KoinicLabs/AXL-Architecture-eXperimental-Lab

AXL — Architecture eXperimental Lab

27 CPU-Optimized Code Generation Models by Koinic

All models are trained from scratch on consumer hardware (AMD Ryzen 5 5600G, 16GB RAM). No GPU required.

Models

Lion-Optimized (Recommended)

Model	Params	PPL	GGUF (F16)	GGUF (Q4_K_M)
AXL-Code-1B-Lion	318M	1.90	606 MB	188 MB
AXL-Reasoning-Lion	70M	1.03	134 MB	44 MB
AXL-Refactor-Lion	19.1M	1.02	37 MB	12 MB
AXL-TestGen-Lion	15.2M	1.02	30 MB	18 MB
AXL-Chat-Lion	9.9M	1.03	19 MB	7 MB
AXL-Micro-Lion	12.8M	1.04	25 MB	15 MB
AXL-Secure-Lion	11.7M	1.03	23 MB	8 MB
AXL-Docs-Lion	9.9M	1.01	19 MB	7 MB
AXL-Comment-Lion	7.2M	1.02	14 MB	5 MB

SGD Models

Model	Params	PPL	Focus
AXL-Micro-600K	600K	63.08	Demo
AXL-Micro-8M	12.8M	3.13	Code gen
AXL-Coder-15M	26.0M	5.97	Agentic
AXL-Debugger-8M	14.1M	6.60	Bug fixing
AXL-Fixer-12M	20.9M	5.90	Debug
AXL-Reasoning-70M	70M	1.93	CoT
AXL-300M	322M	5.98	Flagship
AXL-Chat-10M	9.9M	1.02	Dialogue
AXL-TestGen-15M	15.2M	1.01	Test gen
AXL-Refactor-20M	19.1M	1.01	Refactoring
AXL-Docs-8M	9.9M	1.03	Docstrings
AXL-Comment-5M	7.2M	1.01	Comments
AXL-Secure-10M	11.7M	1.01	Security

Specialized Models

Model	Params	PPL	Focus
AXL-Code-1B	318M	31.22	Code gen (SGD)
AXL-Chat-Pro	12.8M	3.42	Advanced chat
AXL-Translate	15.2M	1.86	Code translation
AXL-Vision-0.8M	1M	—	Vision encoder
AXL-Vision-v2	4.1M	—	UI vision

Quick Start

Python API Server (Full Quality - Recommended)

pip install -e .
python AXL/API/serve_model.py --model AXL-Micro-Lion/ --port 8880

# OpenAI-compatible endpoint:
curl http://localhost:8880/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"prompt": "def fibonacci(n):", "max_tokens": 100}'

# Works with Continue.dev, LlamaIndex, LangChain, Cursor

With Ollama (Degraded Quality)

Warning: GGUF files for Ollama use only the fine-scale encoder (1/3 of the AXL architecture). The reported PPL values apply to the full multi-scale model. Use the Python API above for full quality.

cd AXL-Micro-Lion
ollama create axl-micro-lion -f Modelfile
ollama run axl-micro-lion "def fibonacci(n):"

With Python (Direct Inference)

import torch
from multiscale_transformer.model.config import load_config, ModelConfig
from multiscale_transformer.model.model import MultiScaleTransformer
from multiscale_transformer.training.tokenizer import ByteTokenizer

config = load_config("AXL-Micro-Lion/config.json")
model = MultiScaleTransformer(config)
ckpt = torch.load("AXL-Micro-Lion/axl_micro_lion.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

tokenizer = ByteTokenizer()
ids = torch.tensor([tokenizer.encode("def hello():")], dtype=torch.long)
out = model.generate(ids, max_new_tokens=50, temperature=0.8, top_k=40)
print(tokenizer.decode(out[0].tolist()))

Architecture

AXL processes token sequences at three parallel resolution scales:

• Fine (1x): All tokens. Attention cost: O(N^2 d)
• Medium (2x): Grouped in pairs. Cost: O(N^2 d/4)
• Coarse (4x): Grouped in quadruplets. Cost: O(N^2 d/16)

Cross-scale attention connects all scale pairs. Adaptive gating fusion combines representations.

Lion optimizer: Sign-based momentum, 20x faster convergence than SGD, 50% less memory than AdamW.

Byte-level tokenizer: 258 vocab (256 bytes + BOS + EOS). No vocabulary training. Works with any programming language.

Training Cost

Model	Time	Cost (USD)
AXL-Comment-Lion	2 min	$0.0004
AXL-Code-1B-Lion	20 min	$0.004
All 9 Lion models	49 min	$0.010

Based on AMD Ryzen 5 5600G (100W system, $0.12/kWh).

Papers

• Research Paper (PDF)
• Research Paper (LaTeX)

Code

Full training code: GitHub