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title: Experimentlab
emoji: ⚛️
colorFrom: indigo
colorTo: purple
sdk: static
pinned: false
---
<div align="center">
# ⚛️ Experimentlab (Experimentlab.in)
### Sovereign Artificial Intelligence for Science, Technology, Engineering & Mathematics (STEM)
[](https://experimentlab.in)
[](https://github.com/Vigyan-workstation)
[](https://huggingface.co/Experimentlab)
[](https://discord.com/invite/Jck3tD6mhf)
[](https://github.com/Vigyan-workstation/vigyan-workstation/releases/tag/v1.0.0)
> *"Here’s to the rebels who build real things in the physical world. The ones who refuse to rent their intelligence by the token. Who pull the plug and keep calculating."*
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---
## 🌌 The Mission: Sovereign STEM Intelligence
**Experimentlab** is a deep-tech research company building sovereign foundation models, efficient Small Language Models (SLMs), and air-gapped desktop workstations engineered specifically for **physical engineers, roboticists, semiconductor designers, and mathematicians**.
Led by Founder & Chief Architect [Shreyansh Singh](https://github.com/shreyansh001boy-tech), our research bridges fundamental academic theory with sovereign edge execution:
* **Zero Cloud Dependence**: 100% offline, privacy-first execution on local laptops and workstations.
* **Deterministic Mathematical Truth**: Computer Algebra System (SymPy CAS) verification eliminating hallucinated calculus, ODEs, and physics.
* **Sovereign Quantization**: Pioneering Sparse Mixture of Experts (MoE) and 1.58-bit ternary {-1, 0, +1} BitNet quantization running natively on edge CPUs and NPUs.
---
## 🏛️ The 5-Pillar Public AI Catalog
```mermaid
graph LR
A["Raw STEM Corpora (1B+ Tokens, arXiv, Dolma)"] --> B["Vigyan Sovereign Pretraining"]
B --> C["1. Frontier STEM SLMs (32B / 7B / 2B)"]
B --> D["2. Advanced Architectures (MoE / 1.58-bit / GRPO)"]
B --> E["3. Domain Mastery (RTL / Math / Biomed / Vidhi)"]
C --> F["5. Edge GGUF Runtimes & Desktop Studios"]
D --> F
E --> F
B --> G["4. Foundation Pretraining Datasets"]
```
### 1. 🚀 Frontier STEM & Dense SLMs
* **[`Vigyan-AI-32B`](https://huggingface.co/Experimentlab/Vigyan-AI-32B)** & **[`Vigyan-AI-32B-Titan-v1`](https://huggingface.co/Experimentlab/Vigyan-AI-32B-Titan-v1)**: Flagship 32B Titan models for deep multi-step scientific deduction.
* **[`Vigyan-7B-STEM-Instruct-v1`](https://huggingface.co/Experimentlab/Vigyan-7B-STEM-Instruct-v1)** & **[`Shreyansh-STEM-AI-7B-Final`](https://huggingface.co/Experimentlab/Shreyansh-STEM-AI-7B-Final)**: High-performance 7B dense scientific models.
* **[`Shreyansh-STEM-AI-2B-v3`](https://huggingface.co/Experimentlab/Shreyansh-STEM-AI-2B-v3)** *(1,195+ downloads)* & **[`Shreyansh-STEM-AI-2B-v3-Distilled`](https://huggingface.co/Experimentlab/Shreyansh-STEM-AI-2B-v3-Distilled)**: Compact, battery-friendly edge SLMs.
### 2. 🌟 Advanced Architectures (MoE, 1.58-Bit Ternary, GRPO)
* **[`Vigyan-1.5B-4x-MoE`](https://huggingface.co/Experimentlab/Vigyan-1.5B-4x-MoE)** *(1,669+ downloads)*: 4-expert routed Sparse MoE delivering 7B-class STEM reasoning at 1.5B inference latency.
* **[`Vigyan-3B-4x-MoE`](https://huggingface.co/shreyansh12183/Vigyan-3B-4x-MoE)** *(704+ downloads)*: Advanced Top-1 routing multi-expert model tuned for symbolic logic.
