Ornith-1.0-35B-MLX-MXFP4

Ornith-1.0-35B-MLX-MXFP4

Summary

MLX MXFP4 (mxfp4, 4-bit, group size 32) quantization of deepreinforce-ai/Ornith-1.0-35B at upstream revision 5df2ed3f675c7beaa490328cc70bb573b65fb660, converted from the BF16 safetensors with mlx_vlm convert (mlx-vlm 0.6.3) after a BF16 expert-fusing pass (pipelines.moe_expert_fuse, majek repo — upstream ships unfused per-expert MoE tensors). ~18.0 GiB on disk.

Runtime status (verified 2026-07-04): loads and generates under mlx-vlm 0.6.3 (vision probe passed on this exact payload) and under mlx-lm 0.31.3 for text-only use (text probe passed). Republished 2026-07-04 in the mlx-vlm module layout, replacing the 2026-07-02 pack, which no MLX runtime could load. Output quality beyond the smoke probes is not verified.

Runtime status

Smoke-gated on Apple Silicon before publish (2026-07-04): this exact payload was loaded and probed per variant (pipelines/vlm_pack_smoke.py / pipelines/lm_pack_smoke.py, majek repo); verdicts in .sisyphus/evidence/lane-a-republish/ and in PROVENANCE.md.

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("majentik/Ornith-1.0-35B-MLX-MXFP4")
prompt = apply_chat_template(
    processor, model.config, "Describe this image.", num_images=1
)
print(generate(model, processor, prompt, image=["image.jpg"],
               max_tokens=128).text)

Text-only use:

from mlx_lm import load, generate

model, tokenizer = load("majentik/Ornith-1.0-35B-MLX-MXFP4")
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain KV caching in one sentence."}],
    add_generation_prompt=True, tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128))

Why this variant

MLX-native MXFP4 — E2M1 weights with per-32-element E8M0 scales (OCP microscaling), group size 32. Pick this for format parity with MXFP4 hardware pipelines.

Reproduce

# base = snapshot of deepreinforce-ai/Ornith-1.0-35B @ 5df2ed3f675c7beaa490328cc70bb573b65fb660
python -m pipelines.moe_expert_fuse --src /tmp/lane-a-republish/ornith-1.0-35b/base --dst /tmp/lane-a-republish/ornith-1.0-35b/fused --report-out /tmp/lane-a-republish/ornith-1.0-35b/fuse-report.json
python -m mlx_vlm convert --hf-path /tmp/lane-a-republish/ornith-1.0-35b/fused --mlx-path /tmp/lane-a-republish/ornith-1.0-35b/MXFP4 -q --q-bits 4 --q-group-size 32 --q-mode mxfp4

Vision tower

The vision tower (333 upstream tensors) rides through in BF16 — mlx_vlm convert quantizes only the language tower by default. This pack stores weights in the mlx-vlm module layout (vision_tower.*, language_model.*) rather than the upstream model.visual.* naming.

Family

All MLX variants of this model ship together:

• majentik/Ornith-1.0-35B-MLX-2bit
• majentik/Ornith-1.0-35B-MLX-3bit
• majentik/Ornith-1.0-35B-MLX-4bit
• majentik/Ornith-1.0-35B-MLX-5bit
• majentik/Ornith-1.0-35B-MLX-6bit
• majentik/Ornith-1.0-35B-MLX-8bit
• majentik/Ornith-1.0-35B-MLX-MXFP4 (this repo)
• majentik/Ornith-1.0-35B-MLX-NVFP4

Provenance

• Upstream: deepreinforce-ai/Ornith-1.0-35B @ 5df2ed3f675c7beaa490328cc70bb573b65fb660
• Quantization: bits=4, mode=mxfp4, group_size=32 (language tower only)
• Toolchain: mlx 0.31.2, mlx-lm 0.31.3, mlx-vlm 0.6.3, transformers 5.12.0, huggingface_hub 1.22.0
• Republished 2026-07-04 in a runtime-loadable layout; supersedes the 2026-07-02 pipelines.mlx_direct_quantize pack. Full details in PROVENANCE.md in this repo.

License + attribution

Quantized by majentik from deepreinforce-ai/Ornith-1.0-35B. All rights in the original model remain with its authors.

Upstream declares the MIT license via its model-card metadata but ships no LICENSE file, so the full MIT license text is reproduced below with attribution to deepreinforce-ai:

MIT License

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