Orca-4B-flash-max

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

Files in this repository

• config.json – model configuration
• tokenizer.json / tokenizer_config.json / vocab.json / merges.txt – tokenizer
• chat_template.jinja – chat / system-one template
• preprocessor_config.json / video_preprocessor_config.json – preprocessor configs
• LICENSE – Apache 2.0
• .gitattributes – Git LFS attributes
• README.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

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Created for research and experimentation with System One + Agentic AGI paradigms.
No model weights included.