raihan-js/orch-fusion

🎼 ORCH Fusion

The Future of Code Generation is Here

One Prompt. Complete Application. Zero Iteration.

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🔥 What if you could generate an entire application from a single sentence?

💡 The Problem We're Solving

Every developer knows the pain:

• 💬 ChatGPT/Copilot → Great for snippets, but you're still copy-pasting file by file
• 🔄 Endless iteration → "Now add authentication" → "Now add dark mode" → "Now fix this bug"
• 💸 API costs → $20/month here, $100/month there, enterprise pricing everywhere
• 🖥️ Hardware requirements → "You need 80GB VRAM to run this"

✨ The ORCH Solution

Input:  "Create a React dashboard with authentication and dark mode"

Output: ├── package.json
        ├── tsconfig.json
        ├── tailwind.config.js
        ├── src/
        │   ├── app/
        │   │   ├── layout.tsx
        │   │   ├── page.tsx
        │   │   └── globals.css
        │   ├── components/
        │   │   ├── Header.tsx
        │   │   ├── Sidebar.tsx
        │   │   ├── Dashboard.tsx
        │   │   └── ThemeToggle.tsx
        │   └── context/
        │       ├── AuthContext.tsx
        │       └── ThemeContext.tsx
        └── ... (complete, working code)

One prompt. Complete project. Runs on YOUR hardware.

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🏆 Benchmark Results

Metric	ORCH-350M
Overall Score	76.6%
Code Parse Rate	95.3%
Format Correctness	93.3%
Valid package.json	80.0%
Config Files	90.0%

⚠️ Note: Traditional benchmarks like HumanEval measure code completion (finishing a function). ORCH is designed for project generation (creating entire applications) — a fundamentally different and harder task.

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🧠 Why ORCH is Different

❌ Traditional Approach Fine-tune existing models Requires 40-80GB VRAM Generates one file at a time $$$$ API costs Black box architecture

✅ ORCH Approach Trained from scratch (272M params) Runs on RTX 3060 (12GB) Generates complete projects Open source (Apache 2.0) Novel efficiency techniques

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🔬 Research Innovations

ORCH isn't just another model — it's a research platform pioneering new efficiency techniques:

🎯 DQGAS (Dynamic Quantization with Gradient-Aware Scaling)

Dynamically allocates precision bits based on weight importance during training. 4-8x memory reduction.

🔄 RKD (Recursive Knowledge Distillation)

Student becomes teacher in iterative loops, progressively refining knowledge without quality loss.

✂️ IWSP (Importance-Weighted Structural Pruning)

Prunes entire attention heads and neurons based on gradient + activation + entropy signals.

🧩 ASMoE (Adaptive Sparse Mixture of Experts)

Task-aware routing that activates only relevant experts based on input complexity.

Paper coming soon on arXiv 📄

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🏗️ Architecture

Built on modern transformer foundations with cutting-edge optimizations:

┌─────────────────────────────────────────────────────────┐
│                    ORCH-350M Architecture               │
├─────────────────────────────────────────────────────────┤
│  Parameters:     272,736,256                            │
│  Architecture:   LLaMA-style Decoder-only               │
│  Hidden Size:    1024                                   │
│  Layers:         24                                     │
│  Attention:      16 heads (GQA: 4 KV heads)            │
│  Activation:     SwiGLU                                 │
│  Normalization:  RMSNorm                                │
│  Position:       RoPE (Rotary Embeddings)               │
│  Context:        4,096 tokens                           │
│  Vocab:          2,103 tokens (code-optimized BPE)      │
└─────────────────────────────────────────────────────────┘

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🚀 Quick Start

Installation

git clone https://github.com/raihan-js/orch.git
cd orch
pip install -r requirements.txt
pip install -e .

Generate Your First Project

import torch
from orch import OrchForCausalLM
from tokenizers import Tokenizer

# Load model (runs on 8GB+ VRAM)
model = OrchForCausalLM.from_pretrained("raihan-js/orch-fusion")
model = model.cuda()

tokenizer = Tokenizer.from_file("orch-tokenizer.json")

# Generate a complete project
prompt = "<|project|>\n<|prompt|>Create a blog with markdown support<|/prompt|>\n<|tech|>"
inputs = torch.tensor([tokenizer.encode(prompt).ids]).cuda()

output = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
print(tokenizer.decode(output[0].tolist()))

Web Interface

python web/server.py --port 8000
# Open http://localhost:8000

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🗺️ Roadmap

Phase	Model	Status	Timeline
1	350M	✅ Released	Now
2	1B	🔨 In Progress	Q1 2025
3	5B	📋 Planned	Q2 2025
4	7B	📋 Planned	Q3 2025

What's Coming:

• 🤖 Multi-agent orchestration — Specialized agents for architecture, implementation, testing, review
• 📚 Self-updating knowledge — Automatically scrapes latest documentation
• 🔍 Autonomous debugging — Generates, tests, and fixes its own code
• 🌐 Full-stack generation — Frontend + Backend + Database + Deployment configs

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🌟 Why This Matters

We're proving that you don't need 100B+ parameters to build useful AI.

The future of AI isn't locked behind corporate APIs and enterprise pricing. It's:

• Open source
• Runs on consumer hardware
• Trained from scratch with novel techniques
• Accessible to everyone

ORCH is the first step toward truly autonomous software development.

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📊 Model Files

raihan-js/orch-fusion/
├── README.md
├── orch-tokenizer.json      # BPE tokenizer
├── tokenizer_config.json
└── 350m-project/
    ├── model.pt             # 1.09 GB
    ├── config.json
    └── model_info.json

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🤝 Community

• ⭐ Star the repo if you believe in open-source AI
• 🐛 Report issues on GitHub
• 💡 Contribute — PRs welcome!
• 🐦 Share — Help spread the word

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📄 Citation

@software{orch2025,
  author = {Raihan},
  title = {ORCH Fusion: Autonomous Code Generation from First Principles},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/raihan-js/orch-fusion},
  note = {Trained from scratch on consumer hardware}
}

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🔥 This is just the beginning.

Star ⭐ the GitHub repo to follow the journey.

Built with 💜 for the open-source community

Democratizing AI, one model at a time.

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_{ORCH Fusion • Apache License 2.0 • 2025}