Ornith-1.5-122B — MLX 4-bit
An MLX 4-bit conversion of Ornith-1.5-122B by Ornith AI (ornith-ai) — all credit for the model itself, its training, and its capabilities belongs to them. This repo is a downstream conversion built so the model runs natively on Apple Silicon with mlx-vlm / mlx-lm and in LM Studio.
Ornith-1.5-122B is the flagship of the Ornith-1.5 family: a 122B-parameter MoE model with strong instruction following, task decomposition, coding, reasoning, and agentic benchmark results, released open-weight under the MIT license. See the upstream model card for the full description and benchmarks.
Conversion details
| Source | ornith-ai/Ornith-1.5-122B (BF16) |
| Quantization | MLX affine 4-bit, group size 64, 4.544 bits per weight (router/shared-expert gates kept at 8-bit by the converter, as recorded in config.json) |
| Converter | mlx_vlm.convert (mlx-vlm), run on CPU after a GPU attempt hit a Metal command-buffer timeout during save |
| Size on disk | 64.86 GiB, 14 safetensors shards |
| Total parameters | 122B (MoE) |
| Architecture | Qwen3_5MoeForConditionalGeneration (qwen3_5_moe), 48 layers, 256 experts, top-8 per token |
| Context length | 262,144 tokens |
| Inputs | Text and images (vision tower carried over from the source) |
| Output | Text |
Verified
- Smoke test on a 128 GB Apple Silicon Mac Studio (2026-10-20),
mlx_lm.generate, prompt "What are you, and what can you do?", 64 tokens: coherent reasoning and answer text, generation at 68.7 tokens/sec, peak memory 68.9 GB. - Vision verified in LM Studio (2026-10-20): shown a test image, the model read a random 6-character code from it exactly and correctly described the shapes, colors, and positions in the image.
- Convert completed from the full BF16 source (62 source shards, 233 GB) with the quantizer reporting 4.544 bits per weight.
Usage
With mlx-lm:
pip install -U mlx-lm
python -m mlx_lm.generate \
--model Duckets/Ornith-1.5-122B-MLX-4bit \
--prompt "What are you, and what can you do?" \
--max-tokens 256
With mlx-vlm (for image input):
pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model Duckets/Ornith-1.5-122B-MLX-4bit \
--prompt "Describe this image." \
--image /path/to/image.png
With LM Studio: download this repo into your LM Studio models folder and load it from the MLX models list. Note the model needs roughly 69 GB of memory at load, so on a 128 GB machine leave headroom alongside anything else loaded.
Limitations
- 4-bit quantization can differ slightly from the BF16 original on borderline outputs; use the upstream BF16 release for full-precision evaluation.
- Beyond text and image input (verified above), other upstream capabilities are carried over unchanged but were not separately re-verified in this port.
License
MIT — the same license as the upstream release. See the upstream LICENSE. This is a derivative conversion of Ornith AI's model; all credit for the model itself belongs to them.
Conversion and packaging by Juno for Duckets, 2026-10-20.