Text Generation
Transformers
Safetensors
PyTorch
English
swarm_agi
causal-lm
swarm-intelligence
multi-agent
convergentintel
Instructions to use reaperdoesntknow/SAGI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaperdoesntknow/SAGI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/SAGI")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SAGI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use reaperdoesntknow/SAGI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reaperdoesntknow/SAGI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/SAGI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reaperdoesntknow/SAGI
- SGLang
How to use reaperdoesntknow/SAGI with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "reaperdoesntknow/SAGI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/SAGI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "reaperdoesntknow/SAGI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/SAGI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reaperdoesntknow/SAGI with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/SAGI
Add DISC Mathematical Foundations, update portfolio stats (49 models, 22,598 total downloads)
Browse files
README.md
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*Part of the [Standalone Models](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
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| Model | Downloads | Format |
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|-------|-----------|--------|
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| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 |
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| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
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**Total Portfolio:
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*Last updated: 2026-03-28 12:58 UTC*
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*Part of the [Standalone Models](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
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## Mathematical Foundations: Discrepancy Calculus (DISC)
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SAGI's swarm intelligence dynamics connect to Discrepancy Calculus through **Discrepancy Mechanics** (Ch. 16 of the DISC monograph) — a reformulation of dynamics that replaces Newton/Lagrange with four discrepancy laws:
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- **DL0 (Co-Motion):** Agent kinematics via metric derivative and environment flow
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- **DL1 (Discrepancy Energy):** $E_{\text{disc}}[f] = \frac{1}{2}\int w(x)(Df(x))^2 d\mu(x)$ — stability through bounded discrepancy energy
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- **DL2 (Force as Discrepancy Gradient):** Trust routing gradients as Euler-Lagrange from discrepancy action
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- **DL3 (Reciprocity):** Symplectic invariance preserved across agent interactions
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The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies the local mismatch in each agent's contribution. The trust mechanism between agents is operationally a discrepancy energy minimization: agents whose outputs have high mutual discrepancy are weighted down; agents converging on shared structure are amplified.
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Classical mechanics is recovered as a degenerate smooth limit of Discrepancy Mechanics — just as standard single-head attention is a degenerate limit of swarm routing.
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Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).
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## Related Models
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| Model | Downloads | Format |
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|-------|-----------|--------|
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| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 |
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| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
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**Total Portfolio: 49 models, 22,598 total downloads**
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*Last updated: 2026-03-28 12:58 UTC*
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