asvuep/orion-atlas-7b

Orion Atlas 7B

Custom Mamba-2 Hybrid architecture built from scratch by Avery Palermini / Cendrix LLC.

Architecture

• Type: Mamba-2 Hybrid (SSM + Differential Attention)
• Parameters: ~7.1B transformer-equivalent (8.77B actual hybrid)
• Layers: 32 total
  • 25 Mamba-2 (SSD) layers
  • 7 Differential Attention layers at indices [3, 7, 11, 15, 19, 23, 27]
  • SwiGLU FFN after every layer (all 32)
• Attention heads: 32 Q / 8 KV (GQA 4:1)
• Attention type: Microsoft Differential Attention (ICLR 2025) -- full causal, no sliding window
• SSM: Mamba-2 / SSD, expand=2, d_state=128, d_conv=4
• FFN hidden dim: 14,336 (SwiGLU)
• Model dim: 4,096
• Normalization: RMSNorm
• Position encoding: RoPE (theta=500,000) on attention layers only; Mamba-2 is position-free
• Context window: 128K tokens (131,072)
• Tokenizer: Custom SentencePiece (32K vocab)
• Weight tying: embedding and output head shared

Why Hybrid?

Validated by NVIDIA (arXiv:2406.07887), Jamba (AI21), and Zamba (Zyphra):

• Mamba-2 SSM layers handle sequence modeling efficiently (O(T) vs O(T^2))
• Full-causal attention layers (7/32) provide global recall for long-context tasks
• Result: better perplexity than pure transformer at same parameter count
• 128K context without the quadratic cost of all-attention

Design Goals

Built for agentic tasks: tool calling, structured JSON output, multi-step reasoning. Part of the Orion Atlas model family (1B -> 3B -> 7B -> 14B -> 37B).

Files

• model_7b.py -- full architecture, pure PyTorch (no mamba-ssm package required)
• model.py -- original 1B Llama-style transformer (reference)

Training

• Pre-training: FineWeb-Edu, SlimPajama, StarCoder, OpenWebMath + custom datasets
• Fine-tuning: OpenClaw tool-calling traces (in progress)
• Framework: Custom PyTorch training loop

Status

Architecture released. Weights in training.

License

Apache 2.0