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Ornith-1.5-35B-A3B-6bit-XL-mlx
npario/Ornith-1.5-35B-A3B-6bit-XL-mlx
Ornith-1.5-35B-A3B-6bit-XL-mlx is a image-text-to-text model from npario. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as mit.
Ornith-1.5-35B-A3B by Ornith AI — a 35B-A3B sparse MoE built through end-to-end self-improvement (jointly optimized task generation, scaffold construction, and solution rollouts via RL), quantized for Apple Silicon us…
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From the Hugging Face model README
leonsarmiento/Ornith-1.5-35B-A3B-6bit-XL-mlx
Ornith-1.5-35B-A3B by Ornith AI — a 35B-A3B sparse MoE built through end-to-end self-improvement (jointly optimized task generation, scaffold construction, and solution rollouts via RL), quantized for Apple Silicon using the BaseQuant_XL 6/8-bit recipe.
Only ~3B parameters active per token — the decode speed of a small model with the capacity of a 35B one. Ornith-1.5 is an agentic-coding specialist: 79 SWE-bench Verified, 59.6 SWE-bench Pro, 67.8/68.5 Terminal-Bench 2.1 — beating its Ornith-1.0 parent (+3.4 SWE-bench) and Qwen3.6-35B-A3B (+5.6) by wide margins.
This is a full multimodal build — the vision tower is preserved.
About XL Quantization
BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.
Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.
Quickstart
pip install -U mlx-vlm
python -m mlx_vlm.generate --model leonsarmiento/Ornith-1.5-35B-A3B-6bit-XL-mlx --max-tokens 256 --temperature 0.6 --top-p 0.95 --prompt "Implement an LRU cache in Python with O(1) get/put."
Works with LM Studio — vision mmproj included. Thinking mode is on by default (emits <think>...</think>).
Quantization Strategy
BaseQuant_XL recipe — precision is allocated by layer importance, not applied uniformly:
| Layers | Bits | Rationale |
|---|---|---|
mlp.gate (router), shared_expert_gate, lm_head, shared_expert | bf16 | Routing decisions and output projection — any quantization noise here causes expert misrouting or output degradation |
embed_tokens, self_attn, linear_attn | 8-bit | Every-token layers — near-lossless, attention quality preserved |
vision_tower, switch_mlp (routed experts) | 6-bit | Bulk parameters — 256 experts with only 8 active per token; redundancy absorbs quantization noise. 6-bit is the sweet spot for routed experts (higher bits can cause overthinking) |
- Bits per weight: ~6.8 · Total size: ~28 GB · Group size: 64
Notes specific to this build:
- Source ships an MTP (multi-token prediction) layer — dropped in this build (engines that execute bundled MTP are the exception, not the rule; standard XL builds omit it).
- Unlike Ornith-1.0 (which stores experts individually), Ornith-1.5's source stores merged per-layer expert tensors — converts via stock
mlx_vlmsanitize.
Recommended Inference Parameters
Per the source model card — new settings for the Ornith-1.5 generation:
| Parameter | Value |
|---|---|
temperature | 0.6 (general tasks) · 1.0 (reproducing reported benchmarks) |
top_p | 0.95 |
top_k | 20 |
presence_penalty | 1.1 |
reasoning_parser | qwen3 (changed from deepseek_r1 in Ornith-1.0) |
tool_call_parser | qwen3_xml |
Thinking is on by default (<think>...</think> before the answer); with a reasoning parser enabled the chain-of-thought is returned in a separate reasoning_content field.
Model Overview
| Property | Value |
|---|---|
| Architecture | Qwen3.5-family MoE (35B-A3B) + native vision encoder |
| Parameters | 35.9B total / ~3B active per token |
| Experts | 256 (8 routed + 1 shared) |
| Attention | Hybrid — 30 linear_attn + 10 full attention (40 layers) |
| Modalities | text, image, video → text |
| Context window | 262,144 tokens native |
| Thinking | <think>...</think> |
| Tool calling | XML-style (<tool_call><function=...><parameter=...>) |
| License | MIT |
Source Model Benchmarks (from Ornith AI)
| Benchmark | Ornith-1.5-35B-A3B | Ornith-1.0-35B-A3B | Qwen3.6-35B-A3B |
|---|---|---|---|
| SWE-bench Verified | 79.0 | 75.6 | 73.4 |
| SWE-bench Pro | 59.6 | 50.4 | 49.5 |
| Terminal-Bench 2.1 (Terminus-2) | 67.8 | 64.2 | 52.5 |
| Terminal-Bench 2.1 (Claude Code) | 68.5 | 62.8 | 49.2 |
Source
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- qwen3_5_moe
- License
- mit
- Created
- Aug 21, 2026
- Updated
- Aug 21, 2026