ArchGene

Verify your LLM architecture before you waste $50K on compute.

Training an LLM costs $10K–$100K+. The #1 reason training fails? Architecture misconfiguration — hidden dimension misalignment, attention bugs, incompatible layer configurations.

ArchGene catches these issues BEFORE you spend on GPU time.

The Problem

You spend $50K on GPU cluster
↓
Start training
↓
Day 3: OOM errors, NaN outputs, training crashes
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Why? Hidden dimension not divisible by attention heads
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$50K wasted

ArchGene prevents this.

What It Does

Quick Start

# Install
pip install archgene

# Design an architecture through conversational Q&A
archgene design

# Verify your architecture BEFORE training
archgene verify --hidden 4096 --heads 32 --layers 24

# Generate runnable PyTorch code
archgene generate --session 0

# Get cost estimate
archgene cost gpt2 --gpu A100

# Check against known architectures
archgene zoo-evaluate llama2_7b

Why This Matters

• Don't waste compute: Catch bugs before GPU costs begin
• Know your bill: Estimate training cost before you start
• Validate fast: Z3 proves correctness mathematically

Use Cases

1. Building a custom LLM? Verify architecture before training
2. Fine-tuning an existing model? Check your config is valid
3. Comparing architectures? Benchmark against model zoo

CLI Examples

# Verify custom architecture
archgene verify --hidden 4096 --heads 32 --layers 24

# Cost estimation
archgene cost gpt2 --gpu H100 --batch-size 16

# List pre-trained architectures
archgene zoo-list

# Benchmark estimate
archgene benchmark llama2_7b

# Design an architecture through conversational Q&A
archgene design

# Generate runnable PyTorch code from a design
archgene generate -d 4096 -l 32 -n 16 -i 11008

Architecture Parameters

Cost Reference

Tech Stack

• Python 3.12+
• Z3 theorem prover (formal verification)
• PyTorch (code generation)
• Streamlit (optional web UI: pip install archgene[web])

Links

• PyPI
• GitHub

License

MIT