* **[`vigyan-3b-1.58bit-qat`](https://huggingface.co/shreyansh12183/vigyan-3b-1.58bit-qat)**: Pure 1.58-bit {-1, 0, +1} ternary BitNet model trained via Quantization-Aware Training (QAT). Replaces matrix multiplication with integer additions.
* **[`vigyan-2b-reasoning-grpo`](https://huggingface.co/shreyansh12183/vigyan-2b-reasoning-grpo)**: DeepSeek-style GRPO test-time reasoning SLM trained on mathematical and logical verification trees.
* **[`vigyan-olmoe-1b-7b-masterpiece-adapter`](https://huggingface.co/shreyansh12183/vigyan-olmoe-1b-7b-masterpiece-adapter)**: Ai2 OLMoE sparse expert architecture.
### 3. 🔬 Doctoral & Domain-Specialized Intelligence
* **[`olmo2-7b-silicon-rtl-eda`](https://huggingface.co/Experimentlab/olmo2-7b-silicon-rtl-eda)**: Hardware silicon EDA specialist for synthesizable IEEE 1364 Verilog RTL, FSMs, and synchronous timing circuits.
* **[`olmo2-7b-phd-pure-math`](https://huggingface.co/Experimentlab/olmo2-7b-phd-pure-math)**: Specialization in pure mathematics, formal topological proofs, and abstract algebra.
* **[`olmo2-7b-biomed-chem`](https://huggingface.co/Experimentlab/olmo2-7b-biomed-chem)**: Deep biochemistry, enzyme kinetics, and molecular thermodynamics.
* **[`olmo2-7b-astro-logic`](https://huggingface.co/Experimentlab/olmo2-7b-astro-logic)**: Orbital mechanics, Hohmann transfers, and astrophysical physics.
* **[`Vidhi-AI-Instruct`](https://huggingface.co/Experimentlab/Vidhi-AI-Instruct)**: Sovereign Indian legal intelligence aligned with the Bharatiya Nyaya Sanhita (BNS).
### 4. 📚 Foundational Pretraining Corpora & Datasets
* **[`shreyansh-1B-SLM-pretrain-stem-english`](https://huggingface.co/datasets/shreyansh12183/shreyansh-1B-SLM-pretrain-stem-english)**: 1 Billion token foundation pretraining corpus balanced for cold-start STEM intelligence.
* **[`shreyansh-hinglish-english-stem-500k`](https://huggingface.co/datasets/shreyansh12183/shreyansh-hinglish-english-stem-500k)**: 500,000 sample bilingual Hinglish-English dataset tailored for Indian engineering students.
* **[`vigyan-ai-research-papers`](https://huggingface.co/datasets/Experimentlab/vigyan-ai-research-papers)**: Curated corpus of scientific literature and verified technical preprints.
### 5. ⚡ Edge GGUF Runtimes & Workstation Integration
* **[`Vigyan-Models-GGUF`](https://huggingface.co/Experimentlab/Vigyan-Models-GGUF)**: Quantized GGUF binaries (`Q4_K_M`) engineered for local execution via llama.cpp and Ollama.
* **[`vigyan-7b-moe-gguf`](https://huggingface.co/shreyansh12183/vigyan-7b-moe-gguf)**: Edge quantized MoE binary.
* **[Vigyan Workstation](https://github.com/Vigyan-workstation/vigyan-workstation)**: Open-source desktop engineering studio pairing these weights with 3D robotics kinematics, WaveDrom waveforms, and KiCad PCB netlists.
---
## 📜 Academic Attribution & Sovereign Citation
```bibtex
@article{singh2026vigyan,
title={Vigyan Sovereign Trilogy: Empirical Scaling Laws and Deterministic Symbolic-AST Evaluation of Edge Small Language Models},
author={Singh, Shreyansh},
institution={Vigyan AI Open Research},
journal={Vigyan AI Research Publications},
year={2026},
url={https://github.com/shreyansh001boy-tech/vigyan-sovereign-benchmark}
}
```
---
<div align="center">
<sub>Crafted with passion for sovereign open-source STEM research by <a href="https://experimentlab.in">ExperimentLab.in</a> • Built for the Crazy Ones.</sub>
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