Solstice-AI/HyperSynapse-SAS-Chat-Templates

🧠 HyperSynapse-SAS (SelfAgentSwarm) Chat Templates

The Unified 10-Level Cognitive Escalation & Autonomous Multi-Agent Swarm Framework

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🌟 Overview

HyperSynapse-SAS (HyperSynapse Self-Agent Swarm) is a high-performance cognitive chat template framework that equips any Large Language Model with:

1. 10-Level Solo Cognitive Escalation (Level 0 Mortal to Level 9 Oracle): Dynamically scale reasoning depth, token runway, and proof rigor per prompt without triggering multi-persona roleplay clutter.
2. Decoupled Multi-Agent Swarm Modes ([swarm] & [deep-swarm]): On-demand simulated 20-agent divergent swarms and adversarial consensus councils for massive, open-ended problem spaces.
3. Model-Agnostic / Universal Template: A plug-and-play ChatML Jinja template compatible with any modern LLM, plus dedicated, fine-tuned templates for all major model families.

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📁 Available Templates

Template	File	Target Architectures & Families
Universal (Model-Agnostic)	templates/universal.jinja	Standard ChatML, vLLM, SGLang, llama.cpp, Ollama, LM Studio, Any LLM
DeepSeek	templates/deepseek.jinja	DeepSeek-V3, DeepSeek-V4, DeepSeek-V4.1 Flash, DSML Tool Calling
Qwen	templates/qwen.jinja	Qwen 2.5, Qwen 3, Qwen 3.5, Qwen 3.8, Qwopus, QwQ
GLM	templates/glm.jinja	GLM-4, GLM-5.3, GLM Flash
Llama	templates/llama3.jinja	Meta Llama 3, Llama 3.1, Llama 3.2, Llama 3.3, Llama 4
Mistral	templates/mistral.jinja	Mistral 7B/12B, Mixtral 8x7B/8x22B, Codestral, Pixtral
Gemma	templates/gemma.jinja	Google Gemma 2, Gemma 3, Gemma 4

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🎯 Cognitive Trigger Spectrum

Simply prepend a tag to your query or pass --level X in your system instructions:

Solo Cognitive Escalation (Pure Analytical Focus)

Tag / Level	Persona / Archetype	Budget	Cognitive Modality
[Level 0]	Mortal	0 tokens	Instant Response: CoT/thinking completely disabled. Rapid, direct execution with zero discursive fluff.
[Level 1]	Hermes	~256 tokens	Rapid Instinct: Single-path heuristic sanity check before emitting answer.
[Level 2]	Apollo	~512 tokens	Crisp Logic: Explicit step-by-step reasoning with edge-case validation.
[Level 3]	Artemis	~1,024 tokens	Boundary Hunter: Focuses on edge cases, off-by-one errors, and null states.
[Level 4]	Athena	~2,048 tokens	Systemic Balance: Second-order effects, system architecture, modular design.
[Level 5]	Prometheus	~4,096 tokens	Defensive Engineering: Fault tolerance, production bottlenecks, failure mode audit.
[Level 6]	Solstice	~8,192 tokens	Deep Derivation: Exhaustive theoretical derivation and mathematical mechanics.
[Level 7]	Hyperion (Default)	~16,384 tokens	Foundational Synthesis: First-principles breakdown from ground axioms.
[Level 8]	Einstein	~32,768 tokens	Mastery & Invariants: Theoretical proofs, deep algorithmic complexity analysis.
[Level 9]	Oracle	Unbounded	Maximal Rigor: Exhaustive search runway, proof-grade verification, definitive closure.

Autonomous Multi-Agent Swarm Modes

Trigger	Mode Name	Protocol & Behavior
[swarm]	20-Agent Divergent Swarm	Activates a simulated 20-perspective emergent swarm (Analytical Logician, Creative Maverick, Systems Engineer, Red-Teamer, Optimizer, Critic, Synthesizer) with divergent exploration followed by structured synthesis.
[deep-swarm]	Adversarial Research Council	Deploys a multi-round debate tournament pitting 3–5 specialized domain specialists against each other to falsify weak assumptions and converge on proof-grade deliverables.

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🚀 Quick Start

1. Using the Universal Template in Python (transformers)

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Load the universal HyperSynapse-SAS template
with open("templates/universal.jinja") as f:
    tokenizer.chat_template = f.read()

messages = [
    {"role": "user", "content": "[Level 3] Explain quantum decoherence rigorously."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(prompt)

2. Apply Directly to Any Hugging Face Hub Repo

Use our built-in CLI applier to update any repository or local folder with one command:

# Push to Hugging Face Model Hub
python apply_template.py --template deepseek --model-id Solstice-AI/My-Model --push

# Or apply locally to a weights directory
python apply_template.py --template universal --local-dir ./my_local_model

3. Using in vLLM or SGLang

Pass --chat-template templates/universal.jinja directly at startup:

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --chat-template templates/universal.jinja \
  --port 8000

4. Using in llama.cpp

llama-cli \
  --model my_model.gguf \
  --chat-template-file templates/universal.jinja \
  -p "<|im_start|>user\n[swarm] Architect an ultra-low latency event bus.<|im_end|>\n<|im_start|>assistant\n"

5. Ollama Modelfile

FROM ./my_model.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
PARAMETER stop "<|im_end|>"

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📄 License

Released under the permissive MIT License. Free for commercial and personal use across any open-weight model or inference stack.

Developed by Solstice-AI
Unshackled Cognition • Multi-Agent Collective Intelligence • Scalable Autonomous Systems