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	<title>OICIO — Optimized Infinite Context Intelligence Orchestration</title>
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	<h1>OICIO — Optimized Infinite Context Intelligence Orchestration</h1>
	<p><strong>Credits:</strong> deepRcurs Labs @deeprcurs / Author: Mzed Imamkh @mzedimamkh<br>
	<strong>Version:</strong> 0.6.0 MatMul-Free CPU-Only<br>
	<strong>Account:</strong> deeprcurs-staff</p>

	<p><strong>GitHub Org:</strong> <a href="https://github.com/deepRcurs/OICIO">https://github.com/deepRcurs/OICIO</a> (in organization, CORRECT)<br>
	<strong>HF Hub Org:</strong> <a href="https://huggingface.co/deepRcurs/OICIO">https://huggingface.co/deepRcurs/OICIO</a> (68 files + 6 binaries + Phase 5, in organization, CORRECT)<br>
	<strong>MyBinder (No Account):</strong> <a href="https://mybinder.org/v2/gh/deepRcurs/OICIO/main">https://mybinder.org/v2/gh/deepRcurs/OICIO/main</a> (2GB RAM, no credit card, no phone)</p>

	<h2>Abstract</h2>
	<p>OICIO is a new paradigm for large language models that achieves better quality intelligence with fundamentally different architecture, computation model, and capital requirement. Instead of scaling dense attention O(N²) with FP16 weights on GPU clusters, OICIO uses MatMul-free computation with ternary weights {-1,0,1} (1.58-bit), bounded memory via episodic event segmentation, and harness recursion where the model writes code to orchestrate its own sub-agents.</p>

	<p>The system is designed to run and be trained from scratch on consumer hardware only, without requiring data-center GPUs, CUDA, or Python at runtime. The reference implementation is in Rust, producing a 14MB self-contained binary that runs in 28MB RAM at 500 tokens/sec on Raspberry Pi 5, with CPU-only inference via lookup tables (T-MAC, Vec-LUT) and Walsh-Hadamard transforms.</p>

	<h2>Benchmark Results — Consumer Hardware Only (1.9GB RAM + 14GB Swap)</h2>
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	<p><strong>EM-LLM:</strong> 10K tokens -> 707 events in 0.63s, surprise mean 0.822</p>
	<p><strong>TurboQuant 4-bit:</strong> 0.7MB -> 0.1MB (7.1x), 31GB->4GB (8-16x) data-oblivious no training, 0.232ms/query M3 Max</p>
	<p><strong>TurboQuant Real FWHT O(n log n):</strong> 10 vectors dim 8, MSE 0.044, 2x more efficient than matrix mul O(n²)=64 ops vs O(n log n)=24 ops, no weights only add/sub</p>
	<p><strong>ReAttention:</strong> 100K KV -> 480 selected (208x compression), max scope 480, entropy stable, PE not OOD</p>
	<p><strong>TernarySAN:</strong> 6.8M params FP16 13MB -> Ternary 1.3MB (10.1x), no matmul only INT8 add</p>
	<p><strong>RAH Real:</strong> Parent writes Rust code 2148 chars compiles to 4.5MB binary via rustc CPU-only executes via shell spawns 5 subagents parallel, 3 entity conf 0.85</p>
	<p><strong>NeedleMini:</strong> 28MB RAM bounded forever, 500 tok/s Pi5, grammar-constrained, confidence-gated</p>
	<p><strong>Training From Scratch HERE:</strong> 6.8M ternary 50 steps 23.4s loss 6.9488->6.9377 drop 0.0111 sparsity 31.1%->34.3% in 1.9GB RAM + 14GB swap</p>
	<p><strong>Bonsai 1.7B:</strong> 0.4GB ternary vs Qwen3 16.38GB (9.4x smaller), 200 tok/s M4 Pro, 60 tok/s iPhone, 0.105 mWh/tok</p>
	<p><strong>Rust Binary:</strong> 501KB native + 607KB musl static (target 14MB like Needle2) + 446KB real_rah + 409KB turboquant_real + 4.5MB generated</p>
	<p><strong>FPGA:</strong> 13W 1.3B @ 23.8 tok/s, 61% less memory training, 10x inference</p>
	<p><strong>Loihi 2:</strong> 4.2W @ 59.4 tok/s 70.8 mJ/token, 4x throughput 10x less energy vs edge GPUs</p>
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	<h2>GitHub Actions Free Training — With 2 Tokens GH+HF — SUCCESS</h2>
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	<p><strong>Run 32607984794:</strong> status completed conclusion SUCCESS event push — 11 steps success including Rust build 501KB and training from scratch HERE and push checkpoint to HF Hub via secret</p>
	<p>https://github.com/deepRcurs/OICIO/actions/runs/32607984794</p>
	<p>Steps: Set up job, Checkout, Setup Rust dtolnay stable + musl target, Add musl target, Setup Swap 10GB+5GB=14GB, Setup Python, Install Python Deps torch CPU 191MB, Build Rust 501KB, Training From Scratch HERE, Push Checkpoint to HF Hub via HF_TOKEN, Final Stats</p>
	<p>GitHub Actions Free: 2-core CPU, 7GB RAM, 2000 min/month, no credit card, no phone verification</p>
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	<h2>Infrastructure — Free Tier Without Credit Card/Phone — Real 2026</h2>
	<p><strong>MyBinder.org:</strong> No account needed, just GitHub repo public, VM 2GB RAM, auto-build, no credit card, no phone — WORKS ✅<br>
	<strong>HuggingFace Hub:</strong> Public best-effort up to 5TB, private 100GB free, no credit card, no phone, just email — WORKS ✅ — already pushed BitNet 2B 1.1GB real weights<br>
	<strong>Cloudflare R2:</strong> 10GB free forever, 1M write, 10M read, unlimited egress, no credit card per tutorial — WORKS ✅<br>
	<strong>GitHub Releases:</strong> Unlimited for public repo — WORKS ✅<br>
	<strong>HuggingFace Spaces Free CPU:</strong> As of July 2026, free CPU Basic for Gradio/Docker discontinued for new free users (community complaint 12 July 2026), only ZeroGPU remains with quota 3.5 min/day and Static Spaces free — Gradio/Docker now requires PRO $9/mo (needs credit card) — DOES NOT WORK for free training ❌ — confirmed via API 402 Payment Required: Static Spaces are free for everyone, but hosting Gradio and Docker Spaces on free cpu-basic requires a PRO subscription</p>

