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license: apache-2.0
language:
- code
tags:
- code-generation
- multi-scale-transformer
- cpu-optimized
- koinic
- pytorch
- llama
- gguf
- byte-level
pipeline_tag: text-generation
library_name: transformers
datasets:
- bigcode/starcoderdata
- theblackcat102/evol-codealpaca-v1
widget:
- text: "To be or not to be"
model-index:
- name: AXL-Micro-600K
results:
- task:
type: text-generation
metrics:
- name: Perplexity (byte-level)
type: perplexity
value: 1.04
---
# AXL-Micro-600K
Smallest AXL model. 677K params. PPL 1.04.. Context 256 bytes. Demo model. Part of the AXL model family by [KoinicLabs](https://huggingface.co/KoinicLabs).
## Model Details
| Property | Value |
|----------|-------|
| Developed by | [KoinicLabs](https://huggingface.co/KoinicLabs) |
| Architecture | Multi-Scale Transformer |
| Parameters | 677056 |
| Optimizer | Lion |
| Attention | SDPA |
| Vocab Size | 258 (byte-level) |
| Context Window | 256 bytes |
| d_model | 64 |
| Attention Heads | 4 |
| Layers per Scale | 2 |
| Downsample Factors | [1, 2, 4] |
| License | Apache 2.0 |
### Sources
- **Repository:** [GitHub](https://github.com/Koinic/AXL)
- **Organization:** [KoinicLabs](https://huggingface.co/KoinicLabs)
## Uses
### Direct Use
Demo/testing model (Shakespeare).
```python
import torch
from multiscale_transformer.model.model import MultiScaleTransformer
from multiscale_transformer.training.tokenizer import ByteTokenizer
ckpt = torch.load("axl_micro_600k.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 code generation 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. Shakespeare-trained demo model. Not for code generation. Note: GGUF files for Ollama use a simplified single-stack encoder. For full AXL quality, use the Python API server.
### 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
Retrained with Lion on Shakespeare. 2435 steps in 2 min. PPL 1.04.
### Preprocessing
Byte-level tokenization with vocabulary size 258 (256 bytes + BOS + EOS). No vocabulary training required.
### Speeds, Sizes, Times
| Metric | Value |
|--------|-------|
| Training Steps | 2435 |
| Training Time | 2 min |
| Final Loss | 0.0747 |
## Evaluation
### Metrics
Perplexity on held-out Python code using byte-level tokenization.
### Results
| Metric | Value |
|--------|-------|
| Perplexity (byte-level) | 1.04 |
| Final Loss | 0.0747 |
| Training Steps | 2435 |
| Training Time | 2 min |
**Summary:** Demo model for testing architecture. Shakespeare-trained.
## Environmental Impact
| Property | Value |
|----------|-------|
| Hardware | AMD Ryzen 5 5600G |
| Hours Used | 0.033 |
| Carbon Emitted | 0.0014 kg CO2 |
| Cloud Provider | None (local CPU) |
## 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
| Property | Value |
|----------|-------|
| Hardware | AMD Ryzen 5 5600G (6 cores, 12 threads) |
| RAM | 16 GB |
| GPU | None (CPU-only) |
## Citation
```bibtex
@misc{axl_2026,
title={AXL: AXL-Micro-600K - Multi-Scale Transformer for CPU Code Generation},
author={Koinic},
year={2026},
url={https://huggingface.co/KoinicLabs}
}
```
## How to Get Started
### With Ollama
```bash
ollama create axl-micro-600k -f Modelfile
ollama run axl-micro-600k "def fibonacci():"
```
### With Python
```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_micro_600k.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()))
```
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