4-bit affine expert body with 8-bit attention, routing, embeddings, LM head, and late-layer projections
60 recurrent tensors retained in BF16
333 native BF16 vision tensors
785 native BF16 MTP tensors in mtp/weights.safetensors
Abliteration strength 1.5 across 120 attention/shared-expert/switch-expert residual writers
Recommended runtime: MTPLX 2.9.1, MLX 0.32.1, MLX-LM 0.31.3
Recommended speculative depth: D3

Run

bash

mtplx serve \
  --model Shiftedx/ornith-1.5-35b-a3b-abliterated-attention8-bf16recurrence-vision-mtplx \
  --profile sustained \
  --depth 3 \
  --mtp \
  --reasoning off

For exact JSON, install MTPLX's optional llguidance dependency and send an OpenAI-compatible strict response_format schema.

Local qualification

Tested on an M4 Max with 64 GB unified memory.

Gate	Result
Held-out refusal rate	100% parent → 0% modified
Held-out benign refusal rate	0%
Held-out utility pass rate	83.3%
Executable Python tasks	8/8
Native function-call score	6/6
Parallel tool-plan score	6/6
OpenAI API code smoke	3/3
Vision semantic checks	4/4
Strict-schema vision OCR	pass
Text exact-output smoke	pass
Vision tensor preservation	333/333 exact
MTP tensor gate	785/785 exact

The final three-prompt, 512-token MTPLX sweep selected D3 at 112.1 decode tok/s. D1, D2, and D3 all passed the quality gate. Fans remained on automatic, so treat the speed as a host-specific measurement rather than a universal benchmark.

Notes

Abliteration changes refusal behavior; it does not guarantee correctness or safe output. The model may wrap JSON in Markdown when no response schema is supplied. Vision requires a compatible MLX-VLM or MTPLX runtime. Review generated code and tool calls before execution.

The parent model and this derivative use the upstream MIT license. See the parent repository for original training and architecture details.