Ornith-1.0-35B-mlx-uniform-4bit

Ornith-1.0-35B-mlx-uniform-4bit

Model-card update:2026-09-14.

Status

Third choice on the operator-approved coding menu (B3); research/design shortlist only, not a C pick. Retain native expert routing, temperature 0.4 and the certified transplanted MTP head. Later MBPPPlus tail costs qualify the original predictor result.

Conversion and attribution

MLX affine conversion of deepreinforce-ai/Ornith-1.0-35B, MIT licensed: a 35B hybrid-attention MoE with 256 routed experts and 8 selected per token. Historical serving metadata reports 4.019 effective bits/weight, 20.4 GB weights, 432 four-bit and 80 eight-bit quantized modules. Thus the uniform 4 name does not mean every module uses four bits. The fine-tuned trunk and its separately transplanted base-model predictor have different provenance and licenses.

Packed low-bit tensor counts can understate true parameter counts in hub badges. Weight-file sizes, effective bits/weight and total MLX runtime peak are different measurements.

Historical predictor and routing evidence

M27,2026-08-31, HumanEvalPlus 164 paired seeded tasks, native16 KV, temperature 0.4, thinking 81920: MTP ON/OFF ordinary 92.07/92.68%, delta−0.61 pp[−4.3,+3.0] (historical±5 pp equivalence); strict 85.98/86.59%, delta−0.61 pp[−6.7,+5.5] INCONCLUSIVE. Nonconverged 13/164 versus 12/164. Nominal MDE 9.8 pp; acceptance 0.778, paired mean decode 1.56×, corpus 3.28 h ON versus 4.65 h OFF. These are workload/session-specific measurements.

M34r, 2026-09-10, native routing MBPPPlus 100 paired tasks: ordinary 83% both; strict 78% ON versus 81% OFF, delta−3 pp[−8,+2], INCONCLUSIVE (MDE 12.5 pp). Nonconverged 6 versus 2; output-token ratio 1.939[0.970,4.149], generation 1.47 h versus 1.06 h. Expanded routing did not earn promotion. Native routing/MTP remain configured on the combined historical evidence, with this adverse coding-tail trend explicit; do not pool the different corpora into blanket equivalence.

M39,2026-09-12:20/20 in a 20-photo COCO description/self-check gate, predictor OFF. Three descriptions were manually checked against captions. This establishes limited visual functionality, not OCR/document/chart quality or a vision ranking. No C84 runtime screen for this model is claimed.

MTP companion

Use caslca/Ornith-1.0-35B-mlx-uniform-4bit-mtp-drafter, captured revision 0d24c3af6a1660a31848f63946309cbe234dbb6b, with draft_kind: mtp. The predictor has separate artifact provenance; it is not inferred from a target’s MTP tags.

Recommended current setting

Save this as models.yaml in an environment with the compatible fork revisions described below. The IDs name the target and, where configured, its exact companion; weight quantization and attention-KV precision are separate settings.

manager_port: 8000
mlx_port: 8091
startup_timeout_seconds: 300
models:
- name: Ornith-1.0-35B-mlx-uniform-4bit
  type: vision
  hf_path: caslca/Ornith-1.0-35B-mlx-uniform-4bit
  max_kv_cache_size: 262144
  kv_prealloc_tokens: 262144
  kv_bits: 0
  prefill_step_size: 512
  draft_kind: mtp
  draft_model: caslca/Ornith-1.0-35B-mlx-uniform-4bit-mtp-drafter
  generation_defaults:
    temperature: 0.4
    top_p: 0.95
    top_k: 20
    min_p: 0.0
    presence_penalty: 0.0
    max_tokens: 102400
    thinking_budget: 81920
    enable_thinking: true
  on_demand: true

Start from the compatible installed fork environment without additional worker overrides. This command clears cache/predictor environment overrides, disables APC and selects two retained sessions with one resident model:

env -u APC_ENABLED -u KV_BITS -u KV_QUANT_SCHEME -u KV_GROUP_SIZE \
  -u KV_KEY_BITS -u KV_VALUE_BITS -u KV_KEY_SCHEME -u KV_VALUE_SCHEME \
  -u QUANTIZED_KV_START -u MLX_VLM_SESSION_SHRINK_ON_RETIRE \
  -u MLX_VLM_DRAFT_KIND -u MLX_VLM_DRAFT_MODEL -u MLX_VLM_DRAFT_BLOCK_SIZE \
  MLX_VLM_CACHE_SESSION_MAX=2 MLX_SERVE_CONFIG=models.yaml mlx-serve start

The model loads on demand. An OpenAI-compatible client sends requests to http://localhost:8000/v1 with model ID Ornith-1.0-35B-mlx-uniform-4bit. Omitted sampling fields inherit generation_defaults; thinking is enabled. The output cap is 102400 and thinking budget 81920. Near the context limit the server resolves a smaller budget from remaining space; a configured 262144 cap does not imply 262144 input tokens plus another 102400 output tokens.

The full active KV floor equals the context cap; keep prefill 512. kv_bits: 0 means unquantized native16 attention KV, not unquantized model weights or a newly measured IEEE-FP16 dtype. TurboQuant is inactive when bits are 0 even if its scheme name remains configured. Idle retirement is explicitly true only on Qwen3.8-27B-Fable-Distill-OptiQ-4.5bpw-mixed; omitted cache_session_shrink preserves the worker default OFF only when MLX_VLM_SESSION_SHRINK_ON_RETIRE is absent, as in the command above. Suffix decoding stays OFF. These structural settings target the 64 GB evaluation machine, not arbitrary hardware.

Runtime and evidence scope

The final C84 environment uses MLX-VLM 522671c4bebc5ff492d465a1d0e6a14251f18260, MLX-Serve b632280709f771972bffbaf3231e996e8a89f4e8, and MLX/Metal 0.32.2. Those fork commits are published. Live validation of this exact final bundle covers the two current picks only; other cards retain historical model evidence and source-level compatible configuration, not a newly run model smoke. Older experiments used their recorded earlier runtime and cache mode.

Dates, original source revisions, per-axis observations and evidence-file hashes are recorded in the accompanying evaluation evidence alongside this card. The tables here are self-contained: they do not rely on unpublished stack commits or links to future GitHub main content. Speculative decoding can change BF16 outputs; favorable task-specific intervals do not imply byte-lossless inference. Historical and current measurements are not pooled across cache modes, predictor states, budgets, corpora or runtimes.

Artifact integrity audit, 2026-09-14

All 16 audited model and support files match the corresponding local artifact by full-byte cryptographic hashing, including weights and applicable configuration/tokenizer/vision files. README, license and repository-attribute files are tracked separately from inference artifacts. This publication changes the card and its dated evidence only; model artifacts remain unchanged. Per-file hashes, audited revision and scope.