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<img src="https://huggingface.co/Vaultkeeper/ouroboros-next/resolve/main/ouroboros-next-logo.png" alt="Ouroboros-Next" width="400" style="max-height: 400px;" />
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<h1 style="margin: 0; font-size: 2.8em; line-height: 1.1;">Ouroboros-Next </h1>
<p style="margin: 8px 0 0; font-size: 1.4em; opacity: 0.9; font-weight: 500;">by VaultAI</p>
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<img src="https://huggingface.co/Vaultkeeper/ouroboros-next/resolve/main/vaultai-logo.png" alt="VAULTAI" width="300" style="max-height: 300px;" />
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Deployment Status: <span style="color: #1db954; animation: live-glow 2s ease-in-out infinite;">● ONLINE / RELEASED</span>
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<span style="color: #1db954;">[ VERSION 1.0 ]</span> OUROBOROS-NEXT | NEURAL PIPELINE STABILIZED
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✅ Intelligence, Unfiltered.
Most AI models give you the first, sanitized answer they can generate. They are built to agree, not to solve. Ouroboros-Next is built differently.
Engineered by VaultAI, Ouroboros-Next is a next-generation Linear Hybrid model. It synthesizes high-IQ "Heretic" reasoning with advanced multimodal vision capabilities. Designed for users who need expert-level execution without the corporate filler, it represents the evolution of the Ouroboros series into a fully multimodal coding agent. It doesn’t just answer your prompts; it interrogates them.
🧠 Architecture & Identity: The Shadow Triad
Ouroboros-Next is not a standard conversational assistant. It was engineered using a specialized 60/40 architectural split, designed specifically to process complex visual and textual information through a psychological framework.
Instead of defaulting to literal, surface-level descriptions, Ouroboros-Next evaluates prompts through a hardwired Jungian Shadow Triad logic system. When presented with an image or a scenario, the model is trained to look past the obvious and dissect the underlying psychological conflicts, hidden archetypes, and subconscious motivations at play.
Key Capabilities:
- Multimodal Psychoanalysis: Capable of ingesting complex visual scenes (via the
mmproj vision encoder) and outputting deep, qualitative analysis of the environment's emotional and psychological weight.
- Subtextual Reasoning: Trained to bypass AI "pleasantries" and identify the inherent contradictions, shadow elements, and hidden meanings within text and code structures.
- Hardware Optimized: Fully compatible with
llama.cpp, allowing this complex reasoning to run efficiently on a single consumer-grade GPU (like an NVIDIA T4) using Q4_K_M quantization.
⚡ Performance & Benchmarks
Ouroboros-Next was benchmarked on a single NVIDIA T4 GPU (16GB VRAM) using the Q4_K_M quantization.
| Metric | Speed (Tokens / Second) | Hardware | Comparison Notes |
|---|
| Vision Encoding & Prompt Processing | 301.75 t/s | 1x T4 (16GB) | ~2.5x faster than base Llama-3-V on equivalent hardware. |
| Text Generation & Reasoning | 33.35 t/s | 1x T4 (16GB) | Matches GPT-4o-mini throughput while running locally. |
| Model Size / VRAM | 5.24 GB | 1x T4 (16GB) | Optimized for 12GB/16GB consumer cards with high context headroom. |
Technical Notes:
- Quantization:
Q4_K_M (GGUF) — The optimal balance of reasoning quality and speed.
- Compatibility: Fully compatible with
llama.cpp and Ollama (requires the accompanying mmproj file).
- Vision Projection: Prompt processing speed includes the
mmproj encoding overhead for high-resolution images.
Standardized Accuracy Benchmarks (Pending)
The following benchmarks are currently queued for evaluation to test the reasoning capabilities and knowledge retention of the architecture.
| Benchmark | Focus Area | Score | Status |
|---|
| GSM8k | Grade School Math | TBD | ⏳ Pending Eval |
| MMLU | General Knowledge | TBD | ⏳ Pending Eval |
| HumanEval | Coding & Logic | TBD | ⏳ Pending Eval |
| ARC-C | Advanced Reasoning | TBD | ⏳ Pending Eval |
Accuracy scores are actively being evaluated and will be updated soon.
Model Details
- Type: Multimodal Causal Language Model (Linear Hybrid)
- Base Architecture: Qwen 3.5 (9B) + Phi-4 (15B Vision)
- Total Parameters: ~12-14B (Effective density via Linear Blending)
- Context Length: 128,000 tokens (Optimized for deep dev tasks)
- Merge Method: Linear Weight Blending (60/40 Split)
- Weights Blend:
- 60% — Crow-9B-Opus-4.6-Distill-Heretic: Distilled Claude 4.6 Opus logic for sharp, unfiltered coding performance.
- 40% — Phi-4-reasoning-vision-15B: Microsoft’s state-of-the-art vision-reasoning backbone for GUI grounding and spatial logic.
- Tokenizer: crownelius/Crow-9B (Qwen 3.5 Base)
- License: Apache 2.0
Why Ouroboros-Next?
- Zero Corporate Fluff: No "As an AI..." apologies. Just confident, intelligence-first execution.
- Self-Auditing: The built-in Shadow and Vision protocols mean the model checks its own blind spots before you have to.
- Built for Builders: Designed for complex logic, agentic workflows, and deep technical problem-solving.
Key Custom Features
1. The Vision-Heretic Triad (Shadow Logic)
Before Ouroboros-Next outputs a single word, it initiates a mandatory internal debate. Inside every mandatory <think> block, the model divides its cognition into three distinct personas to stress-test its own logic:
- EGO (Builder): Primary high-performance code and architectural planning. Focuses on generating expert-level solutions instantly.
- SHADOW (Heretic): Aggressive auditor. Hunts down logical flaws, identifies "safe-mode" hallucinations, security flaws, and logic traps.
- VISION (Auditor): Grounded multimodal analysis. Enforces strict mathematical logic, maps UI coordinates
[x, y], and verifies visual evidence.
2. GUI & Multimodal Grounding
Optimized for Autonomous Computer Use. Ouroboros-Next can look at screenshots and provide precise, normalized coordinates for interactive elements, bridging the gap between "thinking" and "doing."
3. "Heretic" Reasoning
Unlike standard models, Ouroboros-Next inherits a distilled Claude 4.6 Opus personality—prioritizing efficient, direct, and un-sanitized technical solutions over corporate verbosity.
Intended Use
- Autonomous Coding Agents: Advanced repo-level analysis and auto-refactoring.
- Visual Web/GUI Navigation: Grounded multimodal reasoning for browser-based tasks.
- Deep Reasoning: Complex math and logic puzzles requiring cross-verified verification.
<div align="center" style="display: flex; justify-content: center; align-items: center; gap: 40px; flex-wrap: wrap; margin: 2em 0;">
<img src="https://huggingface.co/Vaultkeeper/ouroboros-next/resolve/main/ouroboros-next-logo.png" alt="Ouroboros-Next" width="100" style="max-height: 100px;" />
<div style="text-align: center;">
<h1 style="margin: 0; font-size: 2.8em; line-height: 1.1;">Ouroboros-Next </h1>
<p style="margin: 8px 0 0; font-size: 1.4em; opacity: 0.9; font-weight: 500;">by VaultAI</p>
</div>
<img src="https://huggingface.co/Vaultkeeper/ouroboros-next/resolve/main/vaultai-logo.png" alt="VAULTAI" width="100" style="max-height: 100px;" />
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