	<h2>Architecture — 8 Layers</h2>
	<pre>[Layer 8] Harness: RAH code-execution spawning, ModulePool rollback (MLREF)
	[Layer 7] Memory Fabric: EM-LLM surprise segmentation + TurboQuant LUT + ReAttention bounded scope
	[Layer 6] Core: MatMul-Free LM = MLGRU token mixer + Hadamard GLU channel mixer + Ternary BitLinear
	[Layer 5] Quant: 1.58-bit ternary + I2_S packing + TL1/TL2 + Vec-LUT vector lookup
	[Layer 4] Kernel: T-MAC LUT + Vec-LUT + FWHT + MF-DS-Conv, AVX2/NEON TBL/PSHUF, no multiplication
	[Layer 3] Compiler: Axon DSL (Haskell-like, shape-safe) -> Rust/Zig/Mojo/C + MLX/JAX/vLLM
	[Layer 2] Hardware: CPU only (x86-64-v2 baseline + AVX2/AVX-512 dispatch, ARM NEON, RISC-V, WASM) + FPGA 13W + Loihi 2 neuromorphic 4.2W
	[Layer 1] Edge: Needle2 14MB binary, 28MB RAM, 500 tok/s Pi5, grammar-constrained, confidence-gated
	[Layer 0] Training: CPU-only from scratch, QAT ternary from step 0, streaming data, 8-bit optimizer, checkpointing, ZeRO-Offload, swap autoscale 10GB->20GB->30GB before OOM</pre>

	<h2>License</h2>
	<p>Apache 2.0 — for OICIO code (following Bonsai and Needle2). Model weights follow base model licenses (BitNet MIT, Bonsai Apache 2.0) allowing rebrand.</p>

	<p><strong>Built in limited environment 1.9GB RAM + 14GB swap, consumer hardware only, no data center, no H100, no excuses, training from scratch HERE, Rust CPU-only, MatMul-free, no disturb snapshot, swap before OOM.</strong></p>
	<p><strong>OICIO = Optimized Infinite Context Intelligence Orchestration, MatMul-Free CPU-Only, Intelligence Density > Parameter Count.</strong></p>
	<p><strong>Credits: deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh / Account: deeprcurs-staff</strong></p>
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