Ornith-1.0-35B MLX

Community MLX conversions of deepreinforce-ai/Ornith-1.0-35B, produced and validated by PocketAI Model Lab. PocketAI did not train or fine-tune this model. This is not an official DeepReinforce release, and no endorsement is implied.

Ornith is a reasoning model for agentic coding, post-trained from Qwen3.5-35B-A3B. It is a multimodal mixture-of-experts model with 35B total parameters, approximately 3B active parameters, 256 experts, and 8 routed experts per token.

Variants

Variant	Folder	Stored size	Precision layout
MLX 4-bit	`4bit/`	20,429,175,098 bytes (19.03 GiB)	432 language/MoE modules Q4; 80 router/shared-expert gates Q8; vision tower BF16
MLX 8-bit	`8bit/`	37,748,374,364 bytes (35.16 GiB)	512 language/MoE modules Q8; vision tower BF16
MLX BF16	`bf16/`	70,241,342,643 bytes (65.42 GiB)	All 1,066 stored tensors BF16

All quantized modules use MLX affine quantization with group size 64. The 4-bit release intentionally retains 80 router and shared-expert gate modules at 8-bit precision; this is the converter's quality-preserving MoE layout. The converter reports effective stored precisions of 4.649 and 8.596 bits per weight for the 4-bit and 8-bit releases respectively.

Creative coding showcase

Each variant received the same prompt to create a colorful, single-file HTML voxel pagoda garden. The synchronized comparison below uses the same camera path for all three versions.

Open or download the MP4

MLX generation performance

Variant	Generation speed	Peak MLX memory	Output tokens	Generation time
MLX 4-bit	95.98 tok/s	21.20 GB	11,504	120.44 s
MLX 8-bit	64.96 tok/s	38.92 GB	13,128	203.84 s
MLX BF16	47.72 tok/s	70.59 GB	12,491	268.59 s

These are single-run generation measurements on a 128 GB Apple M5 Max MacBook Pro using mlx==0.32.0, mlx-vlm==0.6.8, batch size 1, thinking enabled, temperature 0.6, top-p 0.95, top-k 20, and seed 20260730. Generation speed excludes prompt prefill. Output lengths differ, so generation time should not be compared as though each variant emitted the same tokens.

The 8-bit HTML passed its original browser run. The 4-bit and BF16 generations contained small JavaScript defects; the video uses minimally repaired copies while preserving the original benchmark outputs. Exact settings, raw measurements, prompt text, and repair status are recorded in benchmarks/creative-voxel-pagoda.json.

Download and load

Install the validated runtime on an Apple Silicon Mac:

python -m pip install "mlx==0.32.0" "mlx-vlm==0.6.8"

Download only the desired variant and load its local subfolder:

from pathlib import Path

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

repo_id = "PocketAiHub/Ornith-1.0-35B-MLX"
variant = "4bit"  # "4bit", "8bit", or "bf16"

snapshot = Path(
    snapshot_download(
        repo_id,
        allow_patterns=[f"{variant}/*"],
    )
)
model, processor = load(str(snapshot / variant))

prompt = apply_chat_template(
    processor,
    model.config,
    "Write a short Python function that checks whether an integer is prime.",
    num_images=0,
    enable_thinking=True,
)
result = generate(
    model,
    processor,
    prompt,
    max_tokens=512,
    temperature=0.6,
    top_p=0.95,
    enable_thinking=True,
)
print(result.text)

Ornith is a reasoning model. With thinking enabled, output can contain a <think>...</think> block before the final response. Thinking can be disabled by passing enable_thinking=False to both prompt construction and generation.

For vision input, pass an image path to mlx_vlm.generate and build the prompt with num_images=1.

Reproducibility and validation

Official source: deepreinforce-ai/Ornith-1.0-35B
Pinned source revision: 5df2ed3f675c7beaa490328cc70bb573b65fb660
Converter: mlx-vlm==0.6.8
Base dtype: BF16
Quantization: MLX affine, group size 64
Preserved MoE layout: 256 experts, 8 routed experts per token
Deterministic text smoke: exact POCKETAI_OK for every variant
Deterministic image smoke: exact dominant color red for every variant
Peak MLX memory: 21.14 GB (4-bit), 38.76 GB (8-bit), 70.44 GB (BF16)
Full PocketAI Model Lab suite: 101/101 tests
Every uploaded variant includes an artifact-manifest.json with exact file sizes and SHA-256 hashes.

The standalone MTP draft weights from the source checkpoint are not included; these are standard MLX generation artifacts, not a speculative-decoding MTP bundle.

The checks above establish artifact integrity, strict runtime loading, basic text generation, and basic image understanding. They are not a broad benchmark or a guarantee of correctness for every prompt, context length, tool-use flow, or serving configuration.

License and attribution

The upstream model card declares the source model under the MIT license. See LICENSE and the official Ornith model card.

PocketAI Model Lab: https://github.com/PocketAIHub/pocketai-model-lab

PocketAiHub/Ornith-1.0-35B-MLX