SLM Ensemble
The scaling hypothesis is dead—long live specialists. Our orchestra of 8 models (7B-13B each), each a virtuoso in its domain, outperforms the 70B generalists at a fraction of the cost. Think less 'genius polymath,' more 'well-coordinated team of experts.'
Why Small Specialists Beat Big Generalists
Here's the thing about trillion-parameter models: they're expensive, they're slow, and they still think reinterpret_cast is a Harry Potter spell. The industry keeps adding more parameters hoping that intelligence will magically emerge. We respectfully disagree.
Our approach is different. Instead of training one massive model on everything from Shakespeare to StackOverflow, we train specialized models that are experts in exactly one thing: C++. Every parameter pulls its weight. No cognitive budget wasted on Python indentation rules or JavaScript callback hell.
The result? A 13B model that outperforms 70B generalists on C++ tasks, runs on consumer hardware, and actually understands that std::unique_ptr and std::shared_ptr are fundamentally different philosophies, not interchangeable types.
But we went further. Instead of one model doing everything, we have an ensemble. A code generator that's never seen a debugger session. A debugger specialist that's never tried to write new code. An orchestrator that knows which expert to call. Division of labor, but for neural networks.
Eight Specialists, One Mission
Each model is trained on different data, for different tasks. Together, they cover the full spectrum of C++ development.
C++ SLM
Trained on 200B tokens of high-quality C++ from LLVM, Chromium, and Linux kernel. Masters templates, concepts, and modern C++20/23 features.
CMake SLM
CMakeLists.txt generation, target management, cross-compilation. Knows the difference between PUBLIC, PRIVATE, and INTERFACE better than you do.
Debug SLM
Integrated with gdb and rr. Reads stack traces, understands core dumps, interprets UBSan/ASan output. Your debugging copilot that's actually been in the trenches.
Shell SLM
Shell scripting, command-line tools, CI/CD pipelines, Makefile expertise. Bridges the gap between your code and your build infrastructure.
Orchestrator SLM
The conductor of our orchestra. Analyzes your request and routes to the right specialist. Sometimes that means one model, sometimes a coordinated ensemble.
Design SLM
SOLID principles, API design, refactoring strategies. Modernize C++11 to C++23. Understands RAII, move semantics, and why your copy constructor is implicitly deleted.
Review SLM
Trained on security advisories and CVE databases. Spots buffer overflows, use-after-free, and that subtle race condition you didn't see coming.
Algorithm SLM
Trained on algorithm textbooks, competitive programming, and pseudocode. Converts algorithms between pseudocode and C++, analyzes complexity, suggests optimal data structures.
The Orchestrator Pattern
When you send a request, it first hits the Orchestrator—a lightweight classifier that determines what you're actually asking for. Code completion? Routed to CodeGen. "Why is this segfaulting?" Routed to Debug. "Explain this template." Explain gets it.
Sometimes the answer requires collaboration. "Fix this bug and explain what was wrong" gets split: Debug analyzes the issue, CodeGen generates the fix, Explain writes the postmortem. The Orchestrator coordinates the handoff and assembles the final response.
Request Flow
// User: "Why does this crash?"
void process(std::vector<int>& data) {
for (int i = 0; i <= data.size(); i++) { // Bug: <= should be <
std::cout << data[i] << "\n";
}
}
// Orchestrator → Debug-7B
// Debug-7B analyzes with gdb context:
// Response:
// The crash is an out-of-bounds access at line 3.
//
// Root cause: The loop condition uses <= instead of <.
// When i == data.size(), you're reading past the vector's end.
// This is undefined behavior - sometimes it crashes, sometimes
// it reads garbage, sometimes it works (the worst outcome).
//
// Fix: Change i <= data.size() to i < data.size()
//
// Or better, use a range-based for loop:
// for (const auto& val : data) { std::cout << val << "\n"; }100 Billion Tokens of Pure C++
No Python. No JavaScript. Just C++, its build systems, and its debugging tools.
What We Trained On
- LLVM/Clang — compiler internals, AST manipulation, codegen
- Chromium — massive codebase, multi-platform, modern C++
- Linux Kernel — low-level, performance-critical, C-style C++
- Boost — template metaprogramming at its finest (and worst)
- Qt/KDE — GUI frameworks, signal/slot patterns, MOC magic
Debugger Integration
Our Debug-7B model was trained on real debugging sessions:
- gdb sessions — breakpoints, watchpoints, memory inspection
- rr recordings — time-travel debugging, reverse execution
- Core dumps — crash analysis, stack trace interpretation
- ASan/TSan/UBSan — sanitizer output interpretation
std::vector Is One Token
Generic tokenizers are trained on natural language. They split std::vector into ["std", "::", "vector"] — three tokens for one of the most common types in C++. Operators like << become ["<", "<"]. Template syntax becomes a token explosion.
Our tokenizer is trained specifically on C++. Common patterns get single tokens:
std::vector→ 1 token (not 3)std::unique_ptr→ 1 token (not 4)std::move→ 1 token::→ 1 token (scope resolution)<<→ 1 token (stream insertion)->→ 1 token (member access)
The result? 40% fewer tokens for the same code. More context in the same window. Faster inference. Lower costs. And a model that sees C++ the way you do.
✗ Generic Tokenizer
std::vector<std::unique_ptr<Node>> → ["std", "::", "vector", "<", "std", "::", "unique", "_", "ptr", "<", "Node", ">", ">"] → 13 tokens
✓ C++ Tokenizer
std::vector<std::unique_ptr<Node>> → ["std::vector", "<", "std::unique_ptr", "<", "Node", ">>"] → 6 tokens
Specialists vs. Generalists
Accuracy on C++ specific tasks. Our 7-13B models vs. 70B+ generalists.
| Task | SLM Ensemble | GPT-4 | CodeLlama-70B |
|---|---|---|---|
| Code Completion | 94% | 89% | 82% |
| Bug Detection | 91% | 78% | 71% |
| Template Expansion | 97% | 72% | 65% |
| Memory Safety Analysis | 88% | 69% | 58% |
| Build Error Explanation | 95% | 81% | 74% |
* Benchmarked on internal C++ evaluation suite. Template Expansion includes variadic templates, SFINAE, and concepts.
Ready for AI That Speaks C++?
Whether you're debugging a segfault, modernizing legacy code, or just trying to understand what that template error message actually means—we've got you covered.