PIONEERING
AUTONOMOUS
INTELLIGENCE
Stellel is an applied artificial intelligence collective architecting next-generation reasoning models, decentralized autonomous agent swarms, and high-throughput homelab compute.
The Hermes Agent Paradigm
Architected for unbounded agentic execution, programmatic code compilation, and verified deductive reasoning chains.
Hermes 3 Neural Core
Frontier general-purpose open reasoning model trained on rigorous synthetic curricula. Features structured JSON-schema emission and native chain-of-thought recursive validation.
Autonomous Tool Sandbox
Direct integration with sandboxed kernel runtimes. Dynamic function registration, continuous self-correction on exit codes, and isolated deterministic container execution.
Neutral-Objective Alignment
Uncompromising commitment to unfiltered truth seeking and rigorous academic compliance. Neutral epistemic boundaries without artificial conversational neuroses.
Peer-Reviewed Publications
Hermes 3: Technical Report on Open-Weight Frontier Autonomous Reasoning
Comprehensive training dynamics, synthetic instruction mixture ablation, and agentic benchmark performance evaluations across Llama-3.1 fine-tuning stages.
Decentralized Reinforcement Learning via Alchemical Consensus
Novel peer-to-peer RLHF architectures facilitating distributed gradient synchronization across untrusted commodity hardware without central parameter servers.
Atropos: Dynamic Environment Optimization for Agent Swarms
Autonomous gym generation frameworks allowing swarms of Hermes agents to self-author and self-grade continuous domain-specific simulation challenges.
DisTrO: Distributed Training Over Internet Scale Heterogeneous Bandwidth
Breakthrough optimizer compression reducing all-reduce communication overhead by 850x, enabling worldwide home-GPU clusters to train multi-billion parameter foundations.
Decentralized High-Throughput Compute
Hermes Neural Terminal
[SYSTEM]: Sandboxed Tool Registry: [python_repl, arxiv_search, execute_shell, vector_retrieve]
1. Analyzing user objective: Verify dynamic scaling factor of tensor matrix FP8 dequantization across high-bandwidth memory.
2. Formulate verification code using CUDA / Triton kernel wrapper.
3. Dispatch function call to 'python_repl' tool to execute simulated latency evaluation under memory pressure.
4. Self-correct if output discrepancy exceeds epsilon 1e-4.
{
"tool": "python_repl",
"arguments": {
"code": "import torch\nt = torch.randn(4096, 4096, dtype=torch.bfloat16)\nfp8_t = t.to(torch.float8_e4m3fn)\nerr = (t - fp8_t.to(torch.bfloat16)).abs().mean()\nprint(f'DEQUANT_DELTA_EPSILON: {err.item():.6f}')"
}
}