README.md

---
license: apache-2.0
language:
  - code
tags:
  - code-generation
  - multi-scale-transformer
  - cpu-optimized
  - cubic
  - pytorch
  - llama
  - gguf
  - byte-level
  - conversational
pipeline_tag: text-generation
library_name: transformers
datasets:
  - cubic/axl-chat-pairs
widget:
  - text: "User: How do I read a CSV in Python?\nAssistant:"
model-index:
  - name: AXL-Chat-10M
    results:
      - task:
          type: text-generation
        metrics:
          - name: Perplexity (byte-level)
            type: perplexity
            value: 1.02
---

# AXL-Chat-10M

Conversational AI. 9.9M params. PPL 1.02. Context 512 bytes. Part of the AXL model family by [CubicLabs](https://huggingface.co/CubicLabs).

## Model Details

| Property | Value |
|----------|-------|
| Developed by | [CubicLabs](https://huggingface.co/CubicLabs) |
| Architecture | Multi-Scale Transformer |
| Parameters | 10M |
| Optimizer | Lion |
| Attention | SDPA |
| Vocab Size | 258 (byte-level) |
| Context Window | 512 bytes |
| d_model | 224 |
| Attention Heads | 4 |
| Layers per Scale | 3 |
| Downsample Factors | [1, 2, 4] |
| License | Apache 2.0 |

### Sources

- **Repository:** https://github.com/Cubic/AXL
- **Organization:** https://huggingface.co/CubicLabs

## Uses

### Direct Use

Conversational AI for programming Q&A.

Example Usage:
import torch
from multiscale_transformer.model.model import MultiScaleTransformer
from multiscale_transformer.training.tokenizer import ByteTokenizer
ckpt = torch.load("axl_chat_10m.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 512 bytes. 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 10MB chat pairs. 216 steps in 10 min. Covers code Q&A, general knowledge.

### Preprocessing

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

## Evaluation

### Metrics

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

### Results

Perplexity (byte-level): 1.02
Final Loss: 0.3650
Training Steps: 216
Training Time: 10 min

## Environmental Impact

Hardware: AMD Ryzen 5 5600G
Hours Used: 0.167
Carbon Emitted: 0.0070 kg CO2
Cloud Provider: None (local CPU)

## Citation

@misc{axl_2026,
  title={AXL: AXL-Chat-10M - Multi-Scale Transformer for CPU Code Generation},
  author={Cubic},
  year={2026},
  url={https://huggingface.co/CubicLabs}
}

## How to Get Started

### With Ollama

ollama create axl-chat-10m -f Modelfile
ollama run axl-chat-10m "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_chat_10m.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()))