SystemOne-4B-Agentic-AGI
A 4-billion-parameter System One style decision & agentic model
Focused on fast, calibrated, typed decisions + stronger AGI-oriented agentic capabilities.
This repository contains the non-weight files (configs, tokenizer, templates, license, documentation) for a conceptual / research System One model.
No model.safetensors files are included (as requested).
What is a System One Model?
Inspired by TypeSafe AI’s Jev and Kahneman’s System 1 thinking:
- Takes unstructured state + typed questions
- Returns typed answers with calibrated probabilities (Choice / Score / Noul)
- Parallel evaluation of multiple questions
- No free-form text generation for decisions → zero schema / type errors by construction
- Extremely low latency and cost for decision workloads
- Designed to be composed inside software (smart if-statements, routing, scoring, guardrails)
This 4B variant adds stronger agentic / AGI-oriented capabilities:
- Tool-use and multi-step planning
- Self-reflection and confidence-aware routing
- Better long-horizon agent behavior
- Improved calibration under uncertainty
Model Details
| Property | Value |
|---|---|
| Architecture | Qwen3.5-based (dense) |
| Parameters | ~4B |
| Context length | 32k – 64k (depending on config) |
| Primary focus | System One decisions + Agentic |
| Training objective | RLCD-style + agentic trajectories |
| License | Apache 2.0 |
| Base | Qwen/Qwen3.5-4B |
Files in this repository
config.json– model configurationtokenizer.json/tokenizer_config.json/vocab.json/merges.txt– tokenizerchat_template.jinja– chat / system-one templatepreprocessor_config.json/video_preprocessor_config.json– preprocessor configsLICENSE– Apache 2.0.gitattributes– Git LFS attributesREADME.md– this file
Intended Use
- High-throughput decision making inside applications
- Agentic workflows that need reliable typed outputs + confidence
- Research into System One / Machine-Native Intelligence
- Prototyping AGI-style agents that combine fast decisions with deeper reasoning
Limitations
- This package does not include the actual model weights.
- Calibration quality and agentic performance depend on the final trained checkpoint.
- English is the strongest language; other languages may require additional fine-tuning.
Citation / Inspiration
- TypeSafe AI – System One Models & Jev (2026)
- Qwen team – Qwen3.5 series
- Kahneman – Thinking, Fast and Slow
How to use (once weights are available)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-org/SystemOne-4B-Agentic-AGI", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("your-org/SystemOne-4B-Agentic-AGI")
# Example System One style call would go through a custom head or constrained decoding
Created for research and experimentation with System One + Agentic AGI paradigms.
No model weights included.