Spaces:
Running
title: AlphaBench
emoji: 🧠
colorFrom: gray
colorTo: gray
sdk: static
pinned: false
AlphaBench
Mission: Build the most capable, efficient, and deployable large language models ever created — trained on home-compute, deployable at any scale, optimized to surpass every existing benchmark, and engineered to be as close to artificial general intelligence as current science permits.
What We Are Building
AlphaBench is an independent AI research initiative. We develop open-weight language models under the Atman series — named from the Sanskrit Atman, meaning the eternal, true self or soul, distinct from the temporary physical body, mind, and ego. The name is intentional. We are not building another chatbot. We are building a model that reasons, reflects, and acts with genuine depth and agency.
Research began in March 2025. Every model we release is the product of rigorous, constraint-driven engineering — no cloud compute budget, no institutional backing, no shortcuts. If it works here, it works anywhere.
Core Principles
Efficiency is not a tradeoff. We treat compute constraints as a design requirement, not a limitation. Every architectural decision is made with deployment in mind — from a single personal device to national-scale infrastructure.
Benchmarks are targets, not marketing. We build toward measurable, reproducible performance. Every release is evaluated against the strongest available baselines. We do not publish claims we cannot back with numbers.
Agentic by design. Atman models are built from the ground up with agentic capability as a first-class requirement — long-horizon reasoning, tool use, self-correction, and autonomous task execution are not afterthoughts.
Open and reproducible. Our research, weights, and methodology are made available to the public. The goal is to advance the field, not gate it.
Atman Model Series
The Atman series represents our primary research line. These models are:
- Trained entirely on home-compute hardware
- Architecturally optimized for parameter efficiency and inference speed
- Designed to exceed state-of-the-art performance on established LLM benchmarks
- Capable of being deployed on minimal hardware without degrading core capability
- Built with agentic reasoning as a native property, not a fine-tuned add-on
The Atman models target the full capability spectrum — from edge deployment on personal machines to scaled production serving millions of requests.
Research Focus Areas
- Efficient pre-training at scale under resource constraints — proving that frontier-class models do not require frontier-class infrastructure
- Reasoning architecture — advancing chain-of-thought, multi-step inference, and self-reflective reasoning beyond current published methods
- Agentic training and alignment — developing training pipelines that produce models capable of sustained autonomous task completion
- Benchmark-driven development — systematic evaluation against MMLU, HumanEval, MATH, ARC, HellaSwag, and emerging agentic benchmarks
- Quantization and deployment optimization — ensuring full capability is accessible at every compute tier
Standing
AlphaBench operates independently. We have no corporate affiliation, no venture backing, and no compute credits from hyperscalers. This is deliberate. The most important proof of concept we can offer is not a benchmark score — it is the fact that everything we build was built without the resources everyone said were required.
The Deepseek R1 reasoning model was the last major inflection point in this field. Atman is the next one.
Contact and Collaboration
We welcome collaboration from researchers, engineers, and institutions aligned with our mission. If you are working on efficient training, novel architectures, or agentic systems and want to contribute, reach out through our HuggingFace profile or associated channels.
All model releases, evaluation results, and technical reports will be published here as research progresses.
AlphaBench — Research started March 2025.