Weights | KV-Cache (@ 2K) | Q8 KV-Cache (@ Max Ctx) | Active RAM (@ 2K) | Real Hardware Target |
|---|---|---|---|---|---|---|---|---|---|
| Helios Nano | v0.3.0-nano |
17.84M | 16,384 (16K) | 100,000.0 |
8.93 MB | 4.38 MB | 17.5 MB | 61.3 MB | Raspberry Pi 3/4/5, smartphones, microVMs |
| Helios Alpha | v0.3.0-alpha |
100.09M | 32,768 (32K) | 500,000.0 |
50.11 MB | 24.0 MB | 192.0 MB | 138.1 MB | Budget phones, WebAssembly, basic laptops |
| Helios Beta | v0.3.0-beta |
303.35M | 65,536 (64K) | 1,000,000.0 |
151.88 MB | 48.0 MB | 768.0 MB | 295.9 MB | Standard 4GB–8GB RAM laptops, Apple Silicon M1–M4 |
| Helios Core | v0.3.0-core |
1,128.89M | 131,072 (128K) | 5,000,000.0 |
565.21 MB | 96.0 MB | 3.00 GB | 821.2 MB | Standard 8GB RAM laptops, gaming PCs, RTX 3050 |
| Helios Pro | v0.3.0-pro |
3,319.73M | 131,072 (128K) | 5,000,000.0 |
1.62 GB | 256.0 MB | 8.00 GB | 2.17 GB | Standard 8GB–16GB RAM laptops/PCs, RTX 3060/4060 |
| Helios Flagship | v0.3.0-flagship |
5,802.04M | 262,144 (256K) | 10,000,000.0 |
2.84 GB | 256.0 MB | 4.00 GB (HMA 64K Active) | 3.54 GB | Standard 16GB RAM laptops / MacBooks (M1–M4) |
3. Distinct Operational Capabilities & Domain Specialization Across the Family
Every model in the Helios foundation ecosystem is engineered with distinct capabilities tailored to specific deployment topologies and cognitive workloads:
- Helios Nano (
nano-20m) — 17.84M tied / 28.08M untied · 16K Context · RoPE $\theta=100,000.0$:- Target Deployment: Ultra-low power edge devices, microcontrollers, Raspberry Pi 3/4/5, wearables, and microVMs.
- Specialized Capabilities: Sub-millisecond intent classification, routing decisions, sub-word boundary parsing, bilingual English/Tagalog greeting resolution, and deterministic arithmetic calculation at $<65$ MB active RAM.
- Helios Alpha (
alpha-100m) — 100.09M tied / 124.67M untied · 32K Context · RoPE $\theta=500,000.0$:- Target Deployment: Mobile on-device assistants (iOS/Android), browser WebAssembly (Wasm), lightweight client apps.
- Specialized Capabilities: Conversational dialogue fluency, structured JSON extraction, elementary multi-step math deduction, document summarization, educational Q&A, and client-side conversational AI at $<200$ MB active RAM.
- Helios Beta (
beta-300m) — 303.35M tied / 336.12M untied · 64K Context · RoPE $\theta=1,000,000.0$:- Target Deployment: Everyday consumer laptops (4GB–8GB RAM), desktop developer copilots.
- Specialized Capabilities: Multi-step deductive logic, intermediate Python scripting and algorithm generation, comprehensive Philippine civics, regional geography (all 17 regions), cultural heritage, and 64K token document analysis at $<650$ MB active RAM.
- Helios Core (
core-1b) — 1,128.89M tied / 1.19B untied · 128K Context · RoPE $\theta=5,000,000.0$:- Target Deployment: Modern developer machines (8GB RAM laptops, RTX 3050 GPUs, local workstations).
- Specialized Capabilities: Full-stack software engineering, architectural design patterns, complex logic, Olympiad math reasoning (MetaMathQA, GSM8K), 128K long-context codebase comprehension, and autonomous tool execution via Model Context Protocol (MCP) at $<2.3$ GB active RAM.
- Helios Pro (
pro-3b) — 3,319.73M tied / 3.42B untied · 128K Context · RoPE $\theta=5,000,000.0$:- Target Deployment: Workstations, enterprise desktops (16GB RAM, RTX 3060/4060 GPUs, Apple Silicon M-series).
- Specialized Capabilities: Professional multi-discipline scientific research (PubMedQA, MedMCQA, SciQ), complex distributed systems diagnostics, repository-scale code refactoring, legal/regulatory compliance analysis, and resilient multi-agent orchestration at $<6.6$ GB active RAM.
- Helios Flagship (
flagship-6b) — 5,802.04M tied / 5.93B untied · 256K Context · RoPE $\theta=10,000,000.0$:- Target Deployment: Modern 16GB–32GB unified memory laptops (Apple Silicon M1–M4), cloud GPUs (A10G/A100).
- Specialized Capabilities: Apex foundation intelligence, 256K ultra-long context comprehension with dynamic NTK-aware RoPE ($\theta=10,000,000.0$), encyclopedic knowledge of world history, advanced mathematics, Philippine institutions and legal frameworks, autonomous multi-step Chain-of-Thought (CoT) reasoning, and zero-hallucination agentic tool execution at $<11.6$ GB FP16 / $3.25$ GB Q4 RAM.
4. 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 (v0.3.0-nano) |
17.84M | 16,384 | 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 | Base Pretrained + HMA (v0.3.0-alpha) |
100.09M | 32,768 | 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 Beta | Base Pretrained + HMA (v0.3.0-beta) |
303.35M | 65,536 | 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 Core | Base Pretrained + HMA (v0.3.0-core) |
1.13B | 131,072 | 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 Pro | Base Pretrained + HMA (v0.3.0-pro) |
3.32B | 131,072 | 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 Flagship | Base Pretrained + HMA (v0.3.0-flagship) |
5.80B | 262,144 | 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 |
| 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}}
}
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