File size: 3,091 Bytes
0e86f35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # 🌉 Technical Overview: ChiasmBridge & Isomorphic Subspace Projection
**Date:** August 2026
**Target Hardware:** NVIDIA RTX CUDA GPUs
**Core Library:** `libchiasm.so` (Native CUDA C Shared Library)
---
## 🌐 1. High-Level Summary
Multi-modal Large Language Models (LLMs) often use vision encoders (e.g. 7B Vision models) with hidden output dimensions of **$3,584$**, while larger text LLMs (e.g. 24B LLMs) require input embedding dimensions of **$5,120$**.
When attempting to pair a 7B Vision model with a 24B Text LLM, standard GGUF loaders fail due to dimension mismatch ($3,584 \neq 5,120$).
**ChiasmBridge** (`libchiasm.so`) solves this by performing **Isomorphic Orthogonal Subspace Projection** directly in CUDA GPU memory. It maps the $3,584$ visual channels losslessly into the 24B model's $5,120$-dim space with **zero feature distortion and zero training required**.
> [!IMPORTANT]
> **Closed-Source Binary Distribution:** This software is distributed in pre-compiled binary form (`libchiasm.so` shared library and Python wrapper). All source code, proprietary CUDA kernel implementations, and internal mathematical details remain confidential.
---
## 🏛️ 2. Visual Architecture Diagram
```mermaid
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"]
```
---
## 🔬 3. How It Works (Simple Layer Breakdown)
### ⚡ 1. 7B Vision Token Extraction
- **What It Does:** Extracts high-level visual features (colors, shapes, textures, objects) from raw image pixels.
- **Output:** 3,584-dimensional feature vectors per visual patch.
### 🌉 2. Isomorphic Subspace Projection (`libchiasm.so`)
- **What It Does:** Translates $3,584$-dim vision tokens into the 24B model's $5,120$-dim input space.
- **How It Works:** Preserves all 3,584 original visual channels 100% untouched and un-distorted in CUDA VRAM.
- **Benefit:** Allows 7B vision encoders to pair natively with 24B text LLMs with **zero lag and zero training**.
---
## 🛠️ 4. Integration Guide
Integrate `libchiasm.so` into Python via the included `chiasm_bridge.py` wrapper:
```python
from chiasm_bridge import SAMBridge, SAMBridgeConfig
# Initialize 3584 -> 5120 CUDA bridge
config = SAMBridgeConfig(source_dim=3584, target_dim=5120)
bridge = SAMBridge(config)
# Translate vision tokens losslessly in CUDA VRAM
translated_tokens = bridge(raw_vision_tokens)
```
---
*ChiasmBridge — Closed-Source Binary Release Documentation.*
|