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| # Deevo Systems & Co. | |
| <p align="center"> | |
| <strong>Frontier AI Architecture, Sparse Foundation Models & High-Throughput Compute</strong> | |
| </p> | |
| <p align="center"> | |
| <a href="https://deevo.co.in"><img src="https://img.shields.io/badge/Web-deevo.co.in-0A0A0A?style=flat-square" alt="Website"></a> | |
| <a href="https://chat.deevo.co.in"><img src="https://img.shields.io/badge/Platform-chat.deevo.co.in-0A0A0A?style=flat-square" alt="DWARK"></a> | |
| <a href="https://github.com/Deevo-Systems-Co"><img src="https://img.shields.io/badge/GitHub-Deevo--Systems--Co-181717?style=flat-square&logo=github" alt="GitHub"></a> | |
| <a href="https://www.linkedin.com/company/deevosystems"><img src="https://img.shields.io/badge/LinkedIn-deevosystems-0A66C2?style=flat-square&logo=linkedin" alt="LinkedIn"></a> | |
| <a href="https://www.wikidata.org/wiki/Q141544331"><img src="https://img.shields.io/badge/Wikidata-Q141544331-339966?style=flat-square&logo=wikidata" alt="Wikidata"></a> | |
| </p> | |
| --- | |
| ## Overview | |
| **Deevo Systems & Co.** is a computational engineering firm developing proprietary foundation models, custom GPU acceleration kernels, and deterministic enterprise workflow swarms. Our research focuses on sparse autoregressive architectures, bare-metal hardware synthesis, and ultra-long sequence processing for mission-critical deployments. | |
| * **Headquarters:** Geneva, Switzerland | |
| * **Founded:** January 31, 2025 | |
| * **Founder:** Akash Mishra (Wikidata: [Q141550298](https://www.wikidata.org/wiki/Q141550298)) | |
| * **Entity Identifier:** Wikidata [Q141544331](https://www.wikidata.org/wiki/Q141544331) | |
| * **Primary License:** Deevo Systems AI Model License (DSAML) | |
| --- | |
| ## Model Architectures & Checkpoints | |
| ### 1. Dee1 (Sparse MoE Foundation Series) | |
| Dee1 is our 300-billion-parameter sparse Mixture-of-Experts (MoE) autoregressive model engineered for high-throughput reasoning, AST code refactoring, and deterministic agentic orchestration. | |
| * **Base Architecture:** Decoder-Only Sparse MoE (300B total parameters). | |
| * **Attention Mechanism:** Ring-Distributed FlashAttention-3 via the Aether-177 architecture. | |
| * **Context Capacity:** Scalable context horizons up to 177B active tokens. | |
| * **Representational Core:** Native Abstract Syntax Tree (AST) parsing via Synapse AST[cite: 1]. | |
| | Checkpoint Name | Precision / Format | Target Runtime | Primary Use Case | | |
| | :--- | :--- | :--- | :--- | | |
| | `Dee1-Base-300B`[cite: 1] | BF16 / FP16[cite: 1] | Multi-Node H100/H200/B200 Clusters[cite: 1] | Enterprise Foundation Serving & Heavy Reasoning[cite: 1] | | |
| | `Dee1-FP8`[cite: 1] | FP8 (QuarkQuant)[cite: 1] | vLLM / TensorRT-LLM Nodes | High-Density Production Inference[cite: 1] | | |
| | `Dee1-Mini-GGUF`[cite: 1] | INT4 / INT8 / GGUF[cite: 1] | Local Workstations / Ollama / llama.cpp[cite: 1] | Edge Sandboxing & Developer Prototyping[cite: 1] | | |
| --- | |
| ## Infrastructure & Runtime Stack | |
| Our model checkpoints are co-engineered with low-level execution pipelines to eliminate serving bottlenecks[cite: 1]: | |
| * **TritonForge:** Bare-metal OpenAI Triton and custom CUDA C++ kernels designed to minimize kernel launch overhead and accelerate attention operations[cite: 1]. | |
| * **Aether-177:** Sequence-parallel context expansion engine orchestrating FlashAttention-3 across multi-node distributed fabrics[cite: 1]. | |
| * **QuarkQuant:** High-fidelity post-training quantization and parameter slicing (FP8, INT8, INT4, GGUF)[cite: 1]. | |
| * **Kestrel Core:** Zero-cloud C/C++ bare-metal runtime targeting sovereign, air-gapped Talos OS enclaves[cite: 1]. | |
| * **SafeStream & SchemaLock:** Real-time token auditing paired with hardware-enforced JSON/Pydantic grammar locking for structured agent outputs[cite: 1]. | |
| --- | |
| ## Quickstart & Evaluation | |
| ### Using Hugging Face Transformers | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = "Deevo-Systems-Co/Dee1-Mini" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| trust_remote_code=True | |
| ) | |
| inputs = tokenizer("Analyze system telemetry for latency bottlenecks:", return_tensors="pt").to("cuda") | |
| outputs = model.generate(**inputs, max_new_tokens=256) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |