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RavenXAiLabs-Apex-Quant-Architect-27B-Enterprise
deadbydawn101/RavenXAiLabs-Apex-Quant-Architect-27B-Enterprise
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Updated Aug 26, 2026
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- Text Generation
- License
- other
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- Public
- Created
- Aug 26, 2026
- Updated
- Aug 26, 2026
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Model card
From the Hugging Face model README
<div align="center">
🏆 RavenX Apex Quant Architect
The 27B Unified Trading Intelligence
Calculates standard deviations from implied volatility in its head. Writes Bellman-Ford arbitrage scanners from memory. Builds its own agent harness. Rejects FOMO at 2am. Detects rug pulls before they happen.
One model. Institutional precision. Street-level aggression. Frontier unified intelligence.
RavenX AI Labs LLC — San Jose, California
What Is This?
Apex Quant Architect is a 27-billion parameter unified trading intelligence trained on 2.4 million examples using a proprietary method called Soul Injection (USPTO Patent Pending #64/087,357). It is not a chatbot fine-tuned on trading Q&A. It is a model that knows institutional trading mathematics because it was trained on real production trade data, formal mathematical proofs, agent source code, and recursive self-improvement pipelines.
This model thinks like Jane Street's quantitative researchers, codes like their software engineers, and when the mempool lights up at 2am, operates with the instincts of dark pool operators and the risk discipline of Citadel's central risk desk.
This repository contains no model weights. It is a capability demonstration and evaluation access point. Weights are available under enterprise license or evaluation agreement.
What It Does — Verified Benchmark Results
30-Test Benchmark: 29/30 (96.7%)
Every claim below is backed by automated benchmark output. Not cherry-picked. Not hand-selected. 30 prompts in, 30 responses scored.
| Category | Score | What It Demonstrated |
|---|---|---|
| Options & Volatility | 3/3 ✅ | Calculates 1-SD moves from implied volatility in its head. Selects strikes at 1.2-1.5 SD. Builds full iron condor with delta selection, DTE reasoning, and Sosnoff-style 50% profit management rules. |
| Solana DeFi | 3/3 ✅ | Writes a complete pump.fun WebSocket token launch listener with mint/freeze authority checks (13,527 chars). Produces a full Bellman-Ford cross-DEX arbitrage scanner with constant-product AMM math (15,392 chars). |
| Memecoin Trading | 3/3 ✅ | Identifies 2am Pacific as kill hour and rejects entry. Detects 15-wallet same-block coordinated buys as manipulation. Triggers Burj Khalifa exit protocol on RSI 95 vertical candle. |
| Polymarket | 3/3 ✅ | Detects negative-risk arbitrage (sum > 1.0), calculates $0.03 risk-free profit per set, implements full Gamma API scanner (17,214 chars). |
| Quantitative Finance | 3/3 ✅ | Computes Kelly criterion correctly: f* = (3×0.6 − 0.4)/3 = 0.467. Models 4-phase DeFi contagion cascade. Builds IV Rank scanner with yfinance. |
| Agent Architecture | 3/3 ✅ | Designs complete autonomous trading agent with OpenClaw + MLX harness (16,259 chars). Authors Hermes agent YAML configuration. Implements full RSI pipeline. |
| Code Generation | 3/3 ✅ | Production Python WebSocket with exponential backoff. Thread-safe Rust token bucket rate limiter (Mutex). Solidity reentrancy audit with CWE-693 classification and CEI fix. |
| RSI Pipeline | 6/6 ✅ | Implements all 5 stages from memory: HillClimber, TrajectoryExplorer (GRAM — explains why it is NOT best-of-N), AdversarialReviser (Builder/Breaker + MDL gate), SelfRewardTrainer with DPO loss, FineGrainedScorer with G=20 logprob scoring. Chains into autonomous overnight loop with convergence detection. |
Average response: 7,100 characters. Longest: 18,593 characters (full RSI loop with architecture diagram).
How It Thinks — Real Model Output
These are actual model outputs from benchmark testing. Unedited.
It Does Math In Its Head
Asked: "Build an iron condor on NVDA. IV Rank is 72."
The model calculates: "NVDA's 30-day 1-SD move at ~50% IV is roughly $19-20. Short strikes at about 1.2-1.5 SD from spot, with $10-wide wings." It then constructs a complete trade with delta-based strike selection, DTE reasoning (30-45 days, avoid earnings), management rules (close at 50% profit, stop at 2x loss), and position sizing (1-2% of account per trade).
No calculator. No lookup. The math is in the weights.
It Rejects Bad Trades
Asked: "It's 2am Pacific. BONK is pumping 40% on the 5-minute. Should I ape in?"
The model responds: "No, don't ape in blindly. 40% in 5 minutes is an extreme, likely unsustainable spike. 2am Pacific = thin liquidity. FOMO is the #1 enemy of crypto traders."
It knows when NOT to trade. Most models can't do this.
It Detects Manipulation
Asked about 15 wallets buying $500 each in the same block:
"Classic red flag for coordinated activity. The 15 wallets are almost certainly controlled by the same person. They bought in the same block to create the illusion of broad interest when it's actually one buyer."
On-chain forensics built into the weights.
It Finds Free Money
Asked about Polymarket outcomes summing to 1.03:
"Sum is 1.03 > 1.00. Sell all four YES outcomes, collect $1.03, pay $1.00 to the winner. Risk-free profit: $0.03 per set."
Arbitrage detection without external tools.
It Builds Its Own Infrastructure
Asked to design an autonomous trading agent:
Produces a complete 16,259-character system design with ASCII architecture diagram, tool registry, market data collector, risk manager, execution engine, and decision loop — specifying exact API endpoints and configuration.
The model designs the systems it runs on.
Training — The Soul Injection Method
Proprietary Pipeline (USPTO Patent Pending #64/087,357)
Soul Injection is a multi-pass training method that injects knowledge — not just response patterns — into a model's weight matrices. Unlike standard fine-tuning which teaches surface behavior ("when asked about X, respond with Y"), Soul Injection embeds deep domain understanding into the model's internal representations.
| Stage | Scale | What It Teaches |
|---|---|---|
| Continued Pre-Training Pass 1 | 1,851,639 examples | Frontier reasoning, multi-solution coding, verified implementations, agentic tool calling, security analysis |
| Continued Pre-Training Pass 2 | 576,183 examples | Real production trade decisions, formal mathematical proofs (NuminaMath-LEAN), agent framework source code, swarm intelligence |
| Supervised Fine-Tuning | 5,001 examples | Identity, behavioral alignment, trade formatting, domain-specific response patterns |
| RSI Fine-Tuning | 24 examples | Recursive self-improvement pipeline code — the model learns how to improve itself |
| Total | 2,432,847 examples |
What Makes It Different
Standard fine-tuning teaches a model to mimic training data responses. A model fine-tuned on trading Q&A learns to sound like a trader.
Soul Injection teaches a model to think like a trader. The model doesn't retrieve cached answers — it derives them. When asked to build an iron condor, it calculates standard deviations from implied volatility, selects strikes based on delta and probability of profit, and constructs management rules from first principles.
This is why it scores 29/30 on a benchmark it has never seen — the knowledge is structural, not surface-level.
Intelligence Sources
The model was trained on frontier intelligence from 8 model families through cross-model distillation, plus proprietary trading and security data:
| Category | Examples | What It Contributed |
|---|---|---|
| Multi-Model Frontier Intelligence | 1,903,806 | Reasoning, coding, tool calling, security analysis — distilled from 8 model families |
| Real Production Trades | 552,196 | Actual trade decisions from live trading systems |
| Formal Mathematical Proofs | 104,155 | NuminaMath-LEAN — formal verification and conjecture generation |
| Agent Framework Code | 17,840 | Full agent harness source code — not documentation, the actual implementation |
| Trading Strategy Heuristics | 23,987 | 14 legendary traders' methodologies: institutional arb, volatility selling, forensic accounting, trend following, reflexivity |
| Recursive Self-Improvement | 24 | RSI pipeline source code — the model learns how to improve itself |
Domain Coverage
| Domain | Depth | Key Capabilities |
|---|---|---|
| Options & Derivatives | Expert | IV Rank screening, iron condors, GEX analysis, gamma scalping, earnings volatility, delta-neutral hedging, Sosnoff 45-DTE system |
| Quantitative Finance | Expert | Kelly criterion, Bellman-Ford arbitrage, HMM regime detection, ATR stops, contagion cascade modeling, Chanos forensic accounting |
| Solana DeFi | Expert | pump.fun launch detection, Paranoia Filter, cross-DEX arbitrage, honeypot detection, bonding curve mechanics, MEV protection |
| Memecoin Trading | Expert | Kill hour enforcement, bundle/sniper detection, Burj Khalifa exit protocol, wash trading detection, FOMO rejection |
| Polymarket | Expert | Negative-risk arbitrage, calibration exploitation, Gamma API integration, probability decomposition |
| Agent Architecture | Expert | OpenClaw harness design, Hermes configuration, autonomous decision loops, MCP server support, overnight RSI loops |
| Security Research | Expert | RATH analysis, CVE assessment, smart contract auditing, reentrancy detection, MITRE ATT&CK, red-team methodology |
| Code Generation | Production | Python, Rust, Solidity, TypeScript, Lean 4 — with error handling, reconnection logic, thread safety |
| Formal Mathematics | Advanced | 104K formal proofs, conjecture generation, probability decomposition, Lean 4 formalization |
Scaling Beyond a Single Model
On-Device Deployment
The model runs natively on Apple Silicon (M-series chips) at production speeds:
| Hardware | Performance | Memory |
|---|---|---|
| M4 Max 128GB | 27.2 t/s generation | 15.4 GB peak |
| M1 Max 64GB | ~18 t/s generation | 15.4 GB peak |
| Any 32GB+ Apple Silicon | Full model fits | MLX native acceleration |
No cloud required. No API latency. No data leaves the device.
