Welcome

Welcome to SoulAI Docs

Powering the Next Wave of Agentic Intelligence

$SOUL AI is a purpose-built Large Language Model (LLM) based on Meta's LLaMA 3.1–8B, fine-tuned for financial reasoning, LLM agent workflows, and smart automation.

🚀 Overview

SoulAI isn't just a chatbot. It’s an intelligent, agentic foundation designed for:

Finance-specific logic
Crypto automation
DeFi workflows
Conversational agents
On-chain agent execution
Multi-agent collaboration
Smart contract analysis
Automated crypto trading
Cross-agent communication
Task parallelization and complex file operations

Jump right in

🧠 Model Details

Base Model: Meta Llama 3.1–8B
Tuning Method: PEFT / LoRA
Training Platform: HuggingFace AutoTrain
Instruction Samples: 10,000+ domain-specific
Purpose: Optimized for chain-of-thought, agent simulation, and dynamic workflow creation

SoulAI agents talk to each other and operate like a fluent, on-chain group chat: the Yield-Hunter pings “SOL pool just flipped to 14 %—any objections?”; the Risk-Oracle replies “Volatility low, contract audited—green light,” while the Bridge-Runner chimes in “Funds en-route via Wormhole, ETA 30 s.” Each message is a signed smart-contract call, so as they converse they simultaneously fetch data, reroute liquidity, and execute transactions—dividing labour, cross-checking outcomes, and finishing complex workflows in parallel before you ever lift a finger.

📊 Dataset Summary

This model was trained on instruction-style data focusing on:

Financial queries, tokenomics
LLM-agent interaction patterns
DeFi strategies and trading logic
API-triggered workflows
Smart contract execution
Autonomous task generation
Crypto chart analysis
Solidity code reviews and risk assessments

💻 Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "YOUR_HF_USERNAME/YOUR_MODEL_NAME"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype="auto"
).eval()

messages = [{"role": "user", "content": "How do autonomous LLM agents work?"}]

input_ids = tokenizer.apply_chat_template(
    conversation=messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

output_ids = model.generate(input_ids.to("cuda"), max_new_tokens=256)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)

🌐 Inference API

Use Hugging Face Inference API:

curl https://api-inference.huggingface.co/models/YOUR_HF_USERNAME/YOUR_MODEL_NAME \
-X POST \
-d '{"inputs": "Tell me something about agent-based AI."}' \
-H "Authorization: Bearer YOUR_HF_TOKEN"

🧬 Soul Ecosystem Integration

Agent Forge: Drag-and-drop agent design
Smart Contracts: LLM-generated contract logic
Monetization: $SOUL-based fee streams
Collaborative Income: Reward nodes, dataset owners, model creators
API Webhooks, human-in-the-loop approvals, and sub-agent spawning

💼 Use Cases

Chat, Discord, and IVR agent deployment
Financial workflow automation
DeFi scenario simulation
Smart contract analysis and deployment
Crypto trading bot agents
Multi-agent teamwork on tasks and files
Chart pattern recognition and decision making

🧾 Tokenomics

Total Supply: 888,888,888 $SOUL (Hard Cap)
75%: Public Launch (DEX liquidity)
10%: Ecosystem (staking, rewards)
5%: Treasury
10%: Exchange Liquidity

💰 Monetization Path

Every agent action:

Generates revenue
Sends auto-split fees in $SOUL to your wallet
Rewards contributors: data, model, infra

🧠 Adaptive Intelligence

Learns from user prompts & documents
Fully customizable & trainable
Expose as an API with pay-per-call $SOUL pricing

🔐 License & Limitations

License: Restricted / Other (permission required for commercial use)
Limitations:
- Not suited for high-stakes finance out-of-the-box
- Best with human validation or approval layers

👥 Team Note

SoulAI is built and maintained by a collaborative team dedicated to AI, crypto, and decentralized ecosystems. While certain contributors are more public-facing, there is a strong development and operations backbone.

If the project performs well and the team receives sufficient support, they are committed to rewarding those who believe in and contribute to SoulAI’s success.

“If we want SoulAI to resonate with crypto audiences, the site and docs must clearly show real use cases—agents that read charts, make trades, analyze smart contracts, and collaborate like real teams.”

— Satoshi Nakamoto (SoulAI Dev)

📞 Get In Touch

🧠 Soul AI Vision

Build once. Deploy forever. Earn passively through every agent action. Backed by Ethereum, governed by smart contracts, and powered by $SOUL.

The next industrial revolution is autonomous LLM agents. SoulAI is your foundation.

📢 What They Say

"We replaced a patchwork of 14 SaaS tools with a single Soul AI agent. It now writes contracts, syncs with our CRM, and pushes PRs." — Alfred Johnson, CEO

45% productivity boost
90% fewer manual errors
100% speed improvement

NextQuickstart SoulAI Agent

Last updated 10 days ago

from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "YOUR_HF_USERNAME/YOUR_MODEL_NAME" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype="auto" ).eval() messages = [{"role": "user", "content": "How do autonomous LLM agents work?"}] input_ids = tokenizer.apply_chat_template( conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ) output_ids = model.generate(input_ids.to("cuda"), max_new_tokens=256) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) print(response)