We believe: the deeper we reach into the roots of mathematics and physics, the more openly we can share the results with the world.
Full-stack AI lab that turns deep research into CLI + open-source products covering LLM training · new architecture research · FPGA NPU synthesis.
Public 3: EulerForge (training) · EulerStack (architecture research) · EulerNPU (FPGA NPU).
No expensive cloud GPUs — turn the model you already have into an MoE.
EulerForge provides a standardized workflow for converting a dense model into an MoE-style trainable one, so experiments can be expressed in configuration rather than in glue code.
mixture_lora / moe_expert_lora| Injection | Dense LoRA · Mixture LoRA · MoE Expert LoRA · Native MoE Expert LoRA |
|---|---|
| Training | SFT · DPO · ORPO · RM · PPO |
| Backbones | Qwen2/3 · Llama 2/3 · Gemma 3 · Gemma 4 (dense+MoE) · Mixtral |
| Quantized training | nf4 / int4 / int8 (bitsandbytes) |
| Extras | HF Export · Optuna grid/bayesian search · integrated benchmark · 5-language CLI |
24 KO/EN tutorials + full CLI reference included
YAML-declarative LLM architecture language. Beyond an assembler, it's a research framework where cortical column · neural differentiation · tissue blocks can be ablated without code changes — letting you investigate hypotheses such as early emergence of reasoning · faster abstraction · independent development of world-knowledge vs reasoning/abstraction at the architecture level, and shifting frontier-LLM research from capital scale to design.
Organized by the v1 "industrial ordering principle", from validated industrial standards → recent hybrid/MoE → v1 experimental primitives: 24 llm_ (5 sizes × 4–5 variants, MLA included) + 33 arch_ (beginner 2 · intermediate 3 · advanced 5 · expert 23, of which 9 are *_mini). You can experiment with Phase B primitives — MLA, MoD, Titans, Dual-Stream — at arch-scale.
| Mixers | Attention · Mamba · RetNet · Hyena |
|---|---|
| FFN | MLP · Gated MLP (SwiGLU) · MoE (top-k routing) |
| Skill-level walkthrough | beginner (GPT-2/Llama) · intermediate (Mistral/Gemma2/Qwen) · advanced (Jamba/Samba/RetNet) · expert (MoE × mixer × depth/receptive-field 3D design space) |
| Compile target | HuggingFace model directory or JSON runtime config |
Three-stage validation (schema → cross-field compatibility → realism heuristics) catches design errors before compilation. All CLI messages are translated into 5 languages (ko/en/zh/ja/es). v0.1.5 adds μP scaling, differentiation auxiliary objectives, and the tissue organ declaration as backward-compatible spec extensions (existing YAML keeps working unchanged).
Reasoning ability emerges from less data. We directly test the hypothesis that structure becomes a function of data efficiency.
Abstraction and reasoning don't compete in the same layer. Inspired by cortical hierarchy, the two capabilities grow independently.
If world knowledge and reasoning/abstraction can be grown separately, the center of LLM research shifts from capital to design.
Write a YAML spec and inference runs on a real FPGA chip. Same flow all the way to ASIC synthesis.
Learn MoreDS-CNN + GRU INT8 full-graph NPU IP. 11/11 accuracy · 8.07× speedup vs CPU (3.17 ms/inference).
Hybrid configuration that offloads only the FFN block to the NPU. CPU↔NPU text 5/5 bit-identical — partial LLM inference demonstrated on a Zynq-7000-class FPGA.
spec → compile → bitstream → real FPGA — three NPUs validated end-to-end in one flow.
A data, agent, and robot-learning product family that runs on top of the three products available today (Forge · Stack · NPU) is joining soon.
A data processing system that bridges the gap between raw datasets and production training. Local-LLM-based profiling, auto-generated domain filters, PPL/MinHash metrics, 1-GPU LoRA/MoE scale-down validation, GPU and cloud cost estimation, and MLOps integration.
A local-first agent built on an 8-state machine with HITL approval. Pattern/Graph orchestrators, RAG, long-term memory (SQLite), MCP integration, 30+ CLI commands, and a self-repairing coding agent (code.dev_loop.v2) on top of deterministic shell gate nodes.
An RL→IL pipeline that combines imitation learning (IL) with FastTD3 reinforcement learning (RL). First two public domains = car (EulerDrive, CARLA-verified) and humanoid (EulerWalk, RL→IL loco-manip). An 8-domain unified schema and Domain Plugin extensibility.
A full-stack AI lab researching and disseminating core AI technologies rooted in mathematics, physics, and humanities.
The name Eulerwa joins the mathematician Leonhard Euler with the Korean word "와 (wa)", meaning "with". The two halves of the name map directly onto the two halves of our motto.
Deep Roots — like Euler. Not chasing trends, but going to the foundations of mathematics and physics. Every Eulerwa product stands on that base.
Open Horizons — like 'wa (with)'. As Euler freely shared knowledge throughout his life, we keep our results open-source so everyone starts from equal opportunity. And the horizon those technologies head toward keeps widening.
We are building inference silicon with EulerNPU, intelligence structure with EulerStack and EulerForge, physical-behavior learning with EulerAtlas, and autonomous orchestration with EulerAgent. Each is an independent product, yet all point in a single direction — an intelligence that can understand and care for the physical world on its own.
The first destination is the ocean. Paired with power-supply barges, an autonomous composite that covers the surface, the underwater, and the air at the same time — collecting marine debris, mapping the spatiotemporal distribution of fish and generating fishing plans, monitoring navigation safety, and accumulating marine-ecology data. Surface vessels, submersibles, and aerial vehicles operating as one intelligent system, observing and caring for the ocean in three dimensions — this is the first physical application Eulerwa is preparing.
On the same technical base, our view extends further. Monitoring and protecting the near-Earth orbital environment is essentially the same problem as today's marine ecology monitoring. Voyaging deeper into space will demand a different level of autonomy and propulsion, but not giving up that direction is Eulerwa's long-term plan. The deeper the roots, the further the horizon.
Access specialized technical publications and the open-source ecosystem.
In-depth research books on quantum computing and AI architectures.
Community tools for data processing and model orchestration.
Technology exists to serve humanity
We oppose the military automation of artificial intelligence. Technology must be used to protect life, not to take it — no technological achievement can take precedence over human safety.
Artificial intelligence must serve as a tool that elevates human dignity and strengthens democratic values. We oppose any use of AI that suppresses individual freedom or undermines democratic principles.
All software, models, and services released by Eulerwa may not be used for purposes that violate these principles. We take our responsibility for how our technology is used seriously.
Bold: currently public · Faded: in preparation
Deep in the roots of mathematics and physics. Open in sharing the results with the world.
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