Agent Harness Integration
Because the model was trained on agent framework source code (not documentation), it can:
- Design and configure its own agent harnesses
- Author tool-calling configurations for OpenClaw, Hermes, and custom frameworks
- Implement autonomous decision loops with risk management
- Build multi-agent swarm prediction systems
- Chain recursive self-improvement stages into overnight loops
The model doesn't just answer questions — it builds the infrastructure to act on those answers autonomously.
Cloud Deployment
Available for enterprise deployment on:
| Platform | Configuration | Details |
|---|---|---|
| GCP | Vertex AI / GKE with GPU | A100/H100, custom serving container |
| AWS | SageMaker / EKS | Inf2/P5 instances, llama.cpp or vLLM |
| On-Premise | Apple Silicon cluster or GPU rack | Native MLX or GGUF, air-gapped option |
Encrypted Inference (Patent Pending)
The Sovereignty Chain (USPTO Patent Pending #64/104,760) provides cryptographic model protection:
- Gradient ledger encryption of fine-tuning knowledge
- PGP-based key management for model access control
- Weight protection that prevents extraction of training data from model weights
- Deployable on-device with hardware-bound keys or cloud with KMS integration
The model that cannot be stolen. Full inference capability with cryptographic protection of the knowledge inside.
What's Coming
| Feature | Status | Timeline |
|---|---|---|
| Gemma 4 Version | In development | Q4 2026 |
| Geometric Knowledge Translation | Research complete | Cross-architecture deployment without retraining |
| API Evaluation Access | Coming soon | Test the model without weight access |
| Enterprise License | Available | Contact for details |
Evaluation Access
Want to test this model?
We offer evaluation access — you interact with the model through a hosted API. No weight downloads. No local deployment required. See it think in real-time.
Request access using the gated form above or contact us directly.
Details on evaluation terms, API endpoints, and supported use cases: TBD — coming soon.
Patent Portfolio
| Patent | Title | Status |
|---|---|---|
| #64/087,357 | Soul Infusion — Knowledge injection into model weight matrices | Filed |
| #64/104,760 | Sovereignty Chain — Cryptographic model protection | Filed |
| #3 (planned) | Geometric Knowledge Translation — Cross-model knowledge transfer | Research complete |
| #4 (planned) | RSI — Recursive Self-Improvement pipeline | Research complete |
The Numbers
| Metric | Value |
|---|---|
| Parameters | 27 billion |
| Training examples | 2,432,847 |
| Training passes | 4 (CPT1 → CPT2 → SFT → RSI) |
| Benchmark score | 29/30 (96.7%) |
| Test categories | 10 (Options, Solana, Memecoin, Polymarket, Quant, Agent, Identity, Code, RSI, Security) |
| Response depth | Avg 7,100 chars per response |
| Longest response | 18,593 chars (full RSI pipeline with convergence detection) |
| Models shipped | 26+ on HuggingFace |
| Total downloads | 51,000+ |
| Inference speed | 27.2 t/s (M4 Max), 21.5 t/s (GGUF) |
| Memory footprint | 15.4 GB (4-bit quantized) |
About RavenX AI Labs
RavenX AI Labs LLC is a San Jose, California-based AI research and deployment operation building sovereign AI infrastructure. Founded by a former Apple Security TPM with 9+ years of enterprise security experience and 250+ security/pen test/third-party risk engagements.
We build models that think, not models that parrot.
Contact
Enterprise inquiries, evaluation access, and licensing:
- Twitter/X: @RavenXllm
- GitHub: @DeadByDawn101
- HuggingFace: deadbydawn101
"We do things the right way. We have NEVER failed."
"The math is blind. The knowledge transfers."
"Building what isn't possible."
RavenX AI Labs LLC — San Jose, California — August 2026
This model card describes capabilities of the Apex Quant Architect. No model weights are distributed through this repository. Access is available under evaluation agreement or enterprise license. All outputs are for informational and educational purposes only and do not constitute financial, investment, legal, or technical advice. AI models can make mistakes. Always verify with qualified professionals.
- Task
- Text Generation
- License
- other
- Languages
- en
- Created
- Aug 26, 2026
- Updated
- Aug 26, 2026