| # AXL — Architecture eXperimental Lab
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| **27 CPU-Optimized Code Generation Models** by [Koinic](https://koinic.ai)
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| All models are trained from scratch on consumer hardware (AMD Ryzen 5 5600G, 16GB RAM). No GPU required.
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| ## Models
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| ### Lion-Optimized (Recommended)
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| | Model | Params | PPL | GGUF (F16) | GGUF (Q4_K_M) |
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| |-------|--------|-----|-----------|---------------|
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| | [AXL-Code-1B-Lion](AXL-Code-1B-Lion) | 318M | **1.90** | 606 MB | 188 MB |
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| | [AXL-Reasoning-Lion](AXL-Reasoning-Lion) | 70M | **1.03** | 134 MB | 44 MB |
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| | [AXL-Refactor-Lion](AXL-Refactor-Lion) | 19.1M | 1.02 | 37 MB | 12 MB |
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| | [AXL-TestGen-Lion](AXL-TestGen-Lion) | 15.2M | 1.02 | 30 MB | 18 MB |
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| | [AXL-Chat-Lion](AXL-Chat-Lion) | 9.9M | 1.03 | 19 MB | 7 MB |
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| | [AXL-Micro-Lion](AXL-Micro-Lion) | 12.8M | 1.04 | 25 MB | 15 MB |
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| | [AXL-Secure-Lion](AXL-Secure-Lion) | 11.7M | 1.03 | 23 MB | 8 MB |
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| | [AXL-Docs-Lion](AXL-Docs-Lion) | 9.9M | 1.01 | 19 MB | 7 MB |
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| | [AXL-Comment-Lion](AXL-Comment-Lion) | 7.2M | 1.02 | 14 MB | 5 MB |
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| ### SGD Models
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| | Model | Params | PPL | Focus |
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| |-------|--------|-----|-------|
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| | [AXL-Micro-600K](AXL-Micro-600K) | 600K | 63.08 | Demo |
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| | [AXL-Micro-8M](AXL-Micro-8M) | 12.8M | 3.13 | Code gen |
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| | [AXL-Coder-15M](AXL-Coder-15M) | 26.0M | 5.97 | Agentic |
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| | [AXL-Debugger-8M](AXL-Debugger-8M) | 14.1M | 6.60 | Bug fixing |
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| | [AXL-Fixer-12M](AXL-Fixer-12M) | 20.9M | 5.90 | Debug |
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| | [AXL-Reasoning-70M](AXL-Reasoning-70M) | 70M | 1.93 | CoT |
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| | [AXL-300M](AXL-300M) | 322M | 5.98 | Flagship |
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| | [AXL-Chat-10M](AXL-Chat-10M) | 9.9M | 1.02 | Dialogue |
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| | [AXL-TestGen-15M](AXL-TestGen-15M) | 15.2M | 1.01 | Test gen |
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| | [AXL-Refactor-20M](AXL-Refactor-20M) | 19.1M | 1.01 | Refactoring |
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| | [AXL-Docs-8M](AXL-Docs-8M) | 9.9M | 1.03 | Docstrings |
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| | [AXL-Comment-5M](AXL-Comment-5M) | 7.2M | 1.01 | Comments |
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| | [AXL-Secure-10M](AXL-Secure-10M) | 11.7M | 1.01 | Security |
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| ### Specialized Models
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| | Model | Params | PPL | Focus |
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| |-------|--------|-----|-------|
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| | [AXL-Code-1B](AXL-Code-1B) | 318M | 31.22 | Code gen (SGD) |
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| | [AXL-Chat-Pro](AXL-Chat-Pro) | 12.8M | 3.42 | Advanced chat |
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| | [AXL-Translate](AXL-Translate) | 15.2M | 1.86 | Code translation |
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| | [AXL-Vision-0.8M](AXL-Vision-0.8M) | 1M | — | Vision encoder |
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| | [AXL-Vision-v2](AXL-Vision-v2) | 4.1M | — | UI vision |
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| ## Quick Start |
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| ### Python API Server (Full Quality - Recommended) |
| ```bash |
| pip install -e . |
| python AXL/API/serve_model.py --model AXL-Micro-Lion/ --port 8880 |
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| # OpenAI-compatible endpoint: |
| curl http://localhost:8880/v1/completions \ |
| -H "Content-Type: application/json" \ |
| -d '{"prompt": "def fibonacci(n):", "max_tokens": 100}' |
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| # Works with Continue.dev, LlamaIndex, LangChain, Cursor |
| ``` |
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| ### 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. |
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| ```bash |
| cd AXL-Micro-Lion |
| ollama create axl-micro-lion -f Modelfile |
| ollama run axl-micro-lion "def fibonacci(n):" |
| ``` |
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| ### With Python (Direct Inference) |
| ```python
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| import torch
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| from multiscale_transformer.model.config import load_config, ModelConfig
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| from multiscale_transformer.model.model import MultiScaleTransformer
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| from multiscale_transformer.training.tokenizer import ByteTokenizer
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| config = load_config("AXL-Micro-Lion/config.json")
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| model = MultiScaleTransformer(config)
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| ckpt = torch.load("AXL-Micro-Lion/axl_micro_lion.pt", map_location="cpu")
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| model.load_state_dict(ckpt["model_state_dict"])
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| model.eval()
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| tokenizer = ByteTokenizer()
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| ids = torch.tensor([tokenizer.encode("def hello():")], dtype=torch.long)
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| out = model.generate(ids, max_new_tokens=50, temperature=0.8, top_k=40)
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| print(tokenizer.decode(out[0].tolist()))
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| ```
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| ## Architecture
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| AXL processes token sequences at three parallel resolution scales:
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| - **Fine (1x)**: All tokens. Attention cost: O(N^2 d)
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| - **Medium (2x)**: Grouped in pairs. Cost: O(N^2 d/4)
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| - **Coarse (4x)**: Grouped in quadruplets. Cost: O(N^2 d/16)
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| Cross-scale attention connects all scale pairs. Adaptive gating fusion combines representations.
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| **Lion optimizer**: Sign-based momentum, 20x faster convergence than SGD, 50% less memory than AdamW.
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| **Byte-level tokenizer**: 258 vocab (256 bytes + BOS + EOS). No vocabulary training. Works with any programming language.
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| ## Training Cost
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| | Model | Time | Cost (USD) |
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| |-------|------|-----------|
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| | AXL-Comment-Lion | 2 min | $0.0004 |
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| | AXL-Code-1B-Lion | 20 min | $0.004 |
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| | All 9 Lion models | 49 min | $0.010 |
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| Based on AMD Ryzen 5 5600G (100W system, $0.12/kWh).
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| ## Papers
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| - [Research Paper (PDF)](paper_axl.pdf)
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| - [Research Paper (LaTeX)](paper_axl.tex)
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| ## Code
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| Full training code: [GitHub](https://github.com/koinic/axl)
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