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Shiftedx/ornith-1.5-9b-mxfp4-vision-mlx

🤗 Hugging Face sourceimage-text-to-textmit9.4B params19 GBsafetensors✓ Checksums witnessedupdated today
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ornith-1.5-9b-mxfp4-vision-mlx

Standard group-32 MXFP4 language quant with the same-parent BF16 vision tower. Built from ornith-ai/Ornith-1.5-9B revision 98db59b for Apple Silicon.

Format

  • Logical size: 5.69 GB
  • Dense Qwen3.5 9B language trunk: 250 uniform MXFP4 group-32 modules, including recurrent inputs
  • Vision: 333 same-parent BF16 tensors
  • Context metadata: 262,144 tokens
  • Runtime: autoregressive MLX-LM/MLX-VLM
  • Native MTP: unavailable; the upstream checkpoint contains no mtp.* tensors

Exact precision and conversion records are included in BUILD_RECIPE.json and conversion_receipt.json. Immutable weight revision: d8f35fe.

Usage

python -m mlx_vlm.generate \
  --model Shiftedx/ornith-1.5-9b-mxfp4-vision-mlx \
  --image image.jpg \
  --prompt "Describe this image." \
  --max-tokens 256

Qualification

ShiftedX Bench v0.3.0 commit 3bbb0bfa01e33503163cb34ef52b4d507e456265, Apple M4 Max / 64 GiB, thinking enabled, medium reasoning, temperature 1, top-p 0.95, top-k 20, KV cache off:

Lane Result Mean decode Peak active memory
Quality 5/10 70.9 tok/s 4.76 GB
Long context 4/15 66.6 tok/s 4.76 GB
Tools 6/6 68.8 tok/s 5.86 GB
Agentic 0/2 — —
Vision, native MLX-VLM 2/4 strict; 4/4 semantic — —

Text lanes used MTPLX 2.7.1 in stock autoregressive mode. Vision used native MLX-VLM because MTPLX AR rejects image content. The two strict vision misses were correct answers wrapped in Markdown fences. Structural loading and deterministic text/vision smokes passed. Quantization can change behavior; see the parent model card for intended use and license details.