Model efficiency
We remove structural redundancy through depth pruning, quantization, compression, and efficient inference. The goal is not a smaller model by itself. The goal is to preserve useful intelligence at a lower cost.
Research themes & collaborations
Fast Lightweight
AGI, built in the open.
Scaling made modern AI powerful. It also made intelligence expensive. We study how to remove redundancy, improve learning, and build systems that reason and coordinate with less computation.
Fast
Lightweight
AGIResearch objective
Lightweight
AGIResearch objective
Optimization
Reasoning
Open models
Efficiency
- Mission
- Fast Lightweight AGI
- Research mode
- Cross-institutional collaboration
- Primary systems
- LLMs · agents · neural networks
- Open output
- Papers · models · evaluations
Research themes
Capability should not require unlimited scale.
We work across four connected themes. Each one asks the same practical question: how can an AI system learn, reason, and act better while using fewer resources?
Optimization and generalization
We study gradient dynamics, sharpness, low-precision training, and calibration. Better optimization should make learning more stable and more transferable, not simply faster on one benchmark.
Reasoning and agent systems
We investigate models that can plan, revise, use tools, and coordinate with other agents. Reliable intelligence comes from the entire loop, including memory, feedback, and verification.
Evaluation and deployment
We measure the tradeoff between capability, latency, memory, and financial cost. A model is useful only when its efficiency gains survive contact with real workloads and responsible deployment.
Active program
Questions we are working on now.
Our projects move between theory, experiments, and usable artifacts. Results may become papers, model checkpoints, evaluations, or new research questions.
How much depth does an LLM actually need?
We study locality-aware redundancy, calibration objectives, and search methods for removing layers without losing the behavior that matters.
Can low-precision learning remain stable?
We examine gradient slowdown, normalization, and sharpness-aware optimization to make low-precision training more reliable.
What makes an agent loop improve over time?
We explore multi-agent coordination, feedback loops, memory, and verification as parts of a complete reasoning system.
How we collaborate
One question, many points of view.
Researchers from universities, public institutes, and industry labs work together around a shared problem. The institution is context. The research question is the center.
01
Frame
Define a clear research question and the cost that matters.
02
Test
Run focused experiments with reproducible baselines.
03
Build
Turn the useful result into a model, method, or system.
04
Share
Release papers, evaluations, and open model artifacts.
Open model hub
Research you can run.
Our Hugging Face organization is where we upload the models we build, including compressed LLM checkpoints and their evaluation materials.
Hugging Face organization
atlasium-efficient
This is the public home for models produced through our efficient AI work. Current collections include compressed Qwen3 and Llama model families, with checkpoints designed to study capability at lower depth and cost.
Public artifactsOpen
Qwen3 compressed seriesDepth-reduced language models
10B to 12BLlama compressed seriesMultiple model sizes and pruning ratios
2B to 7BEvaluation materialsCapability and efficiency reporting
Measured