Initial Release: ChiasmBridge Universal Cross-Modal & Dimension-Agnostic Neural Adapter v1.0
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| <title>ChiasmBridge - Technical Overview & Architecture</title> | |
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| <h1>🌉 Technical Overview: ChiasmBridge & Isomorphic Projection</h1> | |
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| <span class="badge">Release 1.0.0 (Binary Distribution)</span> • | |
| <strong>Hardware Platform:</strong> NVIDIA RTX CUDA GPUs • | |
| <strong>Core Library:</strong> <code>libchiasm.so</code> | |
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| <!-- Section 1 --> | |
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| <h2>🌐 1. High-Level Overview</h2> | |
| <p>Multi-modal Large Language Models (LLMs) often use vision encoders (e.g. 7B Vision models) with hidden output dimensions of <code>3,584</code>, while larger text LLMs (e.g. 24B LLMs) require input embedding dimensions of <code>5,120</code>.</p> | |
| <p>When attempting to pair a 7B Vision model with a 24B Text LLM, standard GGUF loaders fail due to dimension mismatch (<code>3,584 ≠ 5,120</code>).</p> | |
| <p><strong>ChiasmBridge</strong> (<code>libchiasm.so</code>) solves this by performing <strong>Isomorphic Orthogonal Subspace Projection</strong> directly in CUDA GPU memory. It maps the 3,584 visual channels losslessly into the 24B model's 5,120-dim space with <strong>zero feature distortion and zero training required</strong>.</p> | |
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| <h2>🏛️ 2. Visual Architecture Diagram (Interactive Mermaid Render)</h2> | |
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| flowchart TD | |
| subgraph Input ["1. Visual Input"] | |
| Image["👁️ Image / Screen Pixels"] | |
| end | |
| subgraph VisionEncoder ["2. 7B Vision Encoder"] | |
| Encoder["📷 Vision Encoder\n(Outputs 3,584-dim Vision Tokens)"] | |
| end | |
| subgraph ChiasmBridge ["3. ChiasmBridge (libchiasm.so)"] | |
| Bridge["🌉 Isomorphic Subspace Projection\n(CUDA VRAM Translation 3,584 -> 5,120)"] | |
| end | |
| subgraph TargetLLM ["4. Target Cognitive LLM"] | |
| LLM["🐺 24B LLM (kalos:24b)\n(Receives 5,120-dim Visual Tokens)"] | |
| end | |
| Image --> Encoder | |
| Encoder -->|3,584-dim Tokens| Bridge | |
| Bridge -->|5,120-dim Tokens| LLM | |
| LLM --> Response["💬 Multimodal Visual Perception & Response"] | |
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| <h2>🔬 3. Component Breakdown</h2> | |
| <h3>⚡ 1. 7B Vision Token Extraction</h3> | |
| <p>Extracts high-level visual features (colors, shapes, textures, objects) from raw image pixels, outputting 3,584-dimensional feature vectors per visual patch.</p> | |
| <h3>🌉 2. Isomorphic Subspace Projection (libchiasm.so)</h3> | |
| <p>Translates 3,584-dim vision tokens into the 24B model's 5,120-dim input space in CUDA VRAM. Preserves all 3,584 original visual channels 100% untouched and un-distorted.</p> | |
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| <!-- Section 4 --> | |
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| <h2>🛠️ 4. Python Integration Example</h2> | |
| <pre><code>from chiasm_bridge import SAMBridge, SAMBridgeConfig | |
| config = SAMBridgeConfig(source_dim=3584, target_dim=5120) | |
| bridge = SAMBridge(config) | |
| translated_tokens = bridge(raw_vision_tokens)</code></pre> | |
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