Ornith-1.0-9B - MLX int4 (complete VLM, Krill-native)
A 4-bit (MLX affine, group 64) quantization of deepreinforce-ai/Ornith-1.0-9B - a Qwen3.5-class hybrid vision-language model.
Why this build
- 👁️ Complete vision-language model - the vision tower is included. Most community MLX/4-bit Ornith quants are text-only (vision stripped). This one keeps the full VLM, so it does image + text, not just text.
- ⚡ Native Krill runtime (from Krill v0.14). Runs as a native Swift + MLX model on Apple Silicon via
krill run ornith-9b- Krill ships a from-scratch native runtime for Ornith's hybrid GatedDeltaNet (SSM) + attention decoder, not just an mlx_vlm passthrough. - ✅ Parity-verified. Token-for-token match against mlx_vlm on the reference checkpoint.
- 💻 Fits a 24 GB Apple-silicon box at ~6 GB; one-line install + self-update (
krill update).
Run in Krill (recommended)
# install Krill
brew tap srvsngh99/krill && brew install krill
# or:
curl -fsSL https://raw.githubusercontent.com/srvsngh99/Krill/main/install.sh | sh
# run Ornith (pulls this repo)
krill run ornith-9b "Give three tips for staying focused while studying."
# keep Krill up to date
krill update
Run with mlx_vlm (text + vision)
pip install -U mlx-vlm
python -m mlx_vlm generate --model srv-sngh/Ornith-1.0-9B-MLX-4bit \
--prompt "Describe this image." --image path/to/image.jpg --max-tokens 200
About Ornith-1.0-9B
A Qwen3.5-class hybrid VLM: the text decoder is a Qwen3-Next-style stack interleaving GatedDeltaNet linear-attention (SSM) layers with full softmax-attention every fourth layer, plus a vision tower. Full credit to the original creators, deepreinforce-ai.
Quantization
| field | value |
|---|---|
| format | MLX (affine) |
| bits | 4 |
| group size | 64 |
| size | ~6 GB |
| contents | complete VLM (text decoder + vision tower) |
In Krill, the text decoder runs natively; the vision tower currently runs via mlx_vlm (native vision is a follow-up). An nvfp4 build follows in Krill v0.14.1.
License
MIT, matching the base model deepreinforce-ai/Ornith-1.0-9B.
About this build
Quantized and tested by Sourav Singh for Krill, a Mac-native LLM runtime and coding agent in one Swift + MLX binary. More Apple Silicon builds: huggingface.co/srv-sngh. Notes on building with local models and frontier AI: souravailabs.ai · GitHub · LinkedIn · YouTube