AXL-Refactor-Lion

AXL-Refactor-Lion

Refactoring specialist. 19.1M params. PPL 1.02. Context 256 bytes. Part of the AXL model family by CubicLabs.

Model Details

Sources

• Repository: GitHub
• Organization: CubicLabs

Uses

Direct Use

Transforming verbose code to idiomatic Python.

import torch
from multiscale_transformer.model.model import MultiScaleTransformer
from multiscale_transformer.training.tokenizer import ByteTokenizer
ckpt = torch.load("axl_refactor_lion.pt", map_location="cpu")
model = MultiScaleTransformer(config)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
tokenizer = ByteTokenizer()
ids = torch.tensor([tokenizer.encode("def hello():")], dtype=torch.long)
with torch.no_grad():
    out = model.generate(ids, max_new_tokens=50, temperature=0.8)
print(tokenizer.decode(out[0].tolist()))

Out-of-Scope Use

Not for production code generation. Not for non-code NLP tasks. For integration with tools like Continue.dev, LlamaIndex, or LangChain, use the Python API server which provides OpenAI-compatible endpoints.

Bias, Risks, and Limitations

Byte-level perplexity is not comparable to BPE-level perplexity. Max context 256 bytes. Simple refactoring patterns only. IMPORTANT: GGUF files exported for Ollama/LM Studio use only the fine-scale encoder (1/3 of the AXL architecture). The reported PPL applies to the full multi-scale model. For full AXL quality, use the Python API server at http://localhost:8880/v1/completions.

Recommendations

• Use for prototyping and experimentation, not production code generation.
• Byte-level perplexity (258 vocab) is not comparable to BPE-level perplexity (32K vocab).
• For better results, use the Lion-optimized version if available.

Training Details

Training Data

Trained on 7MB before/after pairs. 162 steps in 10 min. Loops to comprehensions, if-else to ternary.

Preprocessing

Byte-level tokenization with vocabulary size 258 (256 bytes + BOS + EOS). No vocabulary training required.

Speeds, Sizes, Times

Evaluation

Metrics

Perplexity on held-out Python code using byte-level tokenization.

Results

Summary: Transforms verbose code to idiomatic Python.

Environmental Impact

Technical Specifications

Model Architecture

Multi-Scale Transformer with three parallel encoder stacks at resolution scales 1x, 2x, and 4x. Cross-scale attention connects all scale pairs. Adaptive gating fusion. SwiGLU feed-forward. RoPE positional encoding.

Compute Infrastructure

Citation

@misc{axl_2026,
  title={AXL: AXL-Refactor-Lion - Multi-Scale Transformer for CPU Code Generation},
  author={Cubic},
  year={2026},
  url={[https://huggingface.co/CubicLabs](https://huggingface.co/CubicLabs)}
}

How to Get Started

With Ollama

ollama create axl-refactor-lion -f Modelfile
ollama run axl-refactor-lion "def fibonacci():"

With Python

import torch
from multiscale_transformer.model.config import load_config
from multiscale_transformer.model.model import MultiScaleTransformer
from multiscale_transformer.training.tokenizer import ByteTokenizer
config = load_config("config.json")
model = MultiScaleTransformer(config)
ckpt = torch.load("axl_refactor_lion.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
tokenizer = ByteTokenizer()
prompt = "def fibonacci():"
ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)
with torch.no_grad():
    out = model.generate(ids, max_new_tokens=100, temperature=0.8, top_k=40)
print(tokenizer.decode(out[0].tolist()))