Ornith-1.0-9B · JANG_4M
Official OsaurusAI JANG_4M build of deepreinforce-ai/Ornith-1.0-9B (MIT) — a vision-language model on a Qwen3.5 hybrid backbone. Mixed-precision affine (JANG_4M); runs on Apple Silicon via Osaurus.
- ~6.3 GB (from ~18.8 GB bf16) bundle.
- JANG_4M: 8-bit affine attention + 4-bit affine MLP (mixed precision, group-size 64); embeddings/head per-module; vision tower kept fp16.
- Vision-language (image + text → text).
Architecture
| Family | qwen3_5 (dense, hybrid) |
| Text layers | 32 — 24 Gated-DeltaNet (linear-attention) + 8 full-attention |
| MoE / dims | hidden 4096 · untied lm_head |
| Vision | ViT tower (model.visual) preserved fp16 |
| Cache | hybrid (GDN state + KV for attention layers) |
| Parsers | reasoning qwen3 · tools qwen |
Running it
JANG bundles use the Qwen3.5 RMSNorm +1 scale_shift applied at runtime, so load them in Osaurus (or the vMLX runtime), which handles it automatically:
osaurus run OsaurusAI/Ornith-1.0-9B-JANG_4M
Note: a plain
mlx_lm.generatewill not be coherent on a JANG bundle — it omits the +1 norm shift. Use the Osaurus / vMLX runtime (orvmlx_engine.loaders.load_jang), which applies it.
Provenance
- Base: deepreinforce-ai/Ornith-1.0-9B © DeepReinforce — MIT (Qwen3.5-based)
- Quantization: Osaurus · JANG_4M (8-bit affine attention + 4-bit affine MLP, group-size 64; vision tower fp16) · eric@osaurus